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On co-channel and adjacent channel interference mitigation in cognitive radio networks

机译:关于认知无线电网络中同信道和邻信道干扰的缓解

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摘要

Cognitive radio (CR) is an emerging wireless communications paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR user themselves. In this paper, we consider the problem of interference mitigation via channel assignment and power allocation for CR users. A cross-layer optimization framework for minimizing both co-channel and adjacent channel interference is developed; the latter has been shown to have considerable impact in practical systems. Cooperative spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation-linearization technique (RLT) based centralized algorithm, as well as a distributed greedy algorithm that uses local information for near-optimal solutions. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.
机译:认知无线电(CR)是在许可(或主要)用户和非许可(或CR)用户之间共享频谱的新兴无线通信范例。在CR网络中,缓解干扰不仅对于主要用户保护至关重要,而且对于CR用户自身的服务质量也至关重要。在本文中,我们考虑了通过CR用户通过信道分配和功率分配来减轻干扰的问题。开发了一种用于最小化同频和邻频干扰的跨层优化框架;已证明后者在实际系统中具有相当大的影响。在问题表述中考虑了协作频谱感知,机会频谱访问,信道分配和功率分配。我们提出了一种基于重构线性化技术(RLT)的集中式算法,以及一种将局部信息用于近乎最佳解决方案的分布式贪婪算法。两种算法均通过仿真进行了评估,并且显示出对于缓解两种类型的干扰并实现高CR网络容量非常有效。

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