[Paper Review] Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization

Rubayet Shafin, Hao Chen+7 more|arXiv (Cornell University)|Jun 14, 2019

Advanced MIMO Systems Optimization27 references4 citations

TL;DR

This paper proposes a deep reinforcement learning (DRL)-based framework for self-tuning sectorization in MIMO broadcast beamforming, enabling dynamic, autonomous optimization of beam patterns based on real-time user distribution. The DRL agent learns to select optimal beam configurations that maximize coverage, converging precisely to the oracle solution under both periodic and Markovian mobility patterns.

ABSTRACT

Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynamically based on user' distribution in the network. Taking directly UE measurement results as input, deep reinforcement learning agent can track and predict the UE distribution pattern and come up with the best broadcast beams for each cell. Extensive simulation results show that the introduced framework can achieve the optimal coverage, and converge to the oracle solution for both single sector and multiple sectors environment, and for both periodic and Markov mobility patterns.

Motivation & Objective

• Address the suboptimal coverage caused by static, manually configured broadcast beam parameters in modern cellular networks.
• Enable dynamic, autonomous adaptation of sector-specific beam patterns in response to changing user distributions and mobility patterns.
• Develop a self-optimizing framework for MIMO broadcast beamforming that eliminates reliance on periodic drive tests and manual parameter tuning.
• Achieve optimal network coverage by learning beam configuration policies that adapt to both periodic and random (Markovian) user mobility.
• Demonstrate convergence of the DRL agent to the optimal beam configuration (oracle solution) in both single- and multi-sector environments.

Proposed method

• Employ deep Q-network (DQN) reinforcement learning to train an agent that selects beam patterns based on real-time user equipment (UE) measurement feedback.
• Use UE distribution data as direct input to the DRL agent, enabling it to track and predict user clustering patterns over time.
• Define a reward function that quantifies coverage performance based on the number of connected UEs per beam configuration.
• Train the DRL agent in a simulated environment with dynamic user mobility, including periodic and Markovian movement patterns.
• Implement a multi-sector learning architecture where each sector independently selects beam patterns based on local user distribution, while maintaining coordination for global coverage optimization.
• Use a state transition model for Markovian mobility to simulate irregular user movement and test algorithm robustness.

Experimental results

Research questions

• RQ1 Can a DRL-based system autonomously optimize broadcast beam patterns in response to dynamic user distributions without manual intervention?
• RQ2 How well does the DRL agent converge to the optimal beam configuration (oracle solution) under periodic user mobility patterns in single- and multi-sector scenarios?
• RQ3 Can the DRL framework maintain optimal performance and convergence under irregular, random user mobility patterns modeled as a Markov process?
• RQ4 To what extent does the DRL agent match the actions of the oracle beam selection policy in terms of beam pattern selection across different user distribution scenarios?
• RQ5 How does the reward signal and action mismatch evolve during training, and does the system achieve stable convergence in complex mobility environments?

Key findings

• The DRL agent achieves perfect convergence with the oracle solution in both single-sector and multi-sector environments under periodic user mobility patterns.
• The average squared difference (ASD) between the DRL agent's reward and the oracle's reward drops to zero after training, indicating exact convergence.
• The average action mismatch (AM) between the DRL agent and the oracle decreases to zero across all sectors, confirming that the agent selects the same optimal beam patterns as the oracle.
• For Markovian mobility patterns, the DRL agent still converges to the oracle solution, with both ASD and AM reaching zero, demonstrating robustness to non-periodic, random user movement.
• Even when reward values for different scenarios differ by only a small amount, the DRL agent successfully distinguishes optimal actions, indicating high learning accuracy.
• Instantaneous reward and action traces at convergence show that the agent selects the correct beam patterns for each scenario, confirming stable and accurate policy learning.