Accepted Manuscript Resource management in cellular base stations powered by renewable energy sources Faran Ahmed, Muhammad Naeem, Waleed Ejaz, Muhammad Iqbal, Alagan Anpalagan PII: S1084-8045(18)30108-5 DOI: 10.1016/j.jnca.2018.03.021 Reference: YJNCA 2102 To appear in: Journal of Network and Computer Applications Received Date: 24 November 2017 Revised Date: 13 February 2018 Accepted Date: 19 March 2018 Please cite this article as: Ahmed, F., Naeem, M., Ejaz, W., Iqbal, M., Anpalagan, A., Resource management in cellular base stations powered by renewable energy sources, Journal of Network and Computer Applications (2018), doi: 10.1016/j.jnca.2018.03.021. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT 1 Resource Management in Cellular Base Stations Powered by Renewable Energy Sources EP TE D SC M AN U Abstract—This paper aims to consolidate the work carried out in making base station (BS) green and energy efficient by integrating renewable energy sources (RES). Clean and green technologies are mandatory for reduction of carbon footprint in future cellular networks. RES, especially solar and wind, are emerging as a viable alternate to fossil fuel based energy, which is the main cause of climate pollution. With advances in technologies, renewable energy is making inroads into all sectors including information and communication technologies (ICT). The main contributors of energy consumption in ICT sector are ’data centers’ and ’cellular networks’. In cellular networks the BS is the main consumer of energy, mostly powered by the utility and a diesel generator. This energy comes at a significant operating cost as well as the environmental cost in terms of harmful greenhouse gas (GHG) emissions. Recent research shows that powering BSs with renewable energy is technically feasible. Although installation cost of energy from non-renewable fuel is still lower than RES, optimized use of the two sources can yield the best results. This paper presents a comprehensive overview of resource management in cellular BSs powered by RES and an in-depth analysis of power consumption optimization in order to reduce both cost and GHGs. Renewable energy sources are not only feasible for a stand-alone or off-grid BSs, but also feasible for on-grid BSs. This paper covers different aspects of optimization in cellular networks to provide reader with a holistic view of concepts, directions, and advancements in renewable energy based systems incorporated in cellular communications. Energy management strategies are studied in the realm of smart grids and other technologies, increasing the possibilities for energy efficiency further by employing schemes such as ’energy cooperation’. Finally, the paper supports the move towards green communication in order to contribute positively towards climate change. RI PT Faran Ahmed, Muhammad Naeem, Waleed Ejaz, Muhammad Iqbal, and Alagan Anpalagan AC C Index Terms—Cellular base station, energy optimization, green communication, renewable energy. I. I NTRODUCTION Over the past decade concepts such as renewable energy, energy conservation, and energy efficiency have found their way into all technology sectors including the information and communication technology (ICT) sector. The reason for this is twofold; firstly, the rising operating cost of power consumption for the energy intensive systems is being felt all over as technology encompasses every facet of our lives. Secondly, the ICT industry, being the fastest growing sector, realizes its obligation in reducing harmful CO2 emissions F. Ahmed, M. Naeem, and M. Iqbal are with the COMSATS Institute of Information Technology-Wah, Wah Cantonment 47040, Pakistan (e-mail: naraf@yahoo.com; muhammadnaeem@gmail.com; driqbal@ciitwah.edu.pk). M. Naeem is the corresponding author. W. Ejaz and A. Anpalagan are with the Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada (email: waleed.ejaz@ieee.org; alagan@ee.ryerson.ca). Fig. 1. Power consumption of cellular elements of mobile communication, with BS consuming upto 60% of network energy. attributed towards it. Amongst all sub-sectors of ICT, the telecomm sector in general and cellular communication in particular have shown huge potential for improvements in energy efficiency and converting systems on clean (renewable) sources of energy. As a result future communication is not only focused on spectrum management and throughput or quality of service (QoS) anymore. Rather, a new paradigm has come in, i.e., energy efficiency with reduced carbon footprint, called green communication [1]. Green communication has become a realistic goal for which new ways and means are being explored by the industry [2]–[6]. Cellular communication is the fastest growing component of telecom sector in particular and ICT in general [7], [8]. It is envisaged that the global BS power consumption will grow from 49 TWh in 2007 to 98 TWh by 2020 [9]. Improving energy efficiency in cellular networks involves energy reduction of all network elements, such as mobile core network, mobile switching centers, BSs, mobile back haul networks, and mobile terminals [10]–[19]. Amongst the mentioned elements, the BS is the most energy hungry component, consuming approximately 60% of the total energy consumed by cellular network [20], as depicted in Fig. 1. For the BS of a 3G and LTE network this ratio increases to 75-80% [2]. Thus, BSs have become the prime focus of research for energy efficiency in cellular communication; especially for installation of RES such as PV arrays and wind turbines. Green wireless communication can be described as a set of concepts and frameworks put together to improve the energy ACCEPTED MANUSCRIPT BB BS CAPEX DG ESS HetNet ICT LTE NOW OFDMA OPEX PA PMSG PV QoS RAN RES RF RRH SG SINR SOC TWh Base Band Base Station Capital Expenditure Diesel Generator Energy Storage System Heterogeneous Network Information and Communication Technology Long Term Evolution Network Operator Orthogonal Freq Division Multiple Access Operational Expenditure Power Amplifier Permenant Magnet Synchronous Generator Photo Voltaic Quality of Service Radio Access Network Renewable Energy Source Radio Frequency Remote Radio Head Smart Grid Signal to Interference and Noise Ratio State of Charge Trillion Watt hour RI PT Description • Energy efficiency metrics and consumption models: Green spectrum management for mobile operators [35], is an area that deals with the quantification of energy consumption and formulation of energy models similar to real time scenarios. The quantification of energy is not only done at system level but also over life span of technology to come up with accurate metrics for energy measurement [36]. Energy efficient hardware and technologies: Another area of interest is the hardware that can be made more energy efficient by improving design and technology, e.g., the power amplifier (PA) is a big candidate for improvement in energy efficiency. It also includes software improvements such as cross-layer and energy storage optimization. Energy efficient architectures: Energy efficiency in wireless networks can also be achieved through different network architectures, such as cost effective deployment strategies of heterogeneous networks (HetNets) [37], multi-cell cooperation, cell zooming or using low-power micro base stations compared to today’s high-power macro BS schemes etc. [38], [39]. Power consumption can be reduced using multi-hop transmission in cellular networks [40] or self-organized energy efficient cellular networks [41]. Energy efficient resource management: Management of both radio and energy resources is vast topic of research from the point of energy efficiency [42]. Radio resource management involves efficient spectrum management and user traffic management. For example, authors in [43] have demonstrated that it is possible to save energy by optimizing the sleep cycle of a BS. Sum-rate maximization and cost minimization are similar objectives [23]. Energy resource management involve schemes such as energy cooperation and optimization of different energy sources [44]. Multi-radio access network technologies (Multi-RAT) management and novel paradigms for delay tolerant services are also some resource management techniques. Authors in [45] present a trade-off between energy and spectral efficiency in downlink orthogonal frequency-division multiple access (OFDMA) networks. Incorporation of renewable energy sources (RESs): M AN U Acronyms energy efficiency in wireless communications [32]. In green cellular networks, the main objective is to maximize the use of renewable energy, for which research has focused on energy consumption strategies, resource management strategies and performance analysis of demonstration systems [33]. In modeling a cellular network supported by RES, the objective is to determine the most advantageous network characteristics such as density of BSs, topology of BSs/RESs, sleep algorithms, BS interconnection, multi-cell cooperation etc. Powering the BSs with RES systems of manageable size is a challenging task especially when it aims to minimize the overall network energy cost without compromising the user QoS [34]. The research on energy efficiency in cellular communication has been carried out from different perspectives, which can be broadly categorized into five categories: SC TABLE I D ESCRIPTION OF ACRONYMS USED IN THIS PAPER . • • AC C EP TE D efficiency of wireless systems. The use of RESs is gaining widespread coverage in all sectors due to the improvements in the photo-voltaic (PV) cells and wind-turbine (WT) related technologies, deep cycle rechargeable batteries, power converters etc. as well as simulation and maintenance softwares [21]. The hybrid systems comprising conventional and RESs have been shown to significantly decrease the overall cost of the isolated power systems over their total life cycle [22]. In cellular applications, the main attraction is to power remotely located BSs that are off the grid, thereby saving substantial cost of running the diesel generator and fuel transportation cost. The use of sustainable renewable (green) energy can also help in cutting down the harmful CO2 emissions. In fact, research shows that green BSs are equally beneficial in energy cost savings and maximization of energy efficiency in networks that are connected to the grid or off the grid [23]. Renewable energy provides an opportunity to bridge the energy gap for powering systems such as cellular BSs, for both developing and under developed countries [24], [25], as well as cut down on harmful GHG emissions. A list consisting of description of acronyms used in this paper is presented in Table I. • A. Related Work The topic of energy efficiency in cellular networks is very vast given the large number of perspectives available for research. Not only academia but industry as well as government and non-government organizations are exploring the realm of 2 • ACCEPTED MANUSCRIPT Reference RESenabled BS 1 2 3 4 5 6 7 8 9 10 11 12 [1] [2] [3] [4] [5] [26] [27] [28] [29] [30] [31] Our Work X X Singular Config. Cellular Config. App. Specific X X X X X X X X X X X X X X X X X X X X X X X Generic Off Grid Smart Grid X X X X X X X X X X X X X X X X X X X X M AN U X TABLE II E NERGY E FFICIENCY IN G REEN C ELLULAR N ETWORKS RI PT Sr. No. ON SC C OMPARISON WITH R ELATED W ORK An upcoming paradigm for energy efficiency is the incorporation of RESs such as solar and wind, particularly on the BSs. In [46], authors have proposed a scheme to optimize the utilization of