Distributed Approach for Power and Rate Allocation to Secondary Users in Cognitive Radio Networks
ABSTRACT We consider a multichannel cognitive radio network where multiple secondary users (SUs) share a single channel and where multiple channels are simultaneously used by a single SU to satisfy their rate requirements. We attempt to evaluate the optimal power and rate distribution choices that each SU has to make to maintain their quality of service (QoS). Our measures for QoS include bit error rate (BER) and minimum rate requirement. We design a QoS-constrained bi-objective optimization problem with the objective of minimizing transmit power and maximizing rate. Unlike prior efforts, we transform the BER constraint into a convex constraint to guarantee optimality of the resulting solution. Our interest in this paper is to develop a user-based distributed approach to solve the optimization problem and compare the solution with the centralized approach. We employ dual decomposition theory to derive three different formulations of the distributed problem. Simulation results demonstrate that optimal transmit power follows the “reverse water filling” process and that rate allocation follows signal-to-interference-plus-noise ratio (SINR). We show that the solutions from the distributed formulations follow the centralized solution.
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- "power needs to be adaptively assigned to each subcarrier according to its subcarrier state variation . More powers and bits per symbol must be allocated to the subcarrier with higher channel fading gain, and less powers and bits per symbol to noisy subcarrier . Adaptive modulation and bit loading are efficient techniques for reliable transmission . "
ABSTRACT: This paper investigates an optimal adaptive rate and power transmission algorithms for Orthogonal Frequency Division Multiplexing (OFDM) – based Cognitive Radio (CR) systems. The aim was to study the problem of maximizing the overall rate achieved by the Secondary User (SU), while keeping the interference powers introduced by the SU on the spectrum band of Primary User’s (PU) below the specified thresholds and considering the total transmit power budget constraints. In addition, the novel suboptimal power allocation algorithm was proposed and consequently the maximum modulation level according to allocated power based on maximizing the overall achievable rate was obtained. The performance of the proposed suboptimal algorithm is compared with the optimal and existing algorithms including uniform loading and water filling algorithms. Numerical results revealed that the proposed suboptimal algorithm had a better performance than the uniform loading and water filling algorithms.Computers & Electrical Engineering 02/2015; 42. DOI:10.1016/j.compeleceng.2015.01.008 · 0.99 Impact Factor
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ABSTRACT: This article proposes a joint power control, rate adaptation and channel selection strategy for Cognitive Radio Networks (CRNs) operating over vacant TV bands, also known as TV Bands Devices (TVBDs). To exploit the abundance of vacant channels available at the disposal of these devices, we combine the well-studied power and rate adaptation strategies with adaptive channel selection. The combined strategy maximizes the achievable throughput while guaranteeing a desired level of performance, i.e., error and outage probabilities. It also reduces the chances of causing or being subject to harmful interference to/from other co-channel users. Furthermore, to waive the processing complexity resulting from the frequent channel switching, we propose an alternative strategy that switches the operating channel only when the operating channel fails to support the least supportable data rate. For the two strategies, we derive closed form expressions for the average data rates and transmission powers. The accuracy of the derived results as well as the performance gains achieved are verified using intensive numerical and simulations results.Global Communications Conference (GLOBECOM), 2012 IEEE; 01/2012
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ABSTRACT: In multi-channel cognitive radio networks, each secondary user can use multiple channels for data transmission to improve the spectrum utilization. In this paper, we investigate the resource allocation problem for multi-layered video streaming over multi-channel cognitive radio networks. The video is encoded into multiple layers, each of which is delivered over a separate channel. Moreover, we jointly optimize the source rate, the transmission rate, and the transmission power at each video session in each channel to provide Quality of Service (QoS) guarantee to all video sessions in the secondary network. The optimization problem is formulated into a Geometric Programming (GP) problem, which can be solved efficiently. We demonstrate in the simulations that the proposed optimal scheme can obtain a lower average Packet Loss Rate (PLR), thus leading to a higher video quality compared to the equal scheme.Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on; 01/2012