Optimal Power Allocation for Underlay-Based Cognitive Radio Networks With Primary User's Statistical Delay QoS Provisioning
Optimal Power Allocation for Underlay-Based Cognitive Radio Networks With Primary User's Statistical Delay QoS Provisioning
复制标题
DOI：
10.1109/twc.2015.2462816
复制
发表时间：
2015-07
影响因子：
10.4
通讯作者：
Yichen Wang;Pinyi Ren;Qinghe Du;Li Sun
中科院分区：
文献类型：
--
作者：
Yichen Wang;Pinyi Ren;Qinghe Du;Li Sun
由于无线信道的高度随机性，如何在优化次用户性能的同时，为主用户提供有效的时延服务质量（QoS）保障是认知无线电网络（CRN）的一个重要任务。为了解决上述问题，我们研究了基于底层的CRN与PU的统计延迟QoS保护的最优功率分配策略。我们的目标是满足PU的统计延迟QoS要求，而不是利用广泛使用的干扰功率约束来保护PU的传输，其特征在于的长度边界违反概率。通过应用有效容量理论，我们进一步转换PU的长度边界违反概率约束等效的最大可持续业务负载要求。然后，我们制定的优化问题，以最大限度地提高SU的平均吞吐量，同时满足PU的统计延迟QoS的要求，以及SU的平均和峰值发射功率的约束，这可以被证明是一个非凸问题。利用凸船体理论和概率传输理论，将原非凸问题转化为等价的严格凸问题，得到了同时适应PU时延QoS要求和信道条件的最优功率分配策略。此外，我们还开发了一个固定的功率分配方案，只调整与PU的延迟QoS要求进行比较。仿真结果表明，无论是最佳的和固定的计划可以灵活地分配上限的发送功率预算根据PU的延迟QoS要求，但建议的最佳功率分配策略也可以有效地利用无线信道的时变特性，从而显着优于固定的功率分配方案。
Due to the highly-stochastic nature of wireless channels, how to provide efficient delay quality-of-service (QoS) provisioning for primary users (PU) while optimizing the performance of secondary users (SU) is a critically important task for cognitive radio networks (CRN). To address the above issue, we investigate the optimal power allocation strategy for underlay-based CRN with PU's statistical delay QoS protection. Instead of utilizing the widely-used interference power constraint to protect PU's transmission, we aim at satisfying PU's statistical delay QoS requirement characterized by the queue-length bound violation probability. By applying the theory of effective capacity, we further convert PU's queue-length bound violation probability constraint to the equivalent maximum sustainable traffic load requirement. Then, we formulate the optimization problem to maximize SU's average throughput while meeting PU's statistical delay QoS requirement as well as SU's average and peak transmit power constraints, which can be proved as a nonconvex problem. By employing the theories of convex hull and probabilistic transmission, we convert the original nonconvex problem to the equivalent strictly convex problem and then obtain the optimal power allocation strategy, which adapts to both PU's delay QoS requirements and channel conditions. Moreover, we also develop for comparison a fixed power allocation scheme that only adjusts with PU's delay QoS requirements. Simulation results are provided which demonstrate that both the optimal and fixed schemes can flexibly allocate the upperbounded transmit power budget according to PU's delay QoS requirements, but the proposed optimal power allocation strategy can also efficiently exploit the time-varying nature of wireless channels and thus significantly outperforms the fixed power allocation scheme.