Jamming-resilient algorithm for underwater cognitive acoustic networks
Jamming-resilient algorithm for underwater cognitive acoustic networks
复制标题
水下认知声网络的抗干扰算法
DOI：
10.1177/1550147717726309
复制
发表时间：
2017
影响因子：
2.3
通讯作者：
Zhang Qunfei
中科院分区：
文献类型：
--
作者：
Wang Zixiang;Zhen Fan;Zhang Senlin;Liu Meiqin;Zhang Qunfei
由于水声网络中频谱资源有限，水下认知声通信是一种很有前途的技术。认知网络中的信道共享机制可以有效提高通信容量。干扰攻击是认知网络中常见的拒绝服务攻击。在水下认知声网络中，抗干扰问题与认知无线电网络有很大的不同。在认知声信道接入中，需要一种有效的抗干扰策略。本文提出了一种在线学习的抗干扰算法--基于多臂带的声信道接入算法，以实现抗干扰的认知声通信。在抗干扰博弈中，考虑了水声信道的非理想感知和水声通信的约束条件。在不同类型的干扰攻击下，我们的抗干扰方法可以提高信道利用率。
Due to the limit spectrum resource in the underwater acoustic networks, underwater cognitive acoustic communication is a promising technique. The channel sharing mechanism in cognitive networks can improve the communication capacity efficiently. Jamming attack is a common deny of service attack in cognitive networks. In the underwater cognitive acoustic networks, the anti-jamming problem is quite different from cognitive radio networks. It calls for an effective anti-jamming strategy in the cognitive acoustic channel access. In this article, we propose an online learning anti-jamming algorithm called multi-armed bandit–based acoustic channel access algorithm to achieve the jamming-resilient cognitive acoustic communication. The imperfect channel sensing and the constraints of underwater acoustic communication are considered in the anti-jamming game. Under different kinds of jamming attacks, the channel utilization can be improved with our jamming-resilient approach.