Q-Learning Inspired Method for Antenna Azimuth Selection in Cellular Networks

• Cellular networks are becoming increasingly complex, requiring careful optimization of parameters such as antenna propagation pattern, tilt, direction, height, and transmitted reference signal power to ensure a high-quality user experience. In this paper, we propose a new method to optimize antenna direction in a cellular network using Q-learning. Our approach involves utilizing the open-source quasi-deterministic radio channel generator to generate radio frequency (RF) power maps for various antenna configurations. We then implement a Q-learning algorithm to learn the optimal antenna directions that maximize the signal-to-interference-plus-noise ratio (SINR) across the coverage area. The learning process takes place in the constructed open-source OpenAI Gym environment associated with the antenna configuration. Our tests demonstrate that the proposed Q-learning-based method outperforms random exhaustive search methods and can effectively improve the performance of cellular networks while enhancing the quality of experience (QoE) for end users.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Keyword

Wireless Communications

Wireless System Architecture

Propagation Channel Modeling

5G

6G

Publication and Content Type

ref (subject category)

kon (subject category)