green (solar) energy during the peak traffic hours, i.e., day time, when solar energy is available. EP TE D This reveals that there are many ways of achieving energy efficiency in a BS including improving efficiency of the hardware, improving the network protocols, improving the system architecture and network deployment tailored to traffic requirements, and using low-power micro BSs compared to today’s high-power macro BS schemes [38]. However, this paper deals with work mainly related to renewable energy based designs and strategies because of the increased interest of research community in energy efficiency and reduction in CO2 emissions. Theoretical modeling as well as software simulations carried out by the researchers in verifying the trade-offs between energy efficiency of RES enabled BSs and other performance parameters [47]. An overview of green mobile network is presented in [48], where incorporation of renewable energy at the BS site or at the grid end, and its implication has been discussed, with a brief description of the energy efficiency schemes employed in such scenarios. A survey on green mobile networking from the perspectives of network operators and mobile users is presented in [28], with discussion on power consumption models and energy efficiency techniques including the use of renewable energy. The survey of methods of achieving reduction in energy consumption at cellular BS by using renewable energy sources has been discussed quite elaborately in [5]. From futuristic point of view, they have described cognitive radio networks and cooperative techniques for achieving energy efficiency in cellular networks. Authors also described the metrics used to measure energy efficiency in wireless networks. However, the configuration and other dynamics of AC C 3 X X X X Energy Mgmt Strategies X X X X X X X X X an RES enabled BS have not been described. Serrano et al. [29], work has reviewed different wireless technologies (such as WLAN, WMAN and PAN, etc.) to highlight main reasons of energy inefficiency and the research done to overcome these so far. Their specific contribution is in terms of the quantitative analysis of the savings made in the proposed solutions. Green cellular networks are not the area of research in their work. Authors in [49], have discussed energy efficient transmission techniques, mainly focusing on relaying techniques, multi hop techniques and multiple input multiple output (MIMO) techniques. Also, cooperative transmission techniques and energy efficient signaling information has been discussed from future research point of view. Use of renewable energy for energy conservation is not the prime focus area. Researchers in [4], while reviewing energy efficiency in cellular networks have mainly investigated radio resource management strategies that reduces energy consumption of networks. The relationship between energy efficiency metrics and their trade offs is also discussed. Particularly, authors have analyzed the role of HetNets deployments and cooperative communication mechanisms in improving the energy efficiency of networks. However, the role of grid in energy efficiency of BSs as explored by researchers, has not been covered in this work. The survey in [1] explores the RES enabled BS similar to what we have done. It starts by characterizing salient features of a RES enabled BS such as type of BS, energy storage, and power control unit. Then it discusses different techniques in cellular architecture such as HetNets. Finally the authors describe some algorithms dedicated for energy management in RES enabled BS. However, the paper lacks the depth in exploring all three said areas and the role of smart grid (a promising technology) is not discussed in connection with green BSs. In [2], authors have briefly described the objectives and constraints of hybrid energy based cellular systems and then presented a case study in which they propose an energy management algorithm to ACCEPTED MANUSCRIPT 4 RESOURCE OPTIMIZATION IN GREEN BS/ NETWORK Green Base Station Wind Turbine Energy Resource Network Optimization Radio Resource BS Sleep Mechanism Energy Cooperation Grid Optimization Smart Grid Conventional Grid M AN U Battery Bank Fig. 2. Multi-cell Cooperation RI PT PV Panels Ressource Optimization SC System Optimization Green Network Classification of optimization focus in Renewable Energy enabled Base Stations and Networks. AC C EP TE D reduce the electricity consumption cost by employing PV array as a RES. Simulation results verify the advantage of RES in cutting down electricity consumption cost, as demonstrated by other research work presented in this paper. Another note worthy survey on energy efficiency in green cellular networks has been carried out in [27]. Once again the green paradigm has been used to describe all energy efficient techniques, however, renewable energy assisted cellular BSs is not the main focus of this paper. The paper discusses energy consumption and cost models for macro/micro BSs, back haul and RAN controller followed by the classification of various energy efficiency metrics and trade offs w.r.t coverage, spectral efficiency, and end user performance. Finally the paper classifies energy efficiency schemes on the basis of their operations in time scale, i.e., short, medium and long time frame mechanism. [3]. Most of the existing surveys cover energy efficiency in wireless systems in general and not specifically in cellular communication networks. Others that do focus on cellular networks use the green paradigm to describe all types of energy efficiency mechanisms including renewable energy incorporation. Thus their treatment of the topic is broad and not focused on renewable energy alone. Few that do focus on renewable energy enabled cellular BSs and networks have not covered all aspects in depth as we have, as depicted in Table II. Moreover, our survey is optimization oriented and almost all aspects of optimization of energy efficiency in green BS have been discussed. Most importantly, the dynamics of an RES enabled BS, including sizing and configuration, and the upcoming energy management strategies have been discussed with their problem formulations to give a deeper insight to the reader. B. Classification of research into RES enabled BSs and Networks The research into renewable energy enabled BSs has been categorized into two distinct areas as depicted in Fig. 2, i.e., Green Base Stations and Green Cellular Networks. Fig. 2 also shows the classification of these BSs as per power grid connectivity. A BS in urban and populated areas is mostly connected to the grid, i.e., on-grid, whereas, those deployed in remote or inhabitable areas are off the grid. The distinct research areas pertaining to single BS includes optimization of RES system configuration, energy storage, energy sources, and radio resource optimization. Optimal utilization of battery/storage-device is particularly required in a stand alone BS. The second category of research work relates to the green cellular networks that have RES-enabled BSs. In the case of cellular configuration the dynamics are different as the BSs can manipulate the cellular energy consumption in conjunction to each other through strategies such as multi-cell cooperation, sleep mechanism, energy cooperation, etc. In this case, introduction of smart grid provides some viable energy management options such as energy cooperation. In most research articles, theoretical modeling of relevant parameters of a BS is often carried out prior to subjecting them to different optimization techniques. These models can be categorized as following:• Configuration model describes the capacity and size of the PV array panels and the wind turbines. The configuration of the renewable systems depends on the availability of other resources like utility and DG, but mostly the wind and solar irradiation of the site. Thus BSs have mostly been configured for either PV panels or ACCEPTED MANUSCRIPT made from advance materials. Charge Controllers involve ACDC and DC-AC convertors, voltage/current regulators, active and passive semiconductor devices. Energy management units are discussed in some research, which can be thought of as intelligent charge controllers with embedded micro-controllers running algorithms. Different types of batteries and fuel cells (storage elements) are under study for storage of harvested energy. RI PT The third generation partnership project (3GPP) standards body is laying emphasis on energy efficiency in the LTE technology [20]. RES based architectures and protocols for green 4G/5G communications are proposed in [55]. Technological improvements in component design and better algorithms in conjunction with efficient deployment and management strategies will bring about the desired results. The energy efficiency of cellular BS is rather poor at off-peak hours, when traffic load is minimal [56]. It must be improved by technological improvements at the component, link, and network level. At lower output power, i.e., in off-peak hours, the efficiency of power amplifier degrades substantially, which can be improved with efficient amplifiers. In current cellular systems certain signaling such as synchronization and pilot signals need to be regularly transmitted, forcing the BS to consume energy continuously that requires link level improvements. Similarly, network level improvements are needed in today’s networks, which are deployed for full capacity handling and are not dynamic in topology so as to adjust in off-peak hours or hybrid power systems. TE D M AN U SC wind turbines or hybrid of the two [50]. Energy storage model is defined in terms of battery parameters such as capacity (AH), battery charging losses, charging rate, the system load, etc. In addition, RESs often taken as a stationary stochastic process, where the objective is to maximize green energy utilization at each stage, while meeting the network’s QoS requirements. • Energy consumption model is comprised of two components, i.e., (1) a static component related to equipment having constant power consumption and (2) a dynamic component related to energy consumption in power amplifier, attributed to traffic load. Estimation of present energy consumption and future energy arrival as well as balancing various energy sources (solar, wind, grid etc) is fundamental optimization problem in energy consumption models. • Traffic/channel model describes user traffic as a function of data arrival rate of the user(s) at the BS and the data service rate provided by the BS. Channel model is described by channel’s signal to interference and noise ratio (SINR) and is defined as a function of transmission power, channel gain, and channel’s noise. The rest of the paper is organized as follows. Section II gives an overview of the RES enabled network and BS in terms of its energy dynamics, renewable energy systems modeling and optimization. Section III discusses energy optimization strategies for a green (RES enabled) BS, while Section IV discusses energy conservation schemes for green cellular networks, with a summary at the end of both parts. Conclusions and future research directions are drawn in Section V. • 5 II. A N OVERVIEW OF G REEN C ELLULAR N ETWORK /BS AC C EP The emerging paradigm of RES enabled cellular networks in smart grid involves different technologies that makes green communication possible. The advances being made in technologies related to renewable energy systems, LTE-A (5G) networks and smart grids make it possible to