Antenna Phase Error Compensation via Reinforcement Learning
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Solution Overview
Problem
Multi-antenna base stations with uncalibrated phases, such as 4T4R radios, suffer from performance degradation due to arbitrary phase settings, leading to misaligned beam directions for different polarizations, which affects user equipment performance and network throughput.
Innovation Solution
The implementation of reinforcement learning to dynamically compensate for antenna phase errors by initializing sets of phase offsets associated with probability distributions, sampling, selecting, applying, and updating phase offsets based on cell-wide Key Performance Indicators (KPIs) to maximize reward values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phase calibration is performed periodically at the base station, then beam direction alignment is improved, but hardware cost and software complexity increase
Solution Approach 1:
The base station performs self-calibration by autonomously determining phase offsets between antenna branches using reinforcement learning. The system automatically adjusts phase parameters based on observed channel conditions and performance metrics, eliminating the need for external calibration equipment or complex manual procedures while maintaining accurate beam alignment.
Solution Approach 2:
The invention dynamically adjusts phase parameters of antenna branches based on changing channel conditions. By continuously optimizing phase offsets using reinforcement learning algorithms, the system adapts to varying environmental conditions without requiring periodic physical recalibration, thereby reducing complexity while maintaining precision.
2Ease of manufacture
If radios with fewer branches (4T4R) are used to reduce hardware cost, then phase calibration capability is lost, but beam direction alignment deteriorates
Solution Approach 1:
The invention replaces the mechanical/physical phase calibration system with a software-based reinforcement learning approach. Instead of relying on hardware calibration mechanisms present in more complex radios, the system uses intelligent algorithms to determine and apply appropriate phase offsets, enabling 4T4R radios to achieve proper beam alignment without physical calibration infrastructure.
Solution Approach 2:
The reinforcement learning algorithm acts as an intermediary that bridges the gap between the simplified 4T4R hardware and the required phase alignment performance. The algorithm processes channel state information and performance metrics to generate compensating phase adjustments, effectively mediating between the limited hardware capabilities and the desired beamforming performance.
3Ease of manufacture
If arbitrary phase settings are used in 4T4R radios, then hardware cost is reduced, but beam direction misalignment occurs between polarizations
Solution Approach 1:
The system implements a feedback mechanism where the reinforcement learning algorithm continuously monitors channel conditions and performance metrics (such as signal quality and throughput) to adjust phase offsets. This closed-loop control ensures that beam directions remain consistent between polarizations by automatically compensating for phase differences based on observed system performance.
4Measurement precision
If reinforcement learning is used to dynamically adjust phase offsets, then beam alignment performance is improved, but computational complexity increases
Solution Approach 1:
The reinforcement learning algorithm focuses on optimizing only the critical phase offset parameters rather than adjusting all possible system parameters. By concentrating computational resources on the most impactful adjustments (phase compensation), the system achieves high alignment accuracy without the excessive computational burden of optimizing every aspect of the radio system.
Data Source
AI summary
Systems and methods of the present disclosure are directed to a method performed by a network node for antenna phase error compensation via reinforcement learning. The method includes initializing M Multi-Arm Bandit (MAB) models to determine M phase offsets for phase deltas of N antenna branches with dual polarization where M=N−3. The method includes selecting M phase offsets with the M MABs. The method includes applying the M phase offsets to phase(s) of at least one antenna branch during transmission. The method includes, while applying the M phase offsets, determining reward values for the M phase offsets. The method includes, based on the reward values, updating the parameters of the M MAB models.