- Information Theory (Mathematics), Cognitive Radio Networks, Delay Tolerant Networks, Decision And Game Theory, Adaptive Control, Radio Channel Modelling, and 45 moreChannel Coding, Probabilistic Markov Modeling, Resource Allocation, Cross-layer optimizations, Adaptive packet transmissions, Packet scheduling, Network collaboration, Multi-network coexistence, Resource sharing, Interference management, Queueing theory, Markov Decision Process, Multiple-input Multiple-output (MIMO) Systems, Hybrid Automatic Repeat Request (HARQ), Adaptive Modulation and Coding (AMC), Optimization techniques, Linear programming (Optimization Techniques), Convex Optimization, Non-convex optimization, Dynamic programming, 3GPP (UMTS), IEEE 802.16 (WiMAX), Statistical Signal Processing, Wireless Communications, Digital Signal Processing, Reinforcement Learning, Mobile Communication Engineering, Smart Antenna, Mixed-integer Nonlinear Programming, Estimation and Filtering Theory, Multi Carrier Communication, UMTS 3G SYSTEMS, Wireless Sensor Networks, Mathematical Programming, Nonlinear Programming, Orthogonal Frequency Division Multiple Access (OFDMA), Wide-band Code Division Multiple Access (WCDMA), Communications/DSP, Multi Antenna Multiple Access Techniques, Dynamic Spectrum Access, Mixed-integer Linear Programming, Mobile Communication, Radio Resource Management, Linear Programming, and Game Theoryedit
- I am currently a Ryerson University Research Fellow in the Electrical and Computer Engineering department at Ryerson ... moreI am currently a Ryerson University Research Fellow in the Electrical and Computer Engineering department at Ryerson University, Toronto, ON, Canada. I am working with Prof. Alagan Anpalagan. I am working on spectrum access and scheduling techniques for cognitive radio networks. I was a Research Associate at the University of Waterloo working on different project related to network cooperation, interference management, resource sharing, schduling and adaptation issues of different wireless networks. Cognitive radio, network collaboration, vehicle-to-vehicle and vehicle-to-infrastructure communications and femtocell networks were of particular interest. I also worked on fault localization issues on all optical WDM networks.
I obtained my Ph.D. in the Electrical and Computer Engineering from the University of British Columbia, Vancouver, BC, Canada in August 2007. I was a Ph.D. student and a Post-doctoral Fellow at the Information Theory and Systems (ITS) Laboratory under Prof. Vijay K. Bhargava. With Prof. Vijay, I worked on the cross-layer adaptation, scheduling and resource allocation techniques for different wireless, cognitive radio and sensor networks.
I worked as a Research Assistant with Prof. Bhargava in the Canadian Institute of Telecommunications Research (CITR) lab at the University of Victoria from Sep. 2002 to Aug. 2003, and then in the Information Theory and Systems lab from Sep. 2003 to Aug. 2007. I also worked with Prof. Bhargava as a Postdoctoral Fellow from Sep. 2007 to Aug. 2008.
I worked as a lecturer in the University of British Columbia (UBC) and the Bangladesh University of Engineering and Technology (BUET). I taught different electrical engineering undergraduate courses in both universities. I also taught several courses in the Ahsanullah University of Science and Technology (AUST).edit
ABSTRACT In this paper, we investigate the optimal allocation of the power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying techniques. The power allocation in DF relaying for green cognitive... more
ABSTRACT In this paper, we investigate the optimal allocation of the power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying techniques. The power allocation in DF relaying for green cognitive radio with objective of maximizing energy efficiency is a constraint nonlinear nonconvex fractional programming (CNNFP) problem. We present the optimal power allocation in DF relaying by transforming the CNNFP power allocation problem into a concave fractional program by using Charnes-Cooper transformation. We also present an iterative ε-optimal solution for the CNNFP problem using Dinkelbach algorithm. The convergence of the iterative algorithm is proved and numerical solutions obtained using simulations for DF cooperative communications are presented.
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ABSTRACT In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio network is investigated as a constrained fractional programming problem (CFPP). The fractional objective is... more
ABSTRACT In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio network is investigated as a constrained fractional programming problem (CFPP). The fractional objective is defined in terms of bits per Joule per Hz. The proposed CFPP is a nonlinear non-convex optimization problem. We propose an iterative power algorithm (IPA) that guarantees ε-optimal solution. The convergence of IPA is proved and numerical solutions obtained using simulations are presented.
