Leveraging MAB for Efficient Neighbor Discovery with Directional Antennas in Wireless Networks

Directional antennas, with their ability to enhance signal strength and mitigate interference, are integral to modern wireless communication, especially in ground-air-space networks. However, the precise alignment requirement of directional antennas in communication pose challenges for efficient neighbor discovery (ND). Aiming at optimizing discovery time by dynamically adjusting sector selection probabilities, a novel Multi-Armed Bandit (MAB)-based algorithm is proposed in this paper. By analyzing the feedback that can accelerate the ND time and the theory of MAB, an important conclusion is drawn that MAB is only applicable to non-uniform distributions where there are significant differences in neighbor distributions. Based on analysis, an appropriate MAB-based ND algorithm is designed. Numerical simulations have demonstrated the correctness of the analysis and the performance advantages of the proposed algorithm both in uniform and non-uniform topologies.