design and implement green communication networks as demonstrated by research conducted in green LTE networks [2], [34], [43], [51]–[53]. An overview of technology perspective of RES enabled cellular networks in smart grid is shown in Fig. 3. Integration of RES at system level has been discussed in this paper. The hybrid of solar and wind system promises most yield in energy harvesting as demonstrated in [54]. The type of RES suited to a site depends mainly on its geographical conditions, grid-power availability, and sustainable resource (solar, wind) availability. More efficient and cheaper photovoltaic cells (poly-crystalline and mono-crystalline) are becoming available, which is reducing the cost of solar technology day by day and their use is getting widespread. Both vertical and horizontal axis wind turbines are being employed; each having their own pros and cons. Robust and efficient permanent magnet generators with high voltage yield at low r.p.m. have a gear assembly for large capacity wind turbines. The wind cutting blades have dynamics similar to aerofoils and According to US department of Energy, smart grid refers to a class of technologies that are making utility electricity delivery systems smarter by integrating ICTs for energy measurement, control, and automation. These systems are made possible by the two way digital communication technologies and computer processing and are embedded at all levels, i.e., generation, transmission, distribution and consumption. The smart grid brings to the grid features that were not available in the conventional grid. Features that make the grid smart, such as measurability, controllability, security, maintainability, scalability, and optimization. US department of Energy lists five fundamental technologies that will drive the smart grid: • • • • • Integrated communications, connecting components to open architecture for real-time information and control, allowing every part of the grid to both talk and listen. Sensing and measurement technologies to support faster and more accurate response such as remote monitoring, time-of-use pricing and demand-side management. Advanced components to apply the latest research in superconductivity, storage, power electronics and diagnostics. Advanced control methods to monitor essential components enabling rapid diagnosis and precise solutions appropriate to any event. Improved interfaces and decision support to amplify human decision-making, and transforming grid operators and managers. ACCEPTED MANUSCRIPT 6 TECHNOLOGIES RELATED TO THE NEW PARADIGM OF RES ENABLED CELLULAR NETWORKS IN SMART GRID 3 4 5 Renewable Energy Systems Photovoltaic Cells Wind Turbines Batteries Fuel Cells Charge Controllers Inverters Convertors Protecters LTE Networks OFDMA SC-FDMA Spectrum Flexibility Power Control Inter Cell Interference Coordination SMART Grid Integrated Communication Sensing and Measurement Advanced Components Advance Control Methods Improved Interfaces Technology perspective of RES enabled cellular networks in smart grid. M AN U A. Energy Consumption in Cellular Base Stations RI PT 2 SC Fig. 3. 1 TE EP AC C Base Station Electronics BB RF PA D A typical base station consists of different sub-systems which can consume energy as shown in Fig. 4. These subsystems include baseband (BB) processors, transceiver (TRX) (comprising power amplifier (PA), RF transmitter and receiver), feeder cable and antennas, and air conditioner [57]. This energy consumption ranges from a few watts to kilo watts depending on the type of BS, with a macro BS consuming most energy, as depicted in Table III. Power consumption of different modules for some known BS types is given in the Table III, i.e., power consumption of radio equipment comprising power amplifier (PA), BB, and RF transmitter/receiver modules, and the auxiliary equipment comprising main supply, voltage converters, and cooling units [58]. Whereas, the power consumption of the PA, RF and BB modules is fixed for a particular type of BS, the consumption of power supplies and air conditioning is shown to be a factor of the radio equipment’s consumption. It is evident from Table III that in case of a macro BS the PA alone accounts for 57% of total energy consumption, which is reduced to 51% for remote radio head (RRH) configuration. Thus, significant power dissipation takes place in the PA, which is directly associated with the traffic load at the BS. From the above discussion we may deduce that the energy consumption by a BS can be bifurcated into static and dynamic types. The former accounts for basic consumption, independent of traffic load, and is attributed to the cooling equipment, power supplies, and RF/BB modules. While the latter is proportional to the traffic load or data throughput of a BS and is attributed to the energy consumption in PAs. Thus the total energy being consumed is the sum of the two types of consumptions noted here. The traffic variations, thus allow us to measure the variation in energy consumption of a BS. The power consumption of a BS b serving a system traffic load of density ρ in time t, may be given as Pb = ρPtb , where Ptb is the power transmitted by the BS in time slot t. Bifurcating this power into the above stated static and dynamic components, which is expressed as: Power Supplies Air Interface Air Conditioning Fig. 4. Typical layout of power consuming modules of cellular BS [60]. Pb = △p Ptb + Pf , (1) where Pf is the fixed energy consumption component and △p is the slope of the load dependent power variable [59]. The energy efficiency of a cellular BS can be broadly defined as the ratio of BS’s output power (transmitted power) to the total input power (consumption) [59]: ηn = Pt /Pn ; = Tn /Pn ; = Cn /Pn , (2) where the first term (Pt /Pn ) is the ratio of the transmitted power to the network power, whereas in the second term, which is a variant of first, Tn stands for power required for certain area coverage and has unit of Watt−1 . The third term (Cn /Pn ) is a further modification and accounts for the aggregate network capacity to the total power consumed, ACCEPTED MANUSCRIPT 7 TABLE III P OWER C ONSUMPTION B REAKDOWN FOR D IFFERENT T YPES OF BASE S TATIONS [58]. NT RX PA [W] RF [W] BB [W] DC-DC [%] Cooling [%] Mains [%] Total per Trx Total for NT RX Macro RRH Micro Pico Femto 6 6 2 2 2 128.2 64.4 6.3 0.13 1.1 12.9 12.9 54 6.8 0.6 29.6 29.6 2.6 4.0 2.5 7.5 7.5 39.0 4.3 9.0 10.0 0.0 0.0 0.0 0.0 9.0 9.0 9.0 11.0 11.0 225.0 W 125.8 W 72.3 W 7.3 W 5.2 W 1350 W 754.8 W 144.6 W 14.7 W 10.4 W B. Design and Optimization of Hybrid Power System AC C EP TE D M AN U Research has shown that the hybrid energy systems can significantly reduce total life cycle cost of stand alone power supplies in many situations [21]. Similarly in the case for cellular BSs the hybrid of both wind/solar and conventional energy give the most optimal energy efficient solutions; as investigated by researchers in [8], [53], [61]–[64]. The fundamental steps involved in equipping a BS with renewable energy sources can be described as following: • Feasibility study involves gathering site parameters, particularly average annual solar insolation and wind speed as well as possibility of installation of systems. Meteorological data generation is required for any feasibility study. • System Modeling/Sizing of PV arrays, wind turbines, and battery bank is the main task, which must be carried out very carefully. Different sizing methods can be used for solar panels and wind turbines, which must ultimately search for an optimum combination of two parameters, i.e., the system reliability and the system cost [65]. For a BS, reliability is of paramount importance especially for an off grid BS powered by RES only, therefore, the system must be designed carefully with sufficient battery backup. Sizing of PV-wind hybrid systems may be performed by different methods such as: the monthly average method, the most unfavorable month method, the loss of power supply probability (LPSP), and through software simulations. LPSP is the probability that an insufficient power supply results when the hybrid system (PV, wind power, and energy storage) is not able to satisfy the load demand. Some other power reliability criteria also exist, such as loss of load probability (LOLP), system performance level (SPL), and loss of load hours (LOLH) [65]. • System evaluation is carried out using simulation software, which can perform power reliability analysis and system cost analysis for renewable energy systems. Several software tools are available for designing of renewable energy hybrid systems, such as hybrid optimization model for electric renewables (HOMER), HYBRID2, HYBRIDS, etc. HOMER is a public domain software produced by National Renewable Energy Laboratory, uses hourly simulations for arriving at optimum target and is popular among researchers. • System Optimization aims at 1) optimization of system capacity of RES, i.e., size of PV array and the wind turbine as well as the battery bank and 2) optimization of energy consumption within system in order to maximize the use of green harvested energy. Different optimization techniques are employed to achieve the stated goals [7]. These can broadly be categorized as follows. – Deterministic approach – Probabilistic approach – Iterative technique – Artificial intelligence methods – Multi-Objective design A detailed look at these and other characteristics for optimum configuration of hybrid stand-alone solar/wind power systems for any application is presented in [21] and [65]. SC having units of bits per second per watt [59]. RI PT BS Type C. Subsystems of Solar/Wind enabled BS A hybrid solar/wind based power system comprises PV array, wind turbine, battery bank, controller, inverter, cabling, and other devices (such as fuses etc.). The layout of a BS employing conventional as well as renewable energy sources is shown in Fig. 5. In a green BS, the energy harvested from nature is mostly either from sun or wind or the hybrid of the two, along with a battery bank to store and regulate the harvested energy. Due to the very high reliability required for a communication system, a standby diesel generator (DG) is also provided to cater for disruptions even in countries with stable utility supply. The heart of the system is a hybrid controller that controls the flow of energy between system components and also charging/discharging of the battery bank. Batteries are indispensable for the success of RES because the energy harvested from sun and wind can be unstable and unpredictable. Consequently, batteries are invariably used to store energy for off-times as well as to regulate the supply of energy to the system. A brief description of the fundamental parts of BS powered by hybrid energy source is given below. • Wind Turbine: A wind turbine is described as comprising two main components, i.e., a rotor assembly of blades and coupling, and a permanent magnet (AC/DC) generator with associated electronics such as AC/DC converter [66]. Vertical-axis or horizontal-axis wind turbines may be employed each having its own merits/demerits. The ACCEPTED MANUSCRIPT 8 modeled, for both integrated or separate installation. III. R ESOURCE M ANAGEMENT IN G REEN C ELLULAR BASE S TATION AC LOAD Fig. 5. Basic layout of a typical BS powered by main grid, a back-up generator, and renewable energy sources of solar and wind. • • • M AN U AC C EP • TE D • electric power generated from a wind generator is given as Pw = 12 × ρ × Ce × Aw × V 3 × 10−3 , where ρ is the air density, Ce