A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

Akindele Segun Afolabi,Shehu Ahmed,Olubunmi Adewale Akinola

doi:10.3991/ijim.v15i12.20751

Abstract

<span lang="EN-US">Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, </span><span lang="EN-US">multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.</span>

Highlights

Mobile broadband usage has increased dramatically in the last couple of years due to new types of terminals such as smart phones and tablet computers [1, 2]
This paper aims at developing a learning model for coordinating inter-cell interference existing between a picocell and macrocell base stations for the purpose of improving network throughput
As can be observed in the figure, when there is no picocell base station present, only the macrocell base station served the user equipment (UE) in the network

Summary

Introduction

Mobile broadband usage has increased dramatically in the last couple of years due to new types of terminals such as smart phones and tablet computers [1, 2]. A key method to fulfill the traffic demands is by network densification which involves adding smaller low power nodes, such as picocells, to traditional high power macro nodes. This results in what is termed "Heterogeneous Networks", or HetNets [3]-[7]. HetNets are expected to boost capacity and coverage beyond macrocells They have been regarded as a promising paradigm to provide mobile users with high quality experience [2, 5]. This paper aims at developing a learning model for coordinating inter-cell interference existing between a picocell and macrocell base stations for the purpose of improving network throughput

Objectives

Results

Conclusion