A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks

TL;DR

This paper introduces MANN, a multi-agent neural network approach for dynamic frequency reuse in LTE networks, significantly improving edge user throughput while maintaining overall network performance.

Contribution

The paper presents a novel multi-agent neural network scheme for dynamic frequency reuse, enhancing edge user throughput in LTE networks compared to static methods.

Findings

Edge user throughput increased by 22% for the bottom 5% of users.

Network's overall throughput retained at 95% of full frequency reuse.

Method outperforms static fractional frequency reuse schemes.

Abstract

Fractional Frequency Reuse techniques can be employed to address interference in mobile networks, improving throughput for edge users. There is a tradeoff between the coverage and overall throughput achievable, as interference avoidance techniques lead to a loss in a cell's overall throughput, with spectrum efficiency decreasing with the fencing off of orthogonal resources. In this paper we propose MANN, a dynamic multiagent frequency reuse scheme, where individual agents in charge of cells control their configurations based on input from neural networks. The agents' decisions are partially influenced by a coordinator agent, which attempts to maximise a global metric of the network (e.g., cell-edge performance). Each agent uses a neural network to estimate the best action (i.e., cell configuration) for its current environment setup, and attempts to maximise in turn a local metric, subject to the constraint imposed by the coordinator agent. Results show that our solution provides improved performance for edge users, increasing the throughput of the bottom 5% of users by 22%, while retaining 95% of a network's overall throughput from the full frequency reuse case. Furthermore, we show how our method improves on static fractional frequency reuse schemes.