is the coefficient of wind turbines performance, Aw is the area traversed by wind and V is the wind speed. It is obvious that wind speed V is the variable on which the output of a given turbine depends. Photo-voltaic System: The PV systems include solar cell module, mounting hardware, DC charge controller, and installation. The electric power generated from a PV array is given as Pp = Ap ×ηm ×Pt ×ηP C ×I, where Ap is the total area of PV array, ηm is the module efficiency (0.15), Pt is the packing factor, ηP C is power conditioning efficiency, and I is the hourly irradiance (kW h/m2 ). Given the low efficiency of PV cells (approximately 15%) large panel sizes are required to completely serve a BS. Energy Management Unit: This module is used to control flow of energy that can be as simple as a charge controller or something quite complex that maintains causal knowledge of wind and solar availability. Several management strategies are implemented and algorithms are developed for this purpose and stored in embedded systems [67]. Battery Bank: The harvested energy is stored in battery bank and is used to power the systems in a regulated manner. The sizing of the battery bank is based on the BS load. Many researchers have focused on optimizing battery charging/ discharging operations [68]–[70]. AC/DC Bus Bar: For connectivity of AC and DC power between different components of the system such as battery bank, air conditioner, radio devices, charge controller etc. Two bus bars are required for the respective loads. An inverter is also used to convert the DC power from the battery bank to AC for air conditioning. Mechanical Assembly: A housing and support structure is required to install the wind turbine and the PV panels. The design needs to have minimum maintenance requirements since BS could be located at remote locations. Both horizontal axis and vertical axis wind turbines have been RI PT INVERTER SC DC LOAD HYBRID CONTROLLER In this section, we explore the research carried out in an RES enabled BS from different perspectives. Incorporation of RESs into a cellular BS is viable not only from climate change point of view but also from energy efficiency perspective [71]. In developing countries, rural population is often outside grid connectivity. In such scenarios, wind and solar energy can effectively supplement the DG and can be a useful alternative to fuel based power. Optimization of an RES enabled BS is performed primarily with two main objectives, as outlined in the previous section, i.e., optimization of the system elements and subsystems, and optimization of overall energy consumption of the BS. The former deals with optimal sizing of PVarray / wind-turbines as well as optimal storage of energy, whereas the latter deals with the optimization of energy and radio resources. The incorporation of renewable energy is equally viable, whether a BS is a stand alone (off-grid) or supported by utility (on-grid). Thus, researchers have addressed resource optimization in both on-grid and off-grid BSs as depicted in Table IV. The dynamics of the off-grid and on-grid are somewhat different, however, the short term and long term benefits can be derived for both types. The present infrastructure of a BS is not particularly suited for installing wind turbines and PV panels, however, practical solutions have been achieved. For example, a wind-powered tower for a macro BS has been deployed by Ericsson [66]. Similarly, Nokia Siemens Networks has developed a hybrid (solar and wind) green BS to sustain off-grid operations [72]. A. System Optimization in a Stand Alone BS The advances made in the design and fabrication of deep cycle rechargeable batteries, charge controllers, inverters, and regulators have made it viable to integrate RES with BS. The main challenge is the sizing of the PV panels and the wind turbine to power a particular BS for which feasibility studies have been done using actual site data as well as simulated data, using software like HOMER, that provide the size and configuration of wind turbines and PV panels [91]. The site data may be used to calculate capacity of RES by using technique such as most unfavourable month method in which the months having least wind and solar energy are taken as reference for calculations. In [24], the authors use this method for sizing a hybrid system to power a BS site having 58.8 kWh energy consumption per day. In designing such a hybrid system, the combined P energy output of the RES is kept equal to the load i.e., Ei .Ai = Eload ; with Ei Ai = fi .Eload (where i represents the PV or wind system and Ai is the size of the wind turbine or PV array in m2 . fi is the P fraction of load provided by PV or wind generator such that fi = 1). The size of the battery storage is determined from the monthly maximum load demand EL,max and given as; Cbat = EL,max × 1000 × δt , Vsys × Nm (3) ACCEPTED MANUSCRIPT 9 TABLE IV G RID C ONNECTIVITY O F G REEN BASE S TATIONS Conventional Grid RES Only Off-Grid Configuration RES with DG D TE EP AC C B. Optimization of Energy Resources A BS having RESs, such as solar panels, in addition to utility and DG, is optimized in a way that the energy sources are adaptively used and overall energy cost is minimized. Different energy resource management strategies for RES [23], [52], [79]–[83] [8], [24], [63] [34], [54], [61], [62], [64], [84]–[90] enabled BSs have been proposed by researchers. The common objectives are to increase the life time and efficiency of power sources, minimize the usage of utility (grid) and maximize the use of RES, and optimization of energy consumption in the BS by adopting different techniques such as optimized cooling etc. The RES can be employed in one of following ways:Off-grid BS having RES installed on site. On-grid BS having RES installed on site. • On-grid BS with distributed generation of energy. • Smart grid BS with on-site/distributed RES installed. The optimization problems are often formulated as stochastic programming problem for adaptive power management, thereby manipulating the uncertainties of different variables such as spot-pricing, state of harvested energy, and temporal fluctuations in user traffic. Battery charging/discharging is another variable, which is discussed in detail in the next section. Stochastic power management formulation shows better result as compared to expected value formulation and worst case formulation, which apply average and worst case values, respectively. Energy consumption can also be reduced by optimizing the cooling/ventilation in the BS shelter, resulting in reduced usage of air conditioner. A sensing and control mechanism regulates operation of cooling devices by dynamic regulation of air conditioner and fans through an heuristic algorithm running on the control unit, that claims 70% reduction in the overall energy consumption [90]. Authors in [64], have tried to address the issue of reducing power consumption of a BS in a rural area, where grid power is highly unreliable, by optimizing the cooling of the shelter. The authors have made simulations of the BS load, comprising 1) transceiver load, 2) cooling load, and 3) battery charging losses, as well as the power sources comprising a DG and a PV array. It is demonstrated that under separate cooling environments for BS equipment and the batteries, a 3 kW PV panel can significantly reduce DG running to 3 hrs a day for a total load of 1.4 kW. In [93], energy management strategy for a battery-diesel stand-alone system, with distributed PV generation, has been developed based on grid frequency modulation. The battery inverter increases grid frequency to reduce power, which also optimizes the life time and efficiency of generator. A green BS powered by a smart grid has an advantage over a BS that is powered by conventional grid, in that the electricity supply can be controlled as per requirement. This has been M AN U where Vsys is the voltage of the system, Nm is the number of days in the worst month, δt is the time in days of autonomy needed, which is defined by the designer. Based on the site specifications and load calculations, the size of the wind turbine and PV array system is found to be comprising a 7.5 kW wind turbines, 8 kW PV array, 7.5 kW inverter (48 V DC input, 220 V AC output), and 114 batteries (6 V, 360 Ah) for a 48 V system voltage. Wind is favoured at locations where its yield is considerable throughout the year. The size and capacity of the wind turbine and PV panels is determined by the base load and the availability or not of other energy sources at the site. Generally, the rated capacity of the RES is much more than the rated load of the BS. A number of off-grid and standalone BSs have been modeled for deployment of solar panels and wind turbine as shown in literature [8], [61], [84]–[86]. In [85], a 3kW BS at an island is powered by 7.6 kW PV panels and and 8 kW wind turbine with 177KWh back up batteries. Their system comprises a wind generator and cylindrical photovoltaic modules that are mounted onto the wind generator pole to save installation space and cost. Similarly, a 3G/4G 3kW off-grid BS has been equipped with fuel cells in addition to solar panels and wind turbine and is claimed as 100% green. Authors in [63], have tried to model the most feasible configuration of a stand-alone hybrid energy system for GSM type mobile telephony BS in central India, with DG as a backup. They conclude that considering the operating and maintenance cost, an autonomous site powered by wind-solarhybrid system pays off in 2-4 years in a good sunny and windy location. On the other hand, in dimensioning the powering system for a typical long term evolution (LTE) BS, which solely relies on RES, with the current technologies, very large dimensions of solar panels are required for powering a BS in peak traffic hours [34]. Table V shows some examples of BS sites enabled with PV arrays and wind turbines. In all cases the peak value is much more than the load. This is because the systems are optimized for their average output ratings and worst yield days. References [43], [51], [73]–[78] RI PT On-Grid Configuration Remarks Research based on green BSs powered by Smart Grid Research based on Green BSs powered by conventional grid Research based on green BS powered only by RES Research based on green BS powered by RES & DG SC Smart Grid • • ACCEPTED MANUSCRIPT 10 TABLE V E XAMPLE O F G ENERATION C APACITIES O F PV A RRAYS , W IND T URBINE A ND BATTERY BANK Site Running Load P.V Array Wind Turbine Battery Bank South China Congo, Africa Africa Kiribati Island Central India 1500 3800 2500 3000 2000 7.8 kW 10.0 kW 6.8 kW 7.6 kW 5.0 kW 12kW (6kWx2) 15kW (7.5kWx2) 6.3 kW 8.0 kW 15kW (7.5kWx2) 5000 AH 5130 AH 54 AH 6.8 AH 183 AH W W W W W shown in [73], where a power management algorithm minimizes the energy cost for an RES enabled BS in a smart grid. An optimization problem based on stochastic programming problem is formulated with multi period recourse to manage energy under the uncertainties of spot power pricing, harvested energy, and varying user demand. C. Battery/Storage Optimization in Green BS area as noted through above examples. Other features of interest related to storage are the types of batteries/ storagedevices and battery capacity sizing. The state of storage/battery is a direct measure of the harvested energy. Based on the battery’s state of charge, algorithms are designed to optimize the BS’s traffic in terms of throughput, coverage etc. When the battery’s state of charge goes