Inline In-Situ MIMO Calibration with Colored-Noise Feedback
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Solution Overview
Problem
Large MIMO systems require frequent calibration to maintain peak performance, which is time-consuming and costly, and taking the system offline for calibration leads to lost revenue and operational inefficiencies.
Innovation Solution
An inline calibration method using highly correlated data sequences masked as colored noise, allowing calibration during normal operations by adjusting phase shifters based on receiver feedback, and employing machine learning to preconfigure transmitters for faster calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MIMO systems are taken offline for calibration, then calibration accuracy is improved, but system productivity and revenue are reduced
Solution Approach 1:
The patent implements continuous calibration during normal MIMO system operation by injecting colored noise signals into the data stream. This allows calibration to proceed without interrupting service, maintaining both calibration accuracy and system productivity simultaneously through overlapping calibration and operational activities
2Reliability
If frequent calibration is performed to maintain peak performance, then system reliability is improved, but time and operational costs increase
Solution Approach 1:
The system performs preliminary calibration actions continuously in the background by embedding colored noise in ongoing data streams. This preliminary continuous calibration prepares the system for peak performance without requiring dedicated calibration time slots, thereby maintaining reliability while minimizing time loss
Solution Approach 2:
Calibration is transformed from a periodic interruptive operation into a continuous background process that runs alongside normal operations, eliminating the need to stop service for calibration while maintaining consistent performance
3Measurement precision
If traditional calibration methods are used, then calibration thoroughness is improved, but system complexity and operational disruption increase
Solution Approach 1:
The patent merges calibration functionality with normal data transmission by combining calibration signals (colored noise) with operational data streams. This integration allows thorough calibration to proceed through the same hardware paths and processing pipelines already in use for normal operations, avoiding additional complexity
Solution Approach 2:
Colored noise serves as an intermediary carrier that embeds calibration information within the normal data stream. This intermediary approach allows calibration data to be transmitted through existing communication channels without requiring separate dedicated calibration hardware or protocols
Data Source
AI summary
A communication system includes one or more transmitters, each transmitter to: transmit communication signals using a defined signaling protocol with multiple antenna elements to a target receiver, the communication signals containing known specific transmit sequences spread across a frequency spectrum of the communication signals to be detectable only by receivers having the known specific transmit sequences, and receive feedback from the target receiver indicating any errors in reception of the communication signals based upon the known specific transmit sequences, and a machine learning system to use configuration of the multiple antenna elements when the communication signal was sent and the feedback to predict preconfigured settings for transmitters. A test and measurement system located at a base station, a signal generator to generate one or more signals having a predetermined modulation format, a receiver to receive the one or more signals, and a machine learning system to develop a calibration matrix.