Deep Reinforcement Learning for Interference-Aware Beam Patterns
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Solution Overview
Problem
Existing codebooks in mmWave and THz communication systems focus solely on improving beamforming gain without considering interference, leading to significant performance degradation in the presence of interference users.
Innovation Solution
A deep reinforcement learning-based beam pattern design algorithm that shapes interference-aware beams by measuring power levels and using actor-critic neural networks to optimize beam patterns without requiring channel knowledge of desired and interference users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If codebook-based beamforming is used to improve beamforming gain, then beamforming gain is improved, but interference suppression capability deteriorates
Solution Approach 1:
The patent transforms the static codebook-based beamforming into a dynamic reinforcement learning-based beam pattern design. The beam pattern is continuously optimized through interaction with the environment, allowing it to adapt to changing interference conditions while maintaining beamforming gain. The agent learns to adjust beam patterns dynamically based on received rewards that reflect both signal quality and interference levels.
Solution Approach 2:
The patent changes the optimization parameters from traditional codebook indices to continuous beam pattern parameters. Instead of selecting from pre-defined codebook entries, the reinforcement learning agent optimizes beam pattern parameters directly, enabling fine-grained control over both beamforming gain and interference suppression. This parameter transformation allows simultaneous optimization of multiple objectives.
2Object-affected harmful factors
If interference-aware beam pattern design is implemented, then interference suppression is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service through autonomous reinforcement learning. The beam pattern design system automatically learns and optimizes interference suppression without requiring manual configuration or complex centralized control. The agent independently interacts with the environment, receives feedback through reward signals, and autonomously improves beam patterns, reducing the need for complex external interference management mechanisms.
Solution Approach 2:
The patent employs feedback mechanisms where the agent receives reward signals based on beamforming performance and interference levels. This feedback loop enables continuous optimization of interference suppression while maintaining system stability. The reward function incorporates both signal quality metrics and interference measurements, guiding the agent to balance beamforming gain with interference suppression automatically.
3Ease of operation
If traditional codebooks are used for initial access and data transmission, then implementation simplicity is maintained, but performance in dense environments deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning agent in simulated environments before deployment. This pre-training phase allows the agent to learn effective beam patterns in advance, so that when deployed in real dense environments, it can immediately provide improved performance without requiring complex real-time adjustments. The preliminary learning in controlled conditions simplifies subsequent operational deployment.
Data Source
AI summary
Reinforcement learning of interference-aware beam pattern design is provided. Employing large antenna arrays is a characteristic of millimeter wave (mmWave) and terahertz (THz) communication systems. Embodiments described herein provide an efficient deep reinforcement learning based beam pattern design algorithm that achieves interference awareness. This is done by not requiring the channel knowledge of both desired user and the interference users. Simulation results show that the developed solution is capable of finding a well-shaped beam pattern that significantly suppresses the interference while sacrificing only negligible beam-forming/combining gain from the desired user, based only on power measurements. Furthermore, a platform and results based on real measurements are also presented, which indicates the effectiveness and robustness of the disclosed interference-aware beam pattern design approach in a practical system.