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With the continual increase of dependence on electrical systems in our daily lives, the consumption of electrical energy is increasing at a rapid pace. The demand and the price of electrical energy vary according to time of the... more
With the continual increase of dependence on electrical systems in our daily lives, the consumption of electrical energy is increasing at a rapid pace. The demand and the price of electrical energy vary according to time of the day/month/year in order to lower the production cost and stabilize generation systems. The information and communication technology (ICT) industry sector is itself responsible for a significant portion of total global CO2 emission and global warming. According to a recent study, ICT accounts for about 6 ...
ABSTRACT We propose a novel opportunistic access scheme for cognitive radios in an interweave cognitive system, that considers the channel gain as well as the predicted idle channel probability (primary user occupancy: Busy/idle). In... more
ABSTRACT We propose a novel opportunistic access scheme for cognitive radios in an interweave cognitive system, that considers the channel gain as well as the predicted idle channel probability (primary user occupancy: Busy/idle). In contrast to previous work where a cognitive user vacates a channel only when that channel becomes busy, the proposed scheme requires the cognitive user to switch to the channel with the next highest idle probability if the current channel's gain is below a certain threshold. We derive the threshold values that maximize the long term throughput for various primary user transition probabilities and cognitive user's relative movement.
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ABSTRACT We investigate the optimal allocation of power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying technique. The power allocation in DF relaying for green cooperative cognitive radio with... more
ABSTRACT We investigate the optimal allocation of power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying technique. The power allocation in DF relaying for green cooperative cognitive radio with an objective of maximising energy-efficiency is a constraint non-linear non-convex fractional programming problem. The optimisation needs to satisfy the primary users interference constraints and secondary users outage constraints. The authors present the optimal power allocation in DF relaying by transforming the constraint non-linear non-convex fractional power allocation problem into a concave fractional programme by using Charnes-Cooper transformation. The authors also present an iterative algorithm that uses parametric transformation and guarantees ε-optimal convergence. The convergence of the iterative algorithm is proved and numerical results obtained for cooperative cognitive radio network are presented with different network parameter settings.
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ABSTRACT In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy-efficient fractional... more
ABSTRACT In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy-efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non-linear non-convex optimisation problem. The authors first transform the energy-efficient maximisation problem into a parametric optimisation problem and then propose an iterative power allocation algorithm that guarantees ε-optimal solution. A proof of convergence is also given for the ε-optimal algorithm. The proposed ε-optimal algorithm provide a practical solution for power allocation in energy-efficient cognitive radio networks. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithms are investigated.
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We study the energy-efficient power allocation techniques for OFDM-based cognitive radio (CR) networks, where a CR transmitter is communicating with CR receivers on a channel borrowed from licensed primary users (PUs). Due to... more
We study the energy-efficient power allocation techniques for OFDM-based cognitive radio (CR) networks, where a CR transmitter is communicating with CR receivers on a channel borrowed from licensed primary users (PUs). Due to non-orthogonality of the transmitted signals in the adjacent bands, both the PU and the cognitive secondary user (SU) cause mutual-interference. We assume that the statistical channel state information between the cognitive transmitter and the primary receiver is known. The secondary transmitter maintains a specified statistical mutual-interference limits for all the PUs communicating in the adjacent channels. Our goal is to allocate subcarrier power for the SU so that the energy efficiency metric is optimized as well as the mutual-interference on all the active PU bands are below specified bounds. We show that the green power loading problem is a fractional programming problem. We use Charnes-Cooper transformation technique to obtain an equivalent concave optimization problem for what the solution can be readily obtained. We also propose iterative Dinkelbach method using parametric objective function for the fractional program. Numerical results are given to show the effect of different interference parameters, rate and power thresholds, and number of PUs.
Packet scheduling is very important in future wireless networks due to their limited resources and increasing demand for high data rates. Time-varying incoming traffic and channel gains make the scheduling decision very challenging. Due... more
Packet scheduling is very important in future wireless networks due to their limited resources and increasing demand for high data rates. Time-varying incoming traffic and channel gains make the scheduling decision very challenging. Due to the inherent dynamic nature of packet scheduling, we formulate the scheduling problem as a Markov decision process. We consider a single user communicating over a correlated fading channel. The incoming traffic is randomly varying and stored in the finite buffer before transmission. We formulate the ...
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Abstract We present a new and simple unconstrained optimization based power control algorithm for interference-limited Rayleigh fading wireless networks. We consider optimizing two objective functions, namely, minimizing outage and... more
Abstract We present a new and simple unconstrained optimization based power control algorithm for interference-limited Rayleigh fading wireless networks. We consider optimizing two objective functions, namely, minimizing outage and maximizing utility under bounds on transmission powers. With some transformation techniques, we show that the formulated constrained optimization problem can be converted into an equivalent unconstrained problem.
