Adaptive Design of mmWave Initial Access Codebooks using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method for adaptively designing beam codebooks in 5G mmWave initial access, improving connectivity by dynamically tailoring beams to environmental conditions.

Contribution

It presents a hybrid RL framework that enhances expert-designed codebooks for more flexible and environment-aware beam management in 5G mmWave networks.

Findings

Achieves a 10.8% increase in user connectivity over static codebooks.

Demonstrates the effectiveness of RL in adaptive beam selection.

Shows potential for improved resilience in dynamic environments.

Abstract

Initial access (IA) is the process by which user equipment (UE) establishes its first connection with a base station. In 5G systems, particularly at millimeter-wave frequencies, IA integrates beam management to support highly directional transmissions. The base station employs a codebook of beams for the transmission of Synchronization Signal Blocks (SSBs), which are periodically swept to detect and connect users. The design of this SSB codebook is critical for ensuring reliable, wide-area coverage. In current networks, SSB codebooks are meticulously engineered by domain experts. While these expert-defined codebooks provide a robust baseline, they lack flexibility in dynamic or heterogeneous environments where user distributions vary, limiting their overall effectiveness. This paper proposes a hybrid Reinforcement Learning (RL) framework for adaptive SSB codebook design. Building on top of expert knowledge, the RL agent leverages a pool of expert-designed SSB beams and learns to adaptively select or combine them based on real-time feedback. This enables the agent to dynamically tailor codebooks to the actual environment, without requiring explicit user location information, while always respecting practical beam constraints. Simulation results demonstrate that, on average, the proposed approach improves user connectivity by 10.8$\%$ compared to static expert configurations. These findings highlight the potential of combining expert knowledge with data-driven optimization to achieve more intelligent, flexible, and resilient beam management in next-generation wireless networks.