Joint Equalization and Decoding Model for Wireless CSI Reporting
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently performing joint equalization and decoding, particularly in handling nonlinear and non-Gaussian channel impairments, and require advanced Channel State Information (CSI) measurements for improved data transmission.
Innovation Solution
The implementation of AI-aided Joint Equalization and Decoding (JED) models, which utilize data-aided reception and CSI measurements, enabling online and offline model training, joint configuration of data and reference signals, and enhanced CSI reporting through decoded data sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate equalization and decoding steps are used, then the system is simpler to implement, but the performance in handling nonlinear and non-Gaussian channel impairments is insufficient
Solution Approach 1:
The patent combines separate equalization and decoding steps into a unified Joint Equalization and Decoding (JED) model. The JED model processes received signals through integrated neural network architectures that simultaneously perform equalization and decoding operations, enabling better handling of nonlinear and non-Gaussian channel impairments while maintaining manageable system complexity through standardized implementation procedures
2Adaptability or versatility
If JED model training is performed offline, then the training process is more manageable, but the model adaptability to changing channel conditions is reduced
Solution Approach 1:
The patent implements offline training procedures that prepare JED models in advance using pre-collected training data and standardized training workflows. This preliminary action makes the training process more manageable and reproducible, while the models retain adaptability through online fine-tuning capabilities and configuration updates that allow adjustment to changing channel conditions without requiring complete retraining
Solution Approach 2:
The system incorporates feedback mechanisms where model performance is continuously monitored and evaluation results are used to guide further training iterations. This feedback loop enables the model to adapt to changing conditions by learning from actual performance data, bridging the gap between manageable offline training and dynamic adaptability
3Measurement precision
If advanced CSI measurements are implemented, then the data transmission accuracy is improved, but the measurement and processing overhead increases
Solution Approach 1:
The patent extracts and utilizes decoded data sequences as auxiliary information for CSI measurements. By taking out the already-decoded data and using it to enhance CSI estimation, the system achieves higher measurement precision without requiring separate dedicated measurement resources, thereby reducing the overall time and overhead burden
Data Source
AI summary
Apparatuses, methods, and systems are disclosed for data-aided channel state information (CSI) measurement and reporting. One apparatus includes at least one processor and coupled with the at least one memory and configured to cause the apparatus to: decode a received signal to determine a decoded data sequence; perform at least one data-aided CSI measurement based on both the received signal and the decoded data sequence; and transmit a CSI report based on CSI reporting criteria and the at least one data-aided CSI measurement, where the CSI report comprises at least a portion of the at least one data-aided CSI measurement.