Abstract We address the issue of optimal packet scheduling over correlated fading channels which trades off between minimization of three goals: average transmission power, average delay and average packet dropping probability. We show... more
Abstract We address the issue of optimal packet scheduling over correlated fading channels which trades off between minimization of three goals: average transmission power, average delay and average packet dropping probability. We show that the problem forms a weakly communicating Markov decision process and formulate the problem as both unconstrained and constrained problem. Relative value iteration (RVI) algorithm is used to find optimal deterministic policy for unconstrained problem, while optimal randomized policy for ...
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Research Interests: Distributed Computing, Wireless Systems, Channel Coding, Packet scheduling, Hybrid Automatic Repeat Request (HARQ), and 16 moreFading Channel, Hidden Markov Models, Maximum Likelihood, hidden Markov model, Fading, Error Correction, Rate Adaptation, Throughput, Real Time, Forward Error Correction, TRANSMITTERS, Signal to Noise Ratio, Electrical And Electronic Engineering, Hybrid ARQ, Time varying, and Fading channels
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Abstract This paper explores optimal and suboptimal packet schedulers for time-varying flat fading channels that trade-off between minimization of the average delay and the average transmitted power. Both uncorrelated and correlated block... more
Abstract This paper explores optimal and suboptimal packet schedulers for time-varying flat fading channels that trade-off between minimization of the average delay and the average transmitted power. Both uncorrelated and correlated block fading channels are investigated. Extending a previous work, we formulate the trade-off as a unconstrained Markov decision processes and find the stationary deterministic optimal policy using both relative value iteration and policy iteration algorithm. As well, we present constrained Markov decision ...
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Abstract We present a cross-layer optimization problem where the coding rate of hybrid ARQ systems is adapted with channel, buffer, and input traffic state to minimize packet errors as well as buffer delay. Representing both the incoming... more
Abstract We present a cross-layer optimization problem where the coding rate of hybrid ARQ systems is adapted with channel, buffer, and input traffic state to minimize packet errors as well as buffer delay. Representing both the incoming traffic and the time-varying wireless channel as a finite state Markov chain, it is shown that the problem forms a partially observable Markov decision process (POMDP) problem. Since finding optimal policy is PSPACE complete, we investigate two policy-heuristic approaches for the purpose of ...
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We propose adaptive variable-rate constant-power scheme for ad hoc wireless networks, employing the modification of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. Potential improvements in throughput and... more
We propose adaptive variable-rate constant-power scheme for ad hoc wireless networks, employing the modification of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. Potential improvements in throughput and back-off probability are presented for different system parameters.
Abstract We study two cross-layer optimization problems for M-QAM systems that adapt transmission rate with channel state and buffer occupancy. We formulate both problems as constrained Markov decision process problem and give linear... more
Abstract We study two cross-layer optimization problems for M-QAM systems that adapt transmission rate with channel state and buffer occupancy. We formulate both problems as constrained Markov decision process problem and give linear programming technique based solutions. In first problem, our objective is to minimize average transmission power under constraint on average delay and packet dropping probability. We minimize average bit error rate (BER) with average delay and packet dropping probability constraints in ...
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Abstract Adaptive modulation and antenna diversity are two important enabling techniques for future wireless network to meet demand for high data rate transmission. We study a Markov decision process based cross-layer design of optimal... more
Abstract Adaptive modulation and antenna diversity are two important enabling techniques for future wireless network to meet demand for high data rate transmission. We study a Markov decision process based cross-layer design of optimal adaptation policy over selection-combining Nakagami-m fading channel for Markov modulated Poisson process (MMPP) traffic. Unlike most of the channel-dependent adaptation policy in the literature, proposed policy chooses modulation constellation dynamically depending on the traffic ...
Abstract We study coding and modulation rate adaptation problem for HARQ systems with partially observable state from cross-layer viewpoint. The rate of convolutionally coded M-QAM is adapted jointly with buffer state and channel state.... more
Abstract We study coding and modulation rate adaptation problem for HARQ systems with partially observable state from cross-layer viewpoint. The rate of convolutionally coded M-QAM is adapted jointly with buffer state and channel state. We assume that perfect channel state information is not known at the transmitter, but it can be estimated from previous actions and observations. POMDP-based approach is utilized to formulate the problem, where average throughput is maximized, and average delay, packet error rate and ...