below a predefined threshold, and the BS’s power consumption is significant, different energy management strategies may be adopted, such as: • Reduce user QoS by decreasing throughput or increasing the data delay. • Switch to non-renewable energy sources till battery is sufficiently charged. • Reduce the coverage area of the BS to save power. AC C EP TE D M AN U Storage operation is a key issue in efficient utilization of energy produced through renewable sources, especially for large capacity applications. In a green BS, the harvested energy is stored in a battery bank of limited capacity, which must be used in such a way that the BS energy requirement is met efficiently. This means the energy drained from battery is not more than the energy stored. Another key feature is the battery state of charge (SoC); it should not be overcharged to avoid damage. Also, depth of discharge, which means a battery should not be discharged below a certain level. Usually this figure is kept around 50%. For example, in [64], a 48V, 600AH battery rack in a single tenancy BS is operated at 60% depth of discharge and takes 4 hours to reach its full capacity when charged at a rate of C/10. Often and energy storage system running heuristics are designed to achieve the aforesaid objectives. Simulation of battery behavior and estimation of battery’s health is an important aspect of research as discussed in [94]. The main objective in optimization of stand-by batteries is to properly size the battery bank for the load (BS) and minimize the ageing of batteries. Different types of batteries such as lead-acid, Lithium-ion, Redox-flow, Lithium-polymer etc are being investigated for use in cellular BS [62], [87]. Proper configuration and optimal battery utilization are particularly important in stand alone or off-grid Bs. Teh configuration/sizing depends on total load serviced and the amount of back-up you want, which could be few haours to few days. For example, in [87], researchers investigate these issues for a stand by power for a day and for five days. Their energy storage algorithm controlling the battery bank’s SoC is shown to sustain the BS load by effectively managing the solar in a stand alone power system. Similarly, work in [62], demonstrates an off line energy management strategy that decreases energy cost significantly if the capacity and charging/discharging rates of batteries are kept above certain threshold. The battery’s state of charge and discharge are the key features in managing the energy harvested from nature. Thus optimization of the battery operation is a distinct research RI PT [92] [24] [24] [85] [63] BS L OADS . SC Ref. FOR DIFFERENT D. Optimization of Radio Resources Green (harvested) energy maximization is also shown possible by the intelligent use of radio resources and traffic management. Strategies developed to this end involve scheduling of traffic in a green BS, regulating transmission power to maximize green energy usage, employing efficient beam forming techniques, and cell size adaptation as per RES availability. Adjusting power output results in increase and decrease of cell size, addressed as cell zooming [95], where the cell size zooms in and out according to traffic to conserve energy and maximize the use of green energy while maintaining required QoS. Authors in [53] have proposed an energyefficient resource allocation in OFDMA systems with hybrid energy harvesting BS. In regulating the transmission power, it is shown in literature that the BS power consumption comprises two components. One is static power consumption attributed to rectifiers, base band unit etc. and the other is the dynamic power which is attributed to the power amplifier (PA). Thus it is the later which fluctuates directly in relation to fluctuating traffic and is regulated. This has been shown in [80], where energy conservation in a micro BS is achieved through optimized use of transmission power of PA. The problem is formulated as a Markov decision problem and shows the existence of scheduling policies that minimize the transmission power. The total power, which can be satisfied with RES, is approximated as sum of static and dynamic power consumption discussed above. Energy cost minimization through radio resource management may be achieved for BS operating only on RES as well as those powered by RES and grid in [23]. In the former ACCEPTED MANUSCRIPT 11 the fluctuating demand of the network. Efficient and cost effective management of green and conventional energy resources is the fundamental objective of this research. Some other objectives are to determine the most advantageous network characteristics, in terms of density and topology of BS and RES that yield energy efficient schemes. Strategies such as sleep algorithms, energy cooperation, multi-cell cooperation, and on/off switching of BSs have been studied so as to minimize the overall network energy cost [34]. For example, authors in [43], have demonstrated that it is possible to save energy by optimizing the sleep cycles of a BS. We take a closer look at the pertinent optimization mechanisms next, especially scenarios where BS cooperate with each other and share their resources. E. Summary of Resource Management in Singular Configuration A. Grid Management in Green Networks SC The incorporation of renewable energy sources onto cellular networks may be done on site, as discussed in previous section or it may be achieved through distributed generation of energy such as solar and wind farms. With the incorporation of RES, the energy dynamics become flexible and thus explored by researchers for energy conservation. Smart grid, with its two way flow of energy and demand side management, adds further flexibility to this paradigm of green cellular networks [78], [97]. These features in a smart grid provide the opportunity to not only schedule energy sources for optimum consumption but also share/sell surplus energy to other BSs and/or grid thereby conserving energy [73]–[77]. Different energy management strategies for green cellular networks are found in literature with the prime objective of maximizing the use of RES, thereby reducing the CO2 emissions and conserving energy. In order to meet this objective a BS sleep mechanism is implemented in [51], which exploits the instantaneous energy state of the SG and the fluctuating user traffic. A similar model is presented in [43] that performs optimization of using various (green & conventional) energy sources to attain the aforesaid objectives of maximizing the green energy. In [77], authors propose several on-line strategies that only require causal knowledge of the renewable energy generation and the consumption of the BS. Similarly, in [98], energy cost minimization is desired via smart grid, which allows for energy exchange between the BSs as well as with the utility. The bidirectional flow of energy and information in SG is particularly suited for energy cooperation among cellular BSs. In [74], demand response management and power scheduling in a smart grid with a wind farm as RES has been studied. Authors maximize the total welfare function under the outage probability constraint and show that probability of shortfall is always positive regardless of number of turbines. The distributed generation of electricity at cellular BSs through RES (solar/wind) can be integrated through a micro-grid to optimally power BSs with maximum green energy. This novel concept of micro-grid of green BSs has been presented in [99], amongst others, which claims that BSs can be powered with harvested energy for 90% of the time. A green cellular network powered by conventional grid is also challenging in many aspects. One such aspect is to AC C EP TE D M AN U The introduction of RES in a BS poses the challenge of energy integration and management such that the use of harvested energy harvested is maximized. Optimization of battery operation is particularly important and challenging because renewable energy is invariably stored in batteries for its regulated use. The uncertainties associated with an RES enabled BS relate to the energy harvested from nature, the fluctuating traffic load, utility energy pricing, and power outages in case of unreliable grid environment. These variations are often modeled as stochastic optimization problem and solved through different stochastic programming algorithms. Optimum utilization of renewable energy has also been addressed as optimization of traffic parameters as well as optimization of BS’s coverage/capacity, scheduling of users, and userrate/sum-rate maximization. However, the foremost challenge in equipping a BS with a solar array or a wind turbine is the sizing and configuration of the systems. Sizing of PV arrays and turbines is directly effected by the fact whether or not a BS is off-grid or on-grid. Results show that although PV-only system has lower initial capital cost, overall, the net present cost (NPC), cost of equipment (COE), and operational cost (OC) makes the hybrid (PV + wind) system more economical than the PV-only or Wind-only systems [24]. Optimization of BS operation can also lead to significant energy reduction such as by optimizing the cooling environment inside the shelter. Whatever the means, the main objective remains optimization of energy resources in the hybrid system because their combined dynamics are challenging. RI PT case, the sum rate of all users is maximized by devising a multi-user down-link zero-forcing beam forming policies. In the later case, the stated objective is achieved by modeling and controlling the grid energy formulated as a time varying convex cost function of time. The radio resources can be manipulated to conserve energy by adapting the capacity and/or converge of the green BS. This is demonstrated in [88], where both aspects are optimized according to the available renewable energy and battery backup available. An intelligent energy management scheme is shown which reduces coverage when battery back-up drops below a certain threshold. The algorithm selects one of predetermined traffic patterns as per weather forecast data and controls the charging of the batteries as well. IV. R ESOURCE M ANAGEMENT IN G REEN C ELLULAR N ETWORKS Green cellular networks are networks that aim to conserve energy, be energy efficient and reduce CO2 emissions. With encouraging results for a BS powered by RES, research extended to green cellular network. The advantages reaped for a green BS in terms of energy efficiency and energy cost minimization can yield even better prospects for green networks [96]. As with a single BS, the challenge here is also to overcome the unsustainable nature of RES while meeting ACCEPTED MANUSCRIPT pm,n ,ct,n ,bn ,Pn ,Pt,n  +λT  X n∈N c n bn + ct,n ct,n t,n∈N n∈N Pgn bn + X X t,n∈N (4)  εt,n Pt,n ct,n  , EP TE D where the first and second term correspond to the total installation cost and connection cost respectively. The third term represents cost of utility, where λ is energy price in unit/kWh and T is specified life cycle of the deployed network. Pn is transmission power of n-th BS, Pt,n is power transferred from t-th to n-th BS, and ct,n represents connection cost between BS t-th and n-th BS. To solve novel cellular network planning framework considering the use of RESs and energy balancing, authors proposed a heuristic algorithm that decomposes equation (4) into two decoupled subproblems. The first sub-problem is termed as ’QoS-aware BS deployment’ that deploy BSs and connect TPs to BSs according to the energy