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Distributed Approach for Power and Rate Allocation to Secondary Users in Cognitive Radio Networks

机译:认知无线电网络中向次用户分配功率和费率的分布式方法

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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.
机译:我们考虑一个多信道认知无线电网络,其中多个辅助用户(SU)共享一个信道,并且单个SU同时使用多个信道来满足其速率要求。我们尝试评估每个SU维持其服务质量(QoS)必须做出的最佳功率和速率分配选择。我们针对QoS的措施包括误码率(BER)和最低速率要求。我们设计了一个QoS约束的双目标优化问题,其目标是最小化发射功率和最大化速率。与先前的努力不同,我们将BER约束转换为凸约束,以保证所得解决方案的最优性。我们在本文中的兴趣是开发一种基于用户的分布式方法来解决优化问题,并将解决方案与集中式方法进行比较。我们采用对偶分解理论来推导分布式问题的三种不同形式。仿真结果表明,最佳发射功率遵循“反向注水”过程,速率分配遵循信号干扰加噪声比(SINR)。我们表明,来自分布式公式的解决方案遵循集中式解决方案。

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