Energy-Efficient Sleep Mode Optimization of 5G mmWave Networks Using Deep Contextual MAB

TL;DR

This paper introduces a neural network-based contextual multi-armed bandit approach for optimizing sleep modes in 5G mmWave base stations, significantly reducing energy consumption while maintaining high user throughput.

Contribution

It presents a novel SMO method using C-MAB with epsilon decay for dynamic traffic in 5G mmWave networks, outperforming traditional strategies in energy efficiency.

Findings

Outperforms all compared SM strategies in energy efficiency.

Maintains comparable average throughput to All On approach.

Improves 10th percentile user rate and average throughput.

Abstract

Millimeter-wave (mmWave) networks, integral to 5G communication, offer a vast spectrum that addresses the issue of spectrum scarcity and enhances peak rate and capacity. However, their dense deployment, necessary to counteract propagation losses, leads to high power consumption. An effective strategy to reduce this energy consumption in mobile networks is the sleep mode optimization (SMO) of base stations (BSs). In this paper, we propose a novel SMO approach for mmWave BSs in a 3D urban environment. This approach, which incorporates a neural network (NN) based contextual multi-armed bandit (C-MAB) with an epsilon decay algorithm, accommodates the dynamic and diverse traffic of user equipment (UE) by clustering the UEs in their respective tracking areas (TAs). Our strategy includes beamforming, which helps reduce energy consumption from the UE side, while SMO minimizes energy use from the BS perspective. We extended our investigation to include Random, Epsilon Greedy, Upper Confidence Bound (UCB), and Load Based sleep mode (SM) strategies. We compared the performance of our proposed C-MAB based SM algorithm with those of All On and other alternative approaches. Simulation results show that our proposed method outperforms all other SM strategies in terms of the $10^{th}$ percentile of user rate and average throughput while demonstrating comparable average throughput to the All On approach. Importantly, it outperforms all approaches in terms of energy efficiency (EE).