distribution of BSs. The second sub-problem is nominated as the ’energy balancing problem’ that further reduces the total energy cost of the network operators by balancing the benefits given through energy sharing among deployed BSs and their RESs, and the incurred cost on BSs connection. Numerical results demonstrate CAPEX and OPEX savings in comparison to traditional deployment strategies. AC C RI PT X M AN U min Switching off BSs while associating users with neighbor cells by extending their coverage area (cell zooming) [102]. This requires centralized channel state information and traffic load information of every cell. • Expanding coverage of BSs powered by RES while constricting the coverage area of BS powered by the grid, also called cell breathing or [103]. This requires awareness of energy sources of a cell and awareness of stored energy at a BS. • Coordinated multi point (CoMP) transmission by BSs in which BSs cooperatively transmit data to cell edge users. This requires joint processing and coordinated scheduling strategies by the BSs interconnected on high speed data links. Scheduling of cell sizes, like dividing a macro cell into micro cells, or shutting down micro cells by extending coverage (cell zooming) with macro cell when traffic is low, is another way of multi-cell cooperation to optimize BS’ energy usage [104]. Closely associated to cell-splitting is cell-on-edge deployment scheme, which reduces network energy consumption as compared to uniformly distributed configuration [105]. In [46], authors have proposed a scheme to optimize the utilization of green (solar) energy during the peak traffic hours, i.e., day times, when solar energy is available, through cell zooming. Their algorithm does not enable the sleeping mode of BSs, which is usually applied to improve the energy efficiency of cellular networks during off-peak traffic hours. However, it does investigate cell size adaptation to traffic load. It is assumed that BS is powered by green stored energy if it exceeds BS energy demand, otherwise it is powered by the grid. The BS has high traffic volume and their proposed algorithms aim to reduce grid consumption in such traffic. The model assumes that BSs can adapt coverage area by varying power level of pilot signal, with upper bound on power, i.e., Q. More users will be served when coverage is large, however, more energy will be consumed. Further, the BSs always have data transmission in each time slot, at equal rate to all users. Thus, the number of users determine traffic volume at each BS. The green energy optimization problem is then formulated as minimization of the grid energy for each BS: • SC determine the most feasible layout of the BSs as well as the RESs, so as to satisfy the coverage requirement with desired QoS and optimal use of renewable energy. This issue has been addressed in [81], by formulating an optimization framework for planning the deployment of RES enabled BS in a network. A heuristic algorithm has been developed by modeling the 1) network characteristics, 2) energy considerations, 3) network planning constraints, 4) prorogation environment, and 5) power outage constraints. The optimization problem is based on the objective to select a subset of candidate BSs within N (set of BS sites) and to assign test points (TPs) within M (TP sites) to an available BS, taking into account the aforementioned constraints. The objective of the problem is then expressed as: 12 B. Multi-Cell Cooperation Multi-cell cooperation is a broad mechanism which entails traffic-load sharing between adjacent or close cells to minimize the network power consumption and/or maximize the use of green energy. Multi-cell cooperation is particularly applicable for heterogeneous networks employing (HeNBs) employing a mix of macro, micro and other cell types [100]. An overview of multi-cell cooperation has been presented in [72], which broadly classifies it as traffic-intensity-aware multi-cell cooperation and energy-aware multi-cell cooperation. In the former, the network is adapted by switching off cells with lesser traffic and off loading traffic to neighboring cells, whereas, in the latter the users are served by off-grid BSs powered by RES whenever possible. The various aspects of these approaches are covered elaborately by the authors in the above stated paper as well as in [101]. The basic approach in multi-cell cooperation is to adjust the cells in one of the following ways: min N L X X p~01 ,p~02 ,..p~0i ,..p~0L i=1 j=1 Gi,j subject to: (5) λk,i ≥ γ, ∀k ∈ 1, 2, ...M , where Gi,j is the on-grid energy consumed by j-th BS during the i-th interval, p~01 , p~02 , ..p~0i , ..p~0L is the pilot signal power of the BSs, γ represents the minimum SINR requirement, which is assumed same for all users. λk,i is the receiving SINR of user k at the i-th time slot. The green energy optimization problem in (5) is decomposed into two sub-problems by the authors to solve the optimization problem separately in temporal and spatial domains. The first sub-problem is called the multi-stage energy allocation problem, which aims to optimize the green energy usages at different time slots to accommodate the temporal dynamics of the green energy generation and the mobile traffic. The second sub-problem ACCEPTED MANUSCRIPT TABLE VI P OWER M ODEL PARAMETERS FOR D IFFERENT BS T YPES . NT RX Pmax [W] P0 [W ] Macro RRH Micro Pico Femto 6 6 2 2 2 20 20 6.3 0.13 0.15 130 86 54 6.8 4.8 RI PT BS Type ∆p 4.7 2.8 2.6 4.0 4.8 Psleep [W ] 75.0 56.0 39.0 4.3 2.9 SC the call/data traffic at any typical BS demonstrates significant temporal and spatial fluctuations in user concentration. Fluctuations in network traffic are encountered at different times of the day such as during the morning office hours, when activity is more as compared to evening hours. In addition, traffic is more dense in urban areas where people mostly visit and work, as compared to urban and rural areas. However, the cell is optimized for peak traffic in a predefined coverage area. That means at off-peak hours, these cells can save energy by switching off the BS radio resources to exploit the lean traffic. Consequently, on/off switching of BSs or putting BSs to sleep to conserve energy, has emerged as a promising research topic. The users of a switched off BS may be associated to another BS when certain pre-selected criterion is met, e.g., BS-to-user distance or data rate. There are two fundamental aspects associated with the sleeping of BSs. EP TE D M AN U is called the multi-stage energy balancing (MEB) problem, which accommodates the spatial dynamics of the mobile traffic and seeks to maximize the utilization of green energy by balancing the green energy consumption among BSs. The two sub-problems are tackled through heuristic algorithms along with an energy allocation algorithm, which is applied to each BS to adjust state of its green energy consumption. Overall, the green energy optimization algorithm is shown to maximize the utilization of green energy and as a result, the algorithm requires a smaller green energy generation rate to achieve zero grid energy consumption. In [106], authors have proposed an energy aware cell size adaptation strategy to optimize the utilization of green energy in cellular networks by minimizing the maximal energy depleting rate of the low-power HeNBs powered by green energy. In a heterogeneous multi-cell cooperation of lowpower (green) BS (LBSs) and high-power (grid) BSs (HBSs), authors proposed a scheme with an objective to minimize the maximal energy depletion rate of low-power BSs through cell breathing, thereby covering more users with green BSs. In a comprehensive analysis of energy dynamics of green cellular HeNBs, cell breathing of smaller cells is shown to maximize green energy usage [89]. In the proposed HetNet model, the macro BS is powered by the grid and provides general coverage in its area, whereas three green (micro/pico/femto) cells provide coverage to high user density buildings/areas within the macro BS’ coverage. After coming up with a generalized model to describe the energy evolution process of RES enabled cells, the energy dynamics metrics, which are ’energy transfer time’ and ’energy outage probability’, are analyzed by adopting diffusion approximation. Based on the results obtained in the analysis, a fluid cell management scheme is proposed where smaller cells adjust their radii to ensure the sustainability of green energy. It is shown that the scheme performs well in improving both the lifetime and green energy utilization in HeNBs. 13 C. On/Off Switching and Sleep Mode of BSs AC C A cellular BS is always consuming energy whether it is serving any traffic or not. The consumption level of this energy ranges from a base-line for no traffic to a full traffic load level. However, in a report from the Energy Aware Radio and neTwork tecHnologies (EARTH) 1 project of EU, researchers quantify three types of energy consumption levels for different types of BS, as shown in Table VI below: P0 is the static power at no load, Pmax is the power consumed at max load and Psleep is the power consumption when BS is put to sleep [107]. The energy consumption of a BS traditionally varies between no traffic load and when BS is operating at max capacity load. However, Psleep has been defined as the power consumption when the BS is not transmitting anything and is considered asleep. NT RX denotes the number of transmitters per node, and ∆p denotes the slope with which power varies according to increase in traffic load. One of the key findings of EARTH project is that a BS is transmitting much below its capacity most of the time. Also, 1 www.ict-earth.eu • • How and when to associate users from one BS to another BS, without compromising users’ QoS? [108] How to perform the on/off switching mechanism of a BS; centrally or locally? Research shows that significant energy saving can be made by switching off BS at certain periods [109]. In times when call traffic is low, the network operators can employ BS on/off switching or sleep mechanisms to save power [59]. A comprehensive analysis of BS on/off switching is presented in [44], which suggests a design principle on the concept of network-impact, introduced in the paper as a metric for on/off switching decision making. Both user association and on/off switching mechanism have been addressed in this work. This work shows that the amount of energy saved is dependent upon the traffic ratio of mean and variance, and BS deployment. Results show that the proposed algorithm can save up to 80% of network energy. The stochastic energy management model that exploits temporal variations in traffic and harvested energy may be augmented with a BS sleep mechanism to make further energy savings as shown in [43]. The optimization problem is considered as a combinatorial problem that has been solved with a heuristic algorithm. At each iteration, the algorithm switches off the BS that when eliminated maximizes utility function U , defined in the problem. It converges when none of the active BSs can be turned off. Simulations are done for a 4 × 4Km2 LTE coverage area for a total 16 BS placed uniformly. The results were compiled for 48 hours, which showed 15 − 16 active BSs in peak hours and 1-2 BSs in ACCEPTED MANUSCRIPT C(b) = N X (b) (b′ ) Pi /Pi , (6) i=1 (b′ ) (b) • • • • RI PT a lucrative scenario for energy sharing as smart grids offer perfect environment for energy cooperation between the cellular BS, where BSs not only cooperate in traffic sharing by