Beamforming and power control in sensor arrays using reinforcement learning.

Area of Science:

• Signal Processing
• Wireless Communications
• Sensor Networks

Background:

• Beamforming and power control are crucial techniques in wireless communications, each with unique advantages and limitations.
• Their combined application offers significant benefits, particularly in interference suppression, but requires efficient operational methodologies.
• Identifying optimal strategies for integrating these techniques is essential for enhancing system performance.

Purpose of the Study:

• To propose and evaluate a reinforcement learning (RL) algorithm for determining the optimal combination of beamforming and power control in sensor arrays.
• To develop an intelligent agent capable of dynamically selecting the most appropriate technique (beamforming or power control) based on application needs.
• To leverage the strengths of both beamforming and power control for superior signal reception.

Main Methods:

• Implementation of a Q-learning algorithm with an ε-greedy policy for decision-making.
• Utilizing an offline training method to develop the RL agent's strategy.
• Simulating the proposed algorithm in sensor array environments to assess its effectiveness.

Main Results:

• The RL algorithm successfully implemented a switching policy between beamforming and power control.
• The intelligent agent effectively leveraged the positive attributes of each technique.
• Simulations demonstrated the efficacy of the RL approach in optimizing signal reception.

Conclusions:

• Reinforcement learning provides an effective framework for managing combined beamforming and power control strategies.
• The proposed Q-learning agent can dynamically adapt to optimize signal reception in sensor arrays.
• This intelligent approach enhances system performance by capitalizing on the distinct advantages of beamforming and power control.