Cooperation Reliability Based on Reinforcement Learning for Cognitive Radio Networks

N. Vucevic, I. Akyildiz, J. Pérez-Romero
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引用次数: 9

Abstract

The primary objective of cooperation in Cognitive Radio (CR) networks is to increase the efficiency and improve the network performance. However, CR users may act destructively and decrease both their own and others'' performances. This can be due to Byzantine adversaries or unintentional erroneous conduct in cooperation. This work presents an autonomous cooperation solution for each CR user, i.e., each CR user decides with whom to cooperate. The objective of the proposed solution is to increase the spectrum access in cooperative CR networks. To realize this, a Reinforcement Learning (RL) algorithm is utilized to determine the suitability of the available cooperators and select the appropriate set of cooperators. In addition, the proposed so-lution determines the most appropriate number of cooperators to achieve the highest efficiency for spectrum access. Accordingly, the control communication overhead is reduced. The simulation results demonstrate the learning capabilities of the proposed to achieve reliable behavior under highly unreliable conditions.
基于强化学习的认知无线电网络合作可靠性研究
认知无线电(CR)网络协作的主要目标是提高效率和改善网络性能。然而,CR用户可能会采取破坏性的行为,降低自己和他人的绩效。这可能是由于拜占庭对手或合作中无意的错误行为。本文提出了每个CR用户的自主协作解决方案,即每个CR用户决定与谁合作。提出的解决方案的目标是增加合作CR网络的频谱接入。为了实现这一点,利用强化学习(RL)算法来确定可用合作者的适用性,并选择合适的合作者集。此外,该方案确定了最合适的合作伙伴数量,以实现频谱接入的最高效率。相应地,减少了控制通信开销。仿真结果证明了该算法在高度不可靠条件下的学习能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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