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

TL;DR

The paper tackles the high energy consumption of densely deployed mmWave 5G networks by optimizing sleep mode activation. It introduces a neural-network-based contextual multi-armed bandit (C-MAB) approach that uses tracking-area (TA) based context in a 3D urban mmWave environment with beamforming to balance energy savings and throughput. The method is benchmarked against Random, Epsilon Greedy, UCB, Load Based, and All On strategies, showing superior energy efficiency and improved 10th percentile user throughput, while approaching All On in average throughput. The results demonstrate scalability to large action spaces and suggest a practical, context-aware SMO solution for sustainable 5G mmWave deployments.

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).

Figures (7)

• Figure 1: Urban Macro (UMa) outdoor-to-outdoor communication scenario, where the reduced BS positions \ref{['RBS']} are represented by red triangles.
• Figure 2: The proposed RL framework for SMO in mmWave networks.
• Figure 3: Performance of NN-based C-MAB for SMO vs. other methods in terms of throughput considering $N=15$, $U=70$, and $\alpha_{\text{off}}=0.3$.
• Figure 4: Comparison of $10^{th}$ percentile user rate via NN-based C-MAB and other SM strategies with $N=15$, $U=70$, and $\alpha_{\text{off}}=0.3$.
• Figure 5: Comparing NEE of the NN-based CMAB with other SM strategies, considering $N=15$, $U=70$, and $\alpha_{\text{off}}=0.3$.