Ebrahim Bedeer

A Multiobjective Optimization Approach for Optimal Link Adaptation of OFDM-Based Cognitive Radio Systems with Imperfect Spectrum Sensing

This paper adopts a multiobjective optimization (MOOP) approach to investigate the optimal link adaptation problem of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, where secondary users (SUs) can opportunistically access the spectrum of primary users (PUs). For such a scenario, we solve the problem of jointly maximizing the CR system throughput and minimizing its transmit power, subject to constraints on both SU and PUs. The optimization problem imposes predefined interference thresholds for the PUs, guarantees the SU quality of service in terms of a maximum bit-error-rate (BER), and satisfies a transmit power budget and a maximum number of allocated bits per subcarrier. Unlike most of the work in the literature that considers perfect SU spectrum sensing capabilities, the problem formulation takes into account errors due to imperfect sensing of the PUs bands. Closed-form expressions are obtained for the optimal bit and power allocations per SU subcarrier. Simulation results illustrate the performance of the proposed algorithm and demonstrate the superiority of the MOOP approach when compared to single optimization approaches presented in the literature, without additional complexity. Furthermore, results show that the interference thresholds at the PUs receivers can be severely exceeded due to the perfect spectrum sensing assumption or due to partial channel information on links between the SU and the PUs receivers. Additionally, the results show that the performance of the proposed algorithm approaches that of an exhaustive search for the discrete optimal allocations with a significantly reduced computational effort.

Joint Optimization of Bit and Power Loading for Multicarrier Systems

In this letter, a novel low complexity bit and power loading algorithm is formulated for multicarrier communication systems. The proposed algorithm jointly maximizes the throughput and minimizes the transmit power through a weighting coefficient α, while meeting constraints on the target bit error rate (BER) per subcarrier and on the total transmit power. The optimization problem is solved by the Lagrangian multiplier method if the initial α causes the transmit power not to violate the power constraint; otherwise, a bisection search is used to find the appropriate α. Closed-form expressions are derived for the close-to-optimal bit and power allocations per subcarrier, average throughput, and average transmit power. Simulation results illustrate the performance of the proposed algorithm and demonstrate its superiority with respect to existing allocation algorithms. Furthermore, the results show that the performance of the proposed algorithm approaches that of the exhaustive search for the discrete optimal allocations.

Fairness-Aware Energy-Efficient Resource Allocation for AF Co-Operative OFDMA Networks

In this paper, we adopt an energy-efficiency (EE) metric, named worst-EE, that is suitable for EE fairness optimization in the uplink transmission of amplify-and-forward (AF) cooperative orthogonal frequency division multiple access (OFDMA) networks. More specifically, we assign subcarriers and allocate powers for mobile and relay stations in order to maximize the worst-EE, i.e., to maximize the EE of the mobile station (MS) with the lowest EE value, subject to MSs transmit power, relay station (RS) transmit power, and MSs quality-of-service (QoS) constraints. The formulated primal max-min optimization problem is nonconvex fractional mixed integer nonlinear program, i.e., NP-hard to solve. We provide a novel optimization framework that studies the structure of the primal problem and prove that the dual min-max optimization problem attains the same optimal solution of the primal problem. Additionally, we propose a modified Dinkelbach algorithm, named dual Dinkelbach, to achieve the optimal solution of the dual problem in a polynomial time complexity. We further exploit the structure of the obtained optimal solution and develop a low complexity suboptimal heuristic. Numerical results show the effectiveness of the proposed algorithm to improve the network performance in terms of fairness between MSs, worst-EE, and average network transmission rate when compared to traditional schemes that maximize the EE of the whole network. Presented results also show that the suboptimal heuristic balances the achieved performance and the computational complexity.

Optimal bit and power loading for OFDM systems with average BER and total power constraints

In this paper, a novel joint bit and power loading algorithm is proposed for orthogonal frequency division multiplexing (OFDM) systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target average bit error rate (BER) and meeting a constraint on the total transmit power. Simulation results are described that illustrate the performance of the proposed scheme and demonstrate its superiority when compared to the algorithm in [1].

