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Blind channel selection strategies for distributed cognitive MAC

机译：分布式认知MAC的盲信道选择策略

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摘要

In spectrum overlay cognitive wireless networks, when the secondary users are blind, i.e. have no prior knowledge of primary users' activities, a major question remains as to what strategy to adopt to learn the primary state information online while sensing the spectrum for exploiting idle bands. To maximize secondary network spectral utilization while minimizing interference and collision, requires searching for a balance between choosing empirically best channel while investigating other channels for potential opportunities. Moreover, competition should also be resolved to prevent congestion and collision among secondary users who tend to access the same idle channel. In this paper, by compressing the inessential exploration phase, we first propose an asymptotically optimal, fast-converging channel selection algorithm referred to as modified-myopic strategy for the single-user scenario based on the result of multi-armed bandit. Through analysis and simulation modeling, we evaluate the performance of our approach and compare it to that of other strategies in the literature. Next we adopt a game-theoretic model and extend our algorithm to design a fair and low-complexity access strategy for multi-user case based on a generalized CSMA/CA scheme. Finally, we show the improved efficiency of the proposed approach, in particular significantly enhanced for dense networks.
机译：在频谱覆盖认知无线网络中，当次要用户是盲人，即不具备主要用户活动的先验知识时，主要的问题仍然是在感知频谱以利用空闲频带时，采用何种策略在线学习主要状态信息。为了在最大限度地减少干扰和冲突的同时最大程度地提高辅助网络频谱利用率，需要在选择经验最佳的信道与调查其他信道的潜在机会之间寻求平衡。此外，还应解决竞争问题，以防止倾向于访问同一空闲信道的辅助用户之间的拥塞和冲突。在本文中，通过压缩非必要探索阶段，我们首先基于多臂匪徒的结果，提出了一种针对单用户场景的渐近最优，快速收敛的信道选择算法，称为修正近视策略。通过分析和仿真建模，我们评估了该方法的性能，并将其与文献中的其他策略进行了比较。接下来，我们采用博弈论模型，并扩展我们的算法，以基于通用CSMA / CA方案设计用于多用户案例的公平且低复杂度的访问策略。最后，我们展示了所提出方法的效率提高，特别是对于密集网络显着增强。

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