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.
Conference Paper: Analysis of various power allocation algorithms for wireless networks[Show abstract] [Hide abstract]
ABSTRACT: In wireless networks, power allocation is an effective technique for prolonging the lifetime of network terminals. Generally optimum power allocation improves the efficiency of wireless systems. When power allocation is properly done, source information can reach the destination efficiently. The problem of power allocation in relay assisted wireless system is investigated here with Maximal Ratio Combining (MRC) at the destination terminal. Based on the SNR and BER, power is allocated to the network terminals using water filling power allocation scheme. It is observed that water filling power allocation scheme allocates more power for the network terminals with less noise. Also based on the users channel capacity optimized power allocation is done. Hence the channel serves good enough in transmitting the source information to the destination.Communications and Signal Processing (ICCSP), 2012 International Conference on; 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
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ABSTRACT: In this paper, the resource allocation for OFDMA-based cognitive radio (CR) system is considered. We propose a two-step resource allocation algorithm that maximizes the sum throughput of the system while satisfying the quality of service (QoS) requirements for guaranteed users (GUs). By the first step, the sum power of the guaranteed users is minimized as the guaranteed rate is provided. By the second step, the remaining resources are distributed among all users so that the sum throughput is maximized. In both steps, power allocation is performed in a way that the adverse interference on the primary users (PUs) is avoided. Simulation results show that the proposed algorithm is highly efficient in terms of rate guarantee for GUs and sum throughput maximization.Telecommunications (IST), 2012 Sixth International Symposium on; 01/2012