Dašić Dejan, Ilić Nemanja, Vučetić Miljan, Perić Miroslav, Beko Marko, Stanković Miloš S
Artificial Intelligence Department, Vlatacom Institute, 11070 Belgrade, Serbia.
Faculty of Technical Sciences, Singidunum University, 11000 Belgrade, Serbia.
Sensors (Basel). 2021 Apr 23;21(9):2970. doi: 10.3390/s21092970.
In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks' practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm's characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.
在本文中，我们提出了一种基于共识的认知无线电网络中分布式频谱感知和信道选择的新算法。该算法在多智能体强化学习框架内运行。所提出的共识策略通过有向的、通常稀疏的、时变低带宽通信网络来实现，以完全分散和分布式的方式强制智能体之间进行协作。所提出方法的动机直接源于典型认知无线电网络的实际场景，在这些场景中，这种分散设置和分布式操作至关重要。具体而言，所提出的设置在未知的环境和应用条件下为所有智能体提供可行的全网络信息。因此，即使单个智能体无法做到，一组参与的智能体也能够成功计算出最优的联合频谱感知和信道选择策略。所提出的算法本质上具有可扩展性，并且对节点和链路故障具有鲁棒性。本文对算法的特性进行了详细讨论和分析，包括去噪效果、组织协调行动的可能性以及共识方案带来的收敛速度提升。大量仿真结果证明了所提出算法的高效性，并且即使在基于稀疏邻居的节点间通信情况下，其行为也接近集中式方案。
