Decentralized Reinforcement Learning Based Anti-Jamming Communication for Self-Organizing Networks

This paper investigates the problem of decentralized spectrum sharing in self-organizing networks against a dynamic and unknown jamming environment using reinforcement learning. In the network, the anti-jamming spectrum sharing has to not only coordinate spectrum access of users, but also combat the malicious jamming. However, most existing anti-jamming approaches are centralized and require information exchange, which are not suitable for decentralized self-organizing networks in the jamming environment. We formulate the multiuser anti-jamming channel selection problem as a Markov game, and propose a decentralized deep reinforcement learning based collaborative anti-jamming algorithm to achieve the equilibrium solution. It is shown in the simulation part that without information exchange, the approach enables multiple users to independently explore the spectrum environment and obtain effective (close to optimal) collaborative anti-jamming strategies against unknown and dynamic jamming.