on/off switching but also by sharing their surplus green energy. The common objectives related to energy cooperation are maximizing the use of renewable energy, minimizing use of grid energy, load sharing between the cooperating BSs, and minimizing the GHG emissions. Thus, energy cooperation entails certain desirable features in the network, which are: Energy storage capacity at the BS/node. A management (processing) capability at the BS/ node. A central control/management unit in the network. Smart grid or physical connectivity between BSs. SC In various energy cooperation models/scenarios discussed in the literature, the direction and quantum of energy to be transferred between the nodes is invariably determined in one form or the other. Both on-line and off-line energy cooperation algorithms have been developed to optimally utilize the harvested energy [79], [111]. Decisions are made on the basis of energy-state information and energy-cost information. Energy state information pertains to instantaneous energy state of energy available, energy harvested or energy consumed. Whereas, energy-cost information tells about the pricing/cost of energy procured, consumed and transferred. As stated above, the objective is optimal scheduling of energy sources with intelligent utilization of energy generated from RES. Work in [83] aims to maximize the power and bandwidth allocation of all users in a two node model, using the energystate information, which is mathematically expressed as:- M AN U night/off-peak hours, serving all users. The BSs are switched on gradually by the proposed green algorithm, while meeting the defined QoS. The user outage is high in off-peak hrs, however, low in peak hours as maximum BSs are operational in peak hrs. Overall the energy procurement model is shown to conserve energy and utilize green resources. Authors in [52] have also shown that energy can be saved by adopting power aware strategies for on/off switching of BS in LTE network. The BS’ transmission power requirement is used as the metric for ranking of BS for switching-Off priority, in their simple model. Authors proposed two criterion for selecting a BS to be switched of. The first is called Power-aware strategy, which finds total power consumption of each BS P(b) and switches off the one having highest PN (b) power consumption, which is calculated as P(b) = i=1 Pi , (b) where Pi is the power consumed in serving ith user. The other is Power-ratio-based strategy, which relates the UE (user equipment) directly with its serving BS (b) and inversely with ′ the power consumption of its neighboring BSs (b ) that are sharing the load, and is mathematically expressed as follows: 14 AC C EP TE D where the ratio between Pi and Pi suggests that for each user i, the priority of switching ’off’ should be for the BS with the highest performance metric C(b) . In both the strategies, the process of switching ’off’ continues until the network fails to accommodate off-loaded traffic at a given target QoS requirement. Simulation results obtained for 100% 2 Mbps data streaming profiles indicate that both power-based and power-ratio-based approaches can achieve enhancements in power saving compared to the distance-aware approach. In [110], authors introduce a dynamic programming algorithm that determines which BSs are to be put sleep, considering the evolving state of cellular traffic and the harvested energy at each BS, while determining the optimum utilization of Resource Blocks for the active traffic. The network’s QoS is maintained by minimizing the call rejection probability and the running call’s hand-off probability. Near optimum performance is achieved by the algorithm and significant energy savings are made by sleep mode of operations as compared to network energy consumption when it does nothing to maximize the usage of the harvested energy. D. Energy Cooperation Between BSs in a Green Network It is quite evident that an RES enabled BS that is not fully loaded or is in idle state, may have surplus energy available from its RES, which can be made available for the neighboring BS. Such an arrangement is called energy cooperation and requires both energy and data control amongst network elements interconnected for such cooperation. In such schemes, the nodes may provide the data transmission service, whereas the power lines are used to transfer the harvested energy between nodes. The BSs act as energy storage devices, which cooperate with each other for their energy needs. A network powered by smart grid as well as RES provides Na X  Pa,i Rtot = Wa,i log2 1 + na,i Wa,i i=1   N b X Pa,i Wb,k log2 1 + + , nb,k Wb,k  (7) k=1 where Wa,i , Pa,i , na,i and Wb,k , Pb,k , nb,k are the allocated bandwidth, allocated power and effective noise power spectral densities for every user, in cell a and b respectively; with some bounded limits acting as constraints for the total bandwidth and power consumption in the network. We also find in literature introduction of an energy management unit (EMU) for centralized control of energy flow between between various entities. Such an EMU is shown to decide on energy cooperation between nodes on the basis of energy-cost data discussed above. The various constraints considered in energy cooperation schemes pertain to the battery’s state of charge, QoS defined, min/max data rates and energy balance at sites i.e., net energy in vs net energy out. In addition to energy cooperation between BS sites, we also find controlled energy transfer from network to grid, particularly smart grid. In [112], authors have proposed an energy cooperation algorithm amongst green BSs in a multitier cellular network, where bi-directional energy cooperation between smart grid and network is employed to achieve similar energy saving results as others. ACCEPTED MANUSCRIPT 15 TABLE VII E NERGY COOPERATION SCENARIOS FOR CELLULAR BS S ENABLED WITH RENEWABLE ENERGY SOURCES (RES). No Grid Smart No central controller With central controller No central controller UTILITY Fig. 6. Energy cooperation between BSs powered by RES and interconnected through a controller with bi-directional energy flow. saved or net power ratio increase in neighbouring BS. Putting BS to sleep in off-peak traffic hours is a promising technique for all networks, especially HetNets. Switching off BSs while associating users with neighbor cells by extending their coverage area, which requires centralized channel state information and traffic load information of every cell. Another is expanding coverage of BSs powered by RES while constricting the coverage area of BS powered by grid. There are two fundamental aspects associated with on/off switching of BSs. Firstly, how and when to associate users from one BS to another BS, and secondly, how to perform the on/off mechanism of a BS. The main objective is sharing of renewable energy to meet BS demand so as to reduce the utility bill. A central controller or an energy management unit is envisaged a necessary addition to realize energy cooperation. EP TE D M AN U Another scenario that has been investigated involves sharing BSs between different network operators. It is a known fact that BSs of multiple operators are densely packed in urban city centers to satisfy the high traffic load in these areas. Often BSs are co-located on a roof top or stand within meters of each other. Sharing BSs in such locations can make substantial savings in energy if the operators agree to do so. The authors of [30] estimate that such BS cooperation can save as much as 85% of the total energy consumed during off-peak hours in dense urban areas, which is considered 35% over and above the savings operators would make if they acted on their own. In other works, authors have proposed a joint energy and spectrum cooperation scheme between different cellular systems to reduce their operational cost [82]. Wireless power transfer between BSs is also under study, however, mostly it is being explored in the realm of mobile user terminals [113]. Other papers that investigate energy cooperation include [114], which employs bi-directional energy flow in smart grid in conjunction with CoMP. In [115], authors studied joint communication and energy cooperation between cellular BSs powered through smart grid, for energy cost savings. Similarly, Table VII highlights the salient features of RES enabled BSs cooperating with each other in sharing the surplus green energy. Smart grid adds a definite advantage to these networks in facilitating energy transfer. CONTROL RI PT Smart Grid [79], [98], [112] [115] [111] [83], [113] SC With central controller E. Summary of Green BSs in Cellular Configuration AC C The emerging paradigm of green cellular networks under smart-grid environment is of particular interest to researchers. The bi-directional flow of energy and information in a SG allows intelligent use of grid energy in conjunction with variations in the energy harvested from nature and the prevailing user traffic. The energy cost reduction via temporal/spatial variations in energy state and the load at a site is often tackled as stochastic programming problem, which is solved through different solvers and algorithms. A SG powered network can also share the surplus energy available at its nodes amongst its sites or sell it back to the grid through net-metering. Such energy cooperation between sites allows optimal use of RES and minimization of utility. Other strategies found in literature for energy conservation in green cellular networks employ multi-cell cooperation, BS sleep mechanism and energy/trafficcooperation. In multi-cell cooperation the cell radii is adjusted as per predefined metrics and criterion; the main objective being maximization of RES. In sleep mechanism a BS is put to sleep remotely under pre-defined criterion such as total power V. C ONCLUSION AND F UTURE W ORK The futuristic wireless technologies of 5G are going to bring dramatic performance improvements in terms of data rates, network capacity, latency, cost and coverage [116]. In order to achieve these desired goals, technological improvements are underway at all tiers as well as the search for new innovative solutions. Densification and diversification of the radio access network will requite new models to make them economical and energy efficient such as dynamic and adaptable allocation of resources. With smart grid and renewable energy systems also maturing, a new paradigm of green communication is emerging that aims to improve energy efficiency of cellular networks comprising macro, micro, femto and pico base station transceivers [10], [55]. The industry is also aware that technology improvements must not be at the cost of adverse climatic effects, thus renewable energy based solutions to meet the power demands are order of the day [26]. Renewable energy systems, particularly solar and wind, are a viable option for a cellular BS as well as the whole network. RES can be employed for not only cutting down on the harmful GHGs but also for decreasing the energy cost of traditional networks. A BS is the most energy hungry element in a cellular network and consumes upto 60% of the energy consumed by a network of macro BSs. Renewable energy is ACCEPTED MANUSCRIPT B. Cooperative Relays RI PT Therefore, more research is required in making cognitive radios part of green cellular networks. Green-energy-powered cognitive radio networks [121], especially cognitive radios at a BS with RESs and networks employing energy saving techniques such as on/off switching and cell