ROUTING SCHEMES IN COGNITIVE RADIO NETWORK KANCHAN HADAWALE & SUNITA BARVE

International Journal of Mathematics and Computer Applications Research (IJMCAR) ISSN 2249-6955 Vol. 3, Issue 2, Jun 2013, 275-280 © TJPRC Pvt. Ltd. ROUTING SCHEMES IN COGNITIVE RADIO NETWORK KANCHAN HADAWALE & SUNITA BARVE Computer Department, MIT Academy of Engineering, Alandi, Pune, Maharashtra, India ABSTRACT Cognitive Radio (CR) is a promising technology which deals with using vacant spectrum of licensed frequency band opportunistically. Cognitive Radio Network (CRN) is introduced to solve spectrum usage inefficiency problem. Thus inefficient usage of the existing spectrum can be improved through opportunistic access to the licensed bands without interfering with the existing users. In CRN, route construction must not affect the transmission of Primary User activity. Thus, in CR technology challenge of maintaining optimal routes in Ad-Hoc CR network arises due to PU activity & mobility of spectrum resources i.e. CR users. The novel functionalities and current routing challenges in CRN are explained in detailed. This work focuses on designing effective routing solutions for multi-hop CRNs. Routing solutions are classifieds into two categories: Full spectrum knowledge based approaches and local spectrum knowledge based approaches. In each category we describe methodologies, routing metrics. Finally open research issues of routing in CRN are outlined. KEYWORDS: Spectrum Hole, Cognitive Radio, Primary User, Full Spectrum Knowledge, Local Spectrum Knowledge INTRODUCTION Current wireless networks have fixed spectrum assigned by governmental policies. Where most of the portion of spectrum is used irregularly in few spaces because of that most amount of spectrum remains unutilized. The use licensed spectrum is quite uneven and depends on specific wireless technologies, their commercial success in market. According to recent studies by Federal Communications Commission (FCC) utilization of assigned spectrum varies in the range from 15% to 85%. Following figure shows how spectrum utilization varies for assigned licensed spectrum band. Dynamic Spectrum Access (DSA) is introduced to solve spectrum usage inefficiency problem. DSA introduced policy based intelligent radios known as Cognitive Radios. A „„Cognitive Radio‟‟ is a radio that can change its transmitter parameters based on interaction with the environment in which it operates. Cognitive radio networks provide high bandwidth to mobile users through heterogeneous wireless architectures and dynamic spectrum access techniques. Thus DSA enables cognitive users to use existing spectrum efficiently without disturbing primary user activities. Cognitive radio techniques allows to use or share spectrum opportunistically. Cognitive Radio technology allows users to detect available portion of spectrum as well as primary user‟s presence, to select best channel, to share the channel with other users and to free the channel whenever primary user is detected. Cognitive radio has 2 main characteristics.  Cognitive Capability: It is ability of radio technology to sense the radio environment. It captures the temporal and spatial variations in radio environment and avoids interference to primary user. This capability identifies unused portion of the spectrum, which is known as “spectrum hole” or “white space”. Cognitive capability task is represented as the cognitive cycle. Three main steps of cognitive cycle are: spectrum sensing, spectrum analysis and spectrum decision. i) Spectrum Sensing: Cognitive radio detects available spectrum bands by capturing their information. ii) Spectrum Analysis: Characteristics of spectrum holes such as data rate, transmission mode and 276 Kanchan Hadawale & Sunita Barve bandwidth estimated. iii) Spectrum Decision: According to spectrum characteristics appropriate spectrum band selected. Cognitive radio should keep track of radio environment since it changes over time and space.  