Reinforcement learning‐based negotiation for spectrum micro‐trading framework

Abstract

Spectrum trading has been permitted in most of the major wireless markets to facilitate better utilisation of spectrum. The authors have considered a spectrum trading framework in which a wireless service provider (WSP) leases its available channel(s) to another WSP for use at the designated base station (BS) of the latter for short duration. In their model, the agents of WSPs carry out the negotiation on the specifications of the channel usage such as transmission power, antenna height, spectrum band of the channel and price of the channel. In this work, they have modelled the negotiation as a multi-issue bilateral negotiation problem. Initially, they have solved the problem with the help of the Bayesian learning-based negotiation (BLBN) method. Furthermore, they have devised the novel reinforcement learning-based technique namely, reinforcement learning-based negotiation (RLBN) considering the adaptability of the BS to the new channel configuration. Surplus utility and convergence time of the negotiation process are considered as performance indices for the above techniques. The simulation results show that the RLBN outperforms BLBN and static negotiation technique as far as the objective of surplus utility is concerned.