Energy efficiency and spectral efficiency trade-off for OFDM systems with imperfect channel estimation

In this paper, the power loading problem for orthogonal frequency division multiplexing (OFDM) with imperfect channel estimation is investigated considering the trade-off between energy efficiency (EE) and spectral efficiency (SE). Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or the achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and the SE functions or change the priority level of each function using a trade-off parameter. Our dynamic approach is more tractable than conventional approaches and more convenient to realistic communication applications and scenarios. The system model considers the path loss and shadowing effect in modeling the EE and SE metrics, in addition to taking the channel estimation error into account. We first solve the marginal problems of maximizing the EE and the SE individuality, and then prove that the multiobjective optimization of the EE and the SE is equivalent to a simple problem that maximizes the capacity and minimizes the total power consumption. Finally, we use numerical results to discuss the choice of the trade-off parameter and study the effect of the estimation error, transmission power budget and channel-to-noise ratio on the multiobjective optimization.

A Novel Energy Efficient Scheme With a Finite-Rate Feedback Channel

In this letter, we propose an energy efficient communication scheme with a limited feedback rate for Rayleigh fading channels. First, we employ the available feedback words to quantize the channel-to-noise ratio (CNR) range and divide it into different regions. Then, we formulate the average energy efficiency (EE) for a given number of CNR regions using a fixed power and rate in each region. After that, we propose an offline optimization algorithm to compute the energy efficient CNR thresholds of each region and the associated transmission powers that maximize the EE. Finally, we evaluate the proposed scheme through simulations, by comparing the EE performance and effective achievable capacity against the case of perfect-CSI.

Joint Optimization of Bit and Power Allocation for Multicarrier Systems with Average BER Constraint

This paper proposes a novel joint bit and power allocation algorithm for multicarrier systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target average bit error rate (BER). Simulation results are described and they illustrate the performance of the proposed scheme and demonstrate its superiority with respect to existing schemes.

Adaptive bit allocation for OFDM cognitive radio systems with imperfect channel estimation

Cognitive radios hold tremendous promise for increasing the spectral efficiency of wireless communication systems. In this paper, an adaptive bit allocation algorithm is presented for orthogonal frequency division multiplexing (OFDM) CR systems operating in a frequency selective fading environment. The algorithm maximizes the CR system throughput in the presence of narrowband interference, while guaranteeing a BER below a predefined threshold. The effect of imperfect channel estimation on the algorithms performance is also studied.

A Systematic Approach to Jointly Optimize Rate and Power Consumption for OFDM Systems

In this paper, we adopt a multiobjective optimization for the bit and power allocation problem in order to meet the requirements of emerging wireless systems, i.e., achieving higher throughput without considerably increasing the transmit power. More specifically, we propose to simultaneously maximize the throughput and minimize the transmit power of an OFDM system subject to average bit error rate (BER), power budget, and maximum allocated number of bits per subcarrier constraints. The formulated optimization problem is not convex and we use an evolutionary algorithm, i.e., genetic algorithm, in order to obtain the solution. We study the structure of the problem and the obtained solution and notice that the constraint on the average BER can be replaced by a BER per subcarrier constraint. As such, we propose an approximate non-convex optimization problem. We further exploit the structure of the approximate optimization problem and notice that the BER constraint per subcarrier (i.e., the source of the non-convexity) must be satisfied with an equality sign and can be substituted; this leads to an equivalent convex optimization problem where the global optimality of the Pareto solutions is guaranteed. Closed-form expressions are obtained for the bit and power allocations with reduced complexity. Simulation results show that the proposed multiobjective optimization approach provides significant performance improvements over single objective optimization techniques presented in the literature, without incurring additional complexity.

A novel algorithm for joint bit and power loading for OFDM systems with unknown interference

In this paper, a novel low complexity bit and power loading algorithm is formulated for orthogonal frequency division multiplexing (OFDM) systems operating in fading environments and in the presence of unknown interference. The proposed non-iterative algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target bit error rate (BER) per subcarrier. Closed-form expressions are derived for the optimal bit and power distributions per subcarrier. The performance of the proposed algorithm is investigated through extensive simulations. A performance comparison with the algorithm shows the superiority of the proposed algorithm with reduced computational effort.