SCC CSI Reporting Frequency Using PCC-Based Channel Prediction
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Solution Overview
Problem
The continuous overheads in mobile communication systems due to Channel State Information (CSI) reporting, particularly on Secondary Component Carriers (SCCs), impact both UE connections and cell throughput, which cannot be effectively mitigated by reducing reporting frequency without significant performance degradation.
Innovation Solution
A method that utilizes a trained machine learning-based prediction model to predict CSI for Secondary Component Carriers (SCCs) based on Primary Component Carrier (PCC) reports, adjusting the SCC CSI reporting frequency dynamically to balance accuracy and overhead, using a mechanism that increases frequency during sudden channel variations and decreases it when predictions are