Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks

TL;DR

This paper introduces a deep contextual bandit approach for rapid initial access in mmWave cell-free networks, leveraging neighboring AP information to enable energy-efficient sleep modes without sacrificing service quality.

Contribution

It proposes a novel deep contextual bandit learning method that uses neighbor AP data for fast, energy-efficient initial access in sleep-enabled mmWave networks.

Findings

Achieves near-instant initial access with negligible latency

Outperforms standard 5G initial access schemes in simulations

Enables energy savings without compromising service quality

Abstract

Access points (APs) in millimeter-wave (mmWave) and sub-THz-based user-centric (UC) networks will have sleep mode functionality. As a result of this, it becomes challenging to solve the initial access (IA) problem when the sleeping APs are activated to start serving users. In this paper, a novel deep contextual bandit (DCB) learning method is proposed to provide instant IA using information from the neighboring active APs. In the proposed approach, beam selection information from the neighboring active APs is used as an input to neural networks that act as a function approximator for the bandit algorithm. Simulations are carried out with realistic channel models generated using the Wireless Insight ray-tracing tool. The results show that the system can respond to dynamic throughput demands with negligible latency compared to the standard baseline 5G IA scheme. The proposed fast beam selection scheme can enable the network to use energy-saving sleep modes without compromising the quality of service due to inefficient IA