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Efficient Spectrum Sensing for Cognitive Radio Networks via Joint Optimization of Sensing Threshold and Duration

机译:通过感知阈值和持续时间的联合优化,对认知无线电网络进行有效频谱感知

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摘要

Cognitive radio networks require fast and reliable spectrum sensing to achieve high network utilization by secondary users. Optimization approaches to spectrum sensing to-date have largely focused on maximizing throughput for secondary users while considering only a single parameter variable pertinent to sensing - notably the threshold or duration, but not both. In this work, we investigate the impact of true joint minimization under two performance criteria: a) minimization of the average time to detection of a spectrum hole and b) joint maximization of the aggregate opportunistic throughput. We show that the resulting non-convex problem is actually biconvex under practical conditions for which effective algorithms can be developed that yields reliable numerical procedures to solve the resulting optimization problem. The results show that the proposed approach can considerably improve system performance (in terms of the mean time to detect a spectrum hole and also the aggregate opportunistic throughput of both primary and secondary users), relative to the scenarios with only a single sensing variable or a sub-optimal ad-hoc optimization approach used for two variable case.
机译:认知无线电网络需要快速可靠的频谱感知,以实现次要用户的高网络利用率。迄今为止,频谱感测的优化方法主要集中在使次要用户的吞吐量最大化上,同时仅考虑与感测有关的单个参数变量-特别是阈值或持续时间,而不是两者。在这项工作中,我们在两个性能标准下研究了真正的联合最小化的影响:a)最小化检测频谱孔的平均时间; b)联合最大机会吞吐率。我们表明,在实际条件下,所得的非凸问题实际上是双凸的,为此可以开发有效的算法,从而产生可靠的数值程序来解决所得的优化问题。结果表明,相对于仅具有单个感测变量或单个变量的情况,所提出的方法可以显着改善系统性能(就检测频谱空洞的平均时间以及主要和次要用户的总机会吞吐量而言)。次优临时优化方法用于两个变量的情况。

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