基于强化学习和共识融合的分布式协作频谱感知方法

在频谱感知中为了解决不同信誉用户网络节点之间的数据融合问题,提出了一种基于强化学习和共识融合的分布式协作频谱感知方法。该方法将每个感知用户认为是一个智能体(agent),agent通过强化学习算法从相邻节点选择合作用户进行共识融合,采用信誉值作为奖励,确保agent倾向于信誉高的节点进行融合,并同时降低恶意用户的信誉值,使其逐渐退出感知网络,最后采用一致性融合方法使整个网络达成共识,并与判决门限对比,完成协作频谱感知。仿真实验表明,该方法能够有效的识别恶意用户,并通过强化学习提高整个网络的感知性能,使协作频谱感知网络更具智能性和稳定性。

Distributed cooperative spectrum sensing method based on reinforcement learning and consensus fusion

In order to solve the problem of data fusion between network nodes of users with different reputation in spectrum sensing, a distributed cooperative spectrum sensing method based on reinforcement learning and consensus fusion is proposed. This method regards each perceived user as an agent, and the agent uses the reinforcement learning algorithm to select a cooperative user from adjacent nodes for consensus fusion. The reputation value is used as reward to ensure that agent tends to merge with high reputation nodes. At the same time, the reputation value of malicious users is reduced, causing that they gradually drop out of the cognitive wireless network. Finally, the conformance fusion method is adopted to make the whole network reach consensus and compare with the decision threshold to complete the cooperative spectrum sensing. The simulation results show that this method can identify malicious users effectively, and enhance the perception performance of the whole cognitive wireless network through reinforcement learning. As a result the cooperative spectrum sensing network is more intelligent and stable.