Reconfigurability: Once radio environment gets captured, reconfigurability helps cognitive radio to program dynamically according to radio environment. It is programmed to operate on different frequencies and to use different transmission technologies. It is the capability of adjusting the operating parameters for the transmission. Parameters that can be adjust are, operating frequency, modulation, transmission power, communication technology. The main objective of cognitive radio is to detect best available spectrum via cognitive capability and reconfigurability. ROUTING Routing is the process of moving packets across a network from one host to another host. Traditional wireless network routing is different from cognitive network routing. In traditional wireless network, routing nodes uses same frequency for communication. Only the node mobility and transmission power affects the routing. In cognitive radio network, routing is combination of traditional routing and spectrum management since spectrum availability varies from node to node with respect to time and location. Spectrum availability is also affected by the primary user activities. For routing and spectrum management two approaches are considered-cross-layer and decoupled. In decoupled approach, route selection is performed independent of spectrum management using various shortest path algorithms. In this approach each source code finds candidate path by using DSR and by scheduling time and channel for each hop. In cross-layer approach considers route selection and spectrum management jointly. As spectrum availability affects the network performance more thus, cross-layer approach is more beneficial for cognitive radio network. The major challenges of routing in cognitive radio network are as given below.  Spectrum Availability: Routing module must be aware of spectrum availability which is achieved by monitoring spectral environment.  Primary User Activity Awareness: Topology of cognitive radio network is affected by primary user activities as well as by route quality measurements such as bandwidth, throughput, delay, energy efficiency which should be considered with spectrum availability.  Route Maintenance: Primary user activities may results in frequent route rerouting which in turn will degrade the network performance. Thus effective signaling procedures are required for convenient routing in cognitive radio network. Following figure shows classification of cognitive routing schemes. Following figure shows classification of cognitive routing schemes. Figure 1: Classification of Routing Schemes Routing Schemes in Cognitive Radio Network 277 FULL SPECTRUM KNOWLEDGE Full spectrum knowledge is based centralized computation of routing paths. In full spectrum knowledge, routing solutions are either based on graph abstraction of CRN or may use mathematical programming tools to model and design flows in cognitive multi-hop network. Graph Based Routing In this subclass graph theory methodologies are used for computation of multi-hop routes since it provides very effective algorithms for multi-hop cognitive radio network routes. In multi-hop cognitive radio network routes are designed into two phases: graph abstraction and route calculation. a) Graph Abstraction: In this phase, physical network topology is represented by logical graph. It generates graph structure G = (N, V, f(V)) Where, N = No. of nodes V = No. of edges f(V) = Function to assign weight to each edge of graph. b) Route Calculation: By considering weights of edges route calculation designs path in network graph that connects source to destination. Following research work is done on graph based routing approaches by using different graph theories. The class of models that can be estimated using pool estimation, can be written as, In 2008, authors of [2] propose two-phase approach for channel assignment and routing in multi-hop cognitive radio network. It mainly focuses on channel assignment rather than considering primary user activities. It creates layered graph where no. of layers are equal to no. of available channels. In layered graph, each secondary user is represented by a node A and M additional sub nodes A1, A2, A3, ….. AM one for each available channel. Edges of layered graph are divided into 3 types. a) Access Edges: These edges connect each node with all its corresponding sub nodes. b) Horizontal Edges: It connects sub nodes of same logical layer if two corresponding secondary users can be tuned to that channel. Horizontal edges are weighted by considering quality parameters of link such as bandwidth, link availability, link load etc. c) Vertical Edges: It connects sub nodes of different layers of single secondary user device. Vertical edges are weighted through quality parameters like cost for switching channels, signal improvement in response to noise ratio. Optimization Approach Optimization models and algorithms are used for optimal route design in multi-hop cognitive radio network. In [3] authors focus on spectrum sharing techniques through Mixed Integer Non-Linear Programming (MINLP) formulation to maximize spectrum reuse factor. MINLP formulation use three aspects link capacity, interference and routing. It ensures multi-hop path between each source-destination pair in cognitive network. Link Capacity: Shannon‟s law is used to define link