Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing

TL;DR

This paper introduces a mean field multi-agent reinforcement learning approach for distributed bandwidth negotiation among massive wireless devices, achieving high spectrum utilization with minimal signaling overhead.

Contribution

It proposes a novel MF-MARL based bandwidth negotiation mechanism that enables scalable, distributed spectrum sharing with reduced signaling overhead.

Findings

Spectrum utilization rate exceeds 95% in simulations

Reduces spectrum conflicts among devices

Enables scalable decision-making for thousands of devices

Abstract

In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal spectrum utilization is achieved through distributed decision-making. Since only sparse feedback is needed, the proposed mechanism can greatly reduce the signaling overhead. In order to solve the distributed optimization problem when massive devices coexist, mean field multi-agent reinforcement learning (MF-MARL) based bandwidth decision algorithm is proposed, which allow device make globally optimal decision leveraging only neighborhood observation. In simulation, distributed bandwidth negotiation between 1000 devices is demonstrated and the spectrum utilization rate is above 95%. The proposed method is beneficial to reduce spectrum conflicts, increase spectrum utilization for massive devices spectrum sharing.