Deterministic estimator learning automata-based neighbor discovery schemes for D2D networks with directional antennas

Abstract

This paper proposed two neighbor discovery schemes in the wireless D2D networks using directional antennas. The neighbor discovery process is modeled as a deterministic estimator learning automaton. Pursuit algorithm and Generalized Pursuit algorithm are used to further improve the efficiency of neighbor discovery. The nodes in the network adjust the direction of the directional antennas through learning the feedback given by the environment in the history discovery process. Finally, OPNET is used to simulate the proposed schemes, and the simulation results show that the proposed two schemes in this paper can effectively improve the efficiency of neighbor discovery.