capacity with bandwidth and signal to interference ratio. Interference: Interference range RT is defined as, RT = (Q/QT) 1/η Where, QT = Threshold power spectral density Routing: For routing traffic in network flow balance constraints is captured at each node. Routing is balanced by maintaining incoming flow at a node equal to outgoing flow. MINLP proposed centralized heuristic algorithm that works iteratively and in two operation phases: a) Set up and solve the relaxed LP version of the original problem as done to obtain the lower bound. b) Sort the assignment variables in descending order. c) Set to 1 (fix) the largest variable in the list, and set to 0 the remaining variables referring to same user. d) Solve the new LP formulation of the problem with the variables fixed at step 3. The interference is modeled through the concept of interference range which avoids effects of interference from 278 Kanchan Hadawale & Sunita Barve multiple transmitters and link capacity is defined by considering that interference. LOCAL SPECTRUM KNOWLEDGE In this approach spectrum occupancy information maintained at each secondary user is used to make resource management decisions about the network state. Interference and Power Based Solutions In [4], to get minimum weight paths communication system is divided into operating system and communication system. Operating system is responsible for selecting wireless communication interface which is used to access various wireless systems like cellular or WLAN. A dedicated interface Common Link Control Radio (CLCR) is used for communication between cognitive radio terminals to maintain cognitive network functions. Two main functions using CLCR interface are neighbor discovery and path discovery. For neighbor discovery CLCR uses transmission power required to reach to all neighbors. The link weight is defined as a function of transmission power used by wireless systems. For increasing transmission power WS[i] with distance the paper uses space propagation model as follows, PTXWS[i] = PRXWS[i] . (4πd/λWS[i]) 2 Where, i=1, 2… W are W wireless systems PTXWS[i] = Transmission power of WS[i] PRXWS[i] = Received signal power of receiver λWS[i] = Wavelength of WS[i] and d = distance between transmitter and receiver Routing protocol is proposed to find path with minimized routing weight between source and destination. Route discovery is similar to state routing algorithms where the newly introduced weight is used. Delay Based Solutions Routing quality can also be measured in terms of delay required for information transmission. In wireless network, delay components are considered related to spectrum mobility. Delay-aware routing metrics consider following delay components, a)Switching Delay: Delay required by a node for path switching from one frequency band to another. b)Medium Access Delay: Based on MAC access schemes used in given frequency band. c)Queuing Delay: based on transmission capacity of a node on given frequency band. In [5], delay metrics considers both switching delay between frequency bands (Dswitching) and Medium Access i.e. Backoff between given frequency bands (Dbackoff). At node i, cumulative delay metric along a candidate path is computed as, Droute, i = DPi + DNi Where, DPi = Dswitching + Dbackoff, i If path is composed of H hops, switching delay can be, Dswitching, i [Bandj - Bandj+1] Where, k = constant and Bandj = frequency band from node‟s active bands Throughput Based Solutions In [6], authors proposed Spectrum Aware Mesh Routing (SAMER) protocol which considers short term and long term spectral availability. This protocol selects routes with highest spectrum availability as candidate paths. It keeps track of long- term routing metric based on spectrum availability and “least-used spectrum first” routing protocol. It balances between long-term route stability and short-term route performance via runtime forwarding route mesh network. Authors defined metric for estimation of Path Spectrum Availability that captures, i) Spectrum availability at node i, its bandwidth and contention from secondary users. ii) Depending on bandwidth and loss rate quality of spectrum blocks. In SAMER optimal routing is defined in terms of, hop-count, end-to-end throughput, spectrum utilization. Routing metric includes Routing Schemes in Cognitive Radio Network 279 both route quality based and high spectrum availability based routing. To increase short-term routing performance, SAMER selects a fine time-scale and coarse time scale. SAMER avoids highly congested and unavailable links. Link Stability Based Solutions Link availability in cognitive network is different from traditional