Joint Power Allocation in Interference-Limited Networks via Distributed Coordinated Learning

TL;DR

This paper introduces a distributed multi-agent Q-learning approach for joint power allocation in dense 5G networks, effectively managing interference through simple message passing among small base stations.

Contribution

It proposes a novel distributed power allocation algorithm using multi-agent Q-learning with coordination for interference management in ultra-dense networks.

Findings

Achieves near-optimal power allocation in simulations.

Effectively manages interference in dense small cell networks.

Demonstrates the potential of distributed learning for network resource management.

Abstract

The deployment of ultra-dense networks is one of the main methods to meet the 5G data rate requirements. However, high density of independent small base stations (SBSs) will increase the interference within the network. To circumvent this interference, there is a need to develop self-organizing methods to manage the resources of the network. In this paper, we present a distributed power allocation algorithm based on multi-agent Q-learning in an interference-limited network. The proposed method leverages coordination through simple message passing between SBSs to achieve an optimal joint power allocation. Simulation results show the optimality of the proposed method for a two-user case.