zooming are still uncharted territory for research. SC In conventional networks, it is difficult to extend the range/ coverage to the distant users due to impeding network characteristics such as path loss, signal fading, etc. Increasing transmission power is not always feasible due to negative effects such as co-channel interference and increased power consumption. Cooperative relays allow extension of coverage by creating a virtual MIMO system [122]. Relaying is achieved either by installing fixed relays within the network or by making use of other users as relays, as and when required [123]. The relays are more complex than mere repeaters as they support complex algorithms and advanced functions. In both fixed or mobile relays, cooperative relaying techniques involve scheduling, routing and data storing algorithms, amongst others, which require much research before they can be integrated into networks [124]. The fixed relays can be investigated from the point of view of incorporating RESs, which will allow them to function independent of grid and at remote locations. AC C EP TE D M AN U not only feasible for stand alone BSs that are off the grid but equally feasible for BSs deployed in a network supported by the grid energy. It is also seen that small cells like femto and micro cells need lesser energy. Thus, small cells are more feasible for the RES to power them. However, for proper integration of RES into the present networks, proven system designs are required that can readily replace the diesel generators. Sizing, interfaces, infrastructure etc. need to be defined and standardized so that RESs can be easily installed and replaced when needed. Smart grids with distributed generation of green energy can provide clean and cheap power to the cellular networks, thereby, decreasing the energy cost and reducing the harmful GHGs. Smart grids have special significance for cellular BSs in terms of facilitating energy exchange between them. Smart grids also provide flexibility in energy price forecasting which allows for scheduling of energy sources to maximize the use of green energy. Energy cooperation is not only possible between the BSs but also between green networks and local community where utility supply is non-existent or intermittent. Intelligent controllers are required that can control the energy flow between different modules. Researchers have come up with the optimal energy management strategies to use renewable energy in their systems under various scenarios that make use of centralized or decentralized controllers. Such controllers will make the BSs smart energy wise and provide effective demand side management in the emerging smart grid environment. Incorporating these controllers into existing hardware is a challenge for the system designers. The essence of research in the are of RESs has been positive and proven empirically as well as theoretically. What is required is a joint policy statement by the key industry players to integrate PV-wind energy system solutions at new sites and gradually replace the generator sets at the older ones. Standardization of the RES is a serious challenge for their practical implementation. Greening of cellular networks through increased energy efficiency as well as incorporation of renewable energy is a growing research area. These aspects of RESs have good prospects when co-joined with other emerging trends in wireless networks, such as cognitive radios, cooperative relaying and cloud radio access network (C-RAN). 16 A. Cognitive Radios Cognitive radios are designed to exploit the channel by sensing the spectrum and using holes in the spectrum [117]. Cognitive radios offer lucrative possibilities of saving energy and expanding bandwidth while maintaining the desired QoS. The ability of a network based on cognitive radios to intelligently modify its parameters or reconfigure, allows the network to manage the resources optimally and dynamically. This means reduction in energy consumption and thus power savings [118]. Recent studies show that cognitive radios based techniques lead to reduction in energy consumption of a network [119], however, the proposed solutions are still considerably complex for practical implementation [120]. C. Energy cooperation in C-RAN Power generated at a BS through RESs can be particularly useful if utilized in a network of co-located BS; as shown in Fig. 6. It is often seen that different service providers are co-located at remote sites or along national highways due to certain geographical advantages. If such BSs are equipped with RES, other than a conventional source (diesel generator), they can efficiently be interconnected in a local network to augment each other in power use. The cellular network traffic at each node varies both temporally and spatially, offering different load situation for each service provider. Thus, a node with less load may have redundant power available that it can lend to its neighbors. The load sharing can be controlled through a central control station, which receives both loadinformation and power from each BS and directs the surplus power of one node towards the other. Such energy sharing in an off-grid scenario can relieve each service provider from expensive diesel generated power to some extent. This type of energy sharing is already a topic of research, particularly in smart grid scenarios [79], [82], [83], [98], [111]. Energy cooperation mechanism has been discussed in this paper which has much scope for study and implementation, especially in smart grid environment. Other schemes like on/off switching and multi-cell cooperation also benefit with the employment of renewable energy. In stand-alone BSs, co-located at a remote site, it especially holds lucrative potential vis-a-vis alternate energy co-sharing. With physical interconnection and necessary software/hardware upgrade, this research has rich ACCEPTED MANUSCRIPT R EFERENCES EP TE D [1] H. Al Haj Hassan, L. Nuaymi, and A. Pelov, “Renewable energy in cellular networks: a survey,” in Online Conference on Green Communications (GreenCom), 2013 IEEE. IEEE, 2013, pp. 1–7. [2] H. A. H. Hassan, L. Nuaymi, and A. Pelov, “Classification of renewable energy scenarios and objectives for cellular networks,” in Personal Indoor and Mobile Radio Communications (PIMRC), 2013 IEEE 24th International Symposium on. IEEE, 2013, pp. 2967–2972. [3] L. Suarez, L. Nuaymi, and J.-M. Bonnin, “An overview and classification of research approaches in green wireless networks,” Eurasip journal on wireless communications and networking, vol. 2012, no. 1, pp. 1–18, 2012. [4] D. Feng, C. Jiang, G. Lim, L. J. Cimini, G. Feng, and G. Y. 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Fettweis, “Green communications in cellular networks with fixed relay nodes,” Cooperative Cellular Wireless Networks, p. 300, 2011. 20 ACCEPTED MANUSCRIPT RI PT Faran Ahmed is currently an Associate Professor at College of Aeronautical Engineering (CAE). He completed his undergraduate studies in Avionics Engineering from CAE, NED University, Karachi in 1989. He holds Master’s degree in Electronics Communications, from NWFP Univ of Engg Tech, Peshawar, Pakistan. He is currently working towards his PhD from COMSATS Institute of Information Technology, Wah Campus, Pakistan. His current research interest includes Wireless Communication with Renewable Energy commonly known as Green Communication. AC C EP TE D M AN U SC Muhammad Naeem received the BS (2000) and MS (2005) degrees in Electrical Engineering from the University of Engineering and Technology, Taxila, Pakistan. He received his PhD degree (2011) from Simon Fraser University, BC, Canada. From 2012 to 2013, he was a Postdoctoral Research Associate with WINCORE Lab. at Ryerson University, Toronto, ON, Canada. Since August 2013, he has been an assistant professor with the Department of Electrical Engineering, COMSATS Institute of IT, Wah Campus, Pakistan and Research Associate with WINCORE Lab. at Ryerson University. From 2000 to 2005, he was a senior design engineer at Comcept (pvt) Ltd. At the design department of Comcept (pvt) Ltd, Dr. Naeem participated in the design and development of smart card based GSM and CDMA pay phones. Dr. Naeem is also a Microsoft Certified Solution Developer (MCSD). His research interests include optimization of wireless communication systems, non-convex optimization, resource allocation in cognitive radio networks and approximation algorithms for mixed integer programming in communication systems. Dr. Naeem has been the recipient of NSERC CGS scholarship. Waleed Ejaz (S’12–M’14–SM’16) received the Ph.D. degree in information and communication engineering from Sejong University, South Korea. He is currently a Senior Research Fellow with the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada. His current research interests include Internet of Things, energy harvesting, 5G cellular networks, and mobile cloud computing. ACCEPTED MANUSCRIPT SC RI PT Muhammad Iqbal was born on October 15, 1976 in Multan. He got B.Sc. Electrical Engineering degree in 1999 from University of Engineering and Technology, Lahore. After completing B.Sc. Electrical Engineering, he served in the state owned telecommunication company for more than seven years. In 2007 he completed his MS Telecommunication Engineering from the University of Engineering and Technology, Peshawar. After completing PhD by July, 2011 from Beijing University of Posts and Telecommunications, P.R. China, he rejoined COMSATS and till this date working as Assistant Professor, Electrical Engineering Department, CIIT, Wah Campus. His research interests include signal and information processing, wireless communication, smart grid and applied optimization. AC C EP TE D M AN U Alagan Anplagan received the B.A.Sc. M.A.Sc. and Ph.D. degrees in Electrical Engineering from the University of Toronto, Canada. He joined the Department of Electrical and Computer Engineering at Ryerson University in 2001 and was promoted to Full Professor in 2010. Dr. Anpalagan directs a research group working on radio resource management (RRM) and radio access & networking (RAN) areas within the WINCORE Lab. His current research interests include cognitive radio resource allocation and management, wireless cross layer design and optimization, cooperative communication, M2M communication, small cell networks, and green communications technologies. Dr. Anpalagan serves as Associate Editor for the IEEE Communications Surveys & Tutorials (2012-), IEEE Communications Letters (2010-13) and Springer Wireless Personal Communications (2009-), and past Editor for EURASIP Journal of Wireless Communications and Networking (2004-2009). He also served as Guest Editor for two EURASIP SI in Radio Resource Management in 3G+ Systems (2006) and Fairness in Radio Resource Management for Wireless Networks (2008) and, MONET SI on Green Cognitive and Cooperative Communication and Networking (2012),. He co-authored of three edited books, Design and Deployment of Small Cell Networks, Cambridge University Press (2014), Routing in Opportunistic Networks, Springer (2013), Handbook on Green Information and Communication Systems, Academic Press (2012). Dr. Anpalagan served as TPC Co-Chair of: IEEE WPMC’12 Wireless Networks, IEEE PIMRC’11 Cognitive Radio and Spectrum Management, IEEE IWCMC’11 Workshop on Cooperative and Cognitive Networks, IEEE CCECE’04/08 and WirelessCom’05 Symposium on Radio Resource Management. He is a registered Professional Engineer in the province of Ontario, Canada.