wireless network. Channel availability in cognitive network varies with time and space. Thus stable links is one of the routing solutions which can be achieved through following routing schemes. In [7], authors proposed SPEctrum-Aware Routing (SPEAR) protocol to get link flexibility with spectrum heterogeneity. Spectrum availability depends on location and presence of primary user activities. SPEAR considers following concepts for link stability,    To deal with spectrum heterogeneity, spectrum discovery is integrated with route discovery. By minimizing inter-flow interference, channel assignments are coordinated on per-flow basis. To achieve spectrum diversity and to reduce intra-flow interference, local spectrum heterogeneity is considered. SPEAR set-up route by broadcasting and AODV-style route discovery is used to get each node‟s channel quality and availability information. Each Route Request (RREQ) contains node IDs, nodes spectrum availability and link quality. All these parameters are combined at destination to select optimal route. SPEAR discovers multiple paths with redundant paths that are sent to destination for best path selection. Selected route is reserved at destination by using RREP messages. Probabilistic Approaches In [8], routing approach introduced is based on probabilistic estimation of available capacity of every CR link. Routing metric is probability based where metric is defined through probability distribution of PU-to-SU interference at given SU over assigned channel. This routing metric determines most probable path to satisfy given bandwidth D in network of N nodes that operates on maximum M frequency bands of respective bandwidths W1, W2,. . ., WM. Probability that channel i can satisfy bandwidth demand D is given as, Pr[C (i) ≥ D] = Pr [PIj(i) ≤ (Prj(i)/(2D/Wi - 1)) – N0] Where, PIj(i) = Total PU-to-SU interference at SUj over channel i and i=1, 2,…, M and j=1,2,…N A source-based routing protocol is used for path selection. Link state advertisement phase allows source to compute most probable path to the destination. Thus in [20], with probabilistic approach node first looks for available channels and then selects efficient channel based on the history. CONCLUSIONS Here two approaches are discussed for finding solutions of routing problems, first approach makes use of all fully available topological information of neighbor nodes and spectrum occupancy and second approach uses local network state information for routing. Several research works is done on these two approaches that focuses on different routing problems. Discussion on these approaches provides open view towards the routing challenges in cognitive radio network. Different routing measurements are provided to cognitive radio network to compute and analyze performance of proposed routing solutions. It starts research on multi-hop cognitive radio network including cognitive wireless mesh network and cognitive radio Ad-Hoc networks. 280 Kanchan Hadawale & Sunita Barve ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my Prof. Sunita Barve, MIT Academy of Engineering, India. REFERENCES 1. I.F.Akyildiz, W.-Y. Lee, M.C. Vuran, S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey”, Computer Networks 50 (13) (2006) 2127–2159. 2. C. Xin, L. Ma, C.-C. Shen,“A path-centric channel assignment framework for cognitive radio wireless networks”, Mobile Networks and Applications 13 (5) (2008) 463–476. 3. Y. Hou, Y. Shi, H. Sherali, “Optimal spectrum sharing for multi-hop software defined radio networks”, in: 26th IEEE International Conference on Computer Communications, INFOCOM 2007, pp. 1–9. 4. C.W. Pyo, M. Hasegawa, “Minimum weight routing based on a common link control radio for cognitive wireless ad hoc networks”, in: IWCMC ‟07: Proceedings of the 2007 International Conference on Wireless Communications and Mobile Computing, 2007, pp. 399–404. 5. H. Ma, L. Zheng, X. Ma, Y. luo, “Spectrum aware routing for multi-hop cognitive radio networks with a single transceiver”, in: 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008, pp. 1–6. 6. I. Pefkianakis, S. Wong, S. Lu, “SAMER: spectrum aware mesh routing in cognitive radio networks”, in: 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2008, pp. 1–5. 7. A. Sampath, L. Yang, L. Cao, H. Zheng, B.Y. Zhao, “High throughput spectrum-aware routing for cognitive radio based ad-hoc networks”, in: 3th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrwownCom 2008. 8. H. Khalife, S. Ahuja, N. Malouch, M. Krunz, “Probabilistic path selection in opportunistic cognitive radio networks”, in: IEEE Global Telecommunications Conference, GLOBECOM 2008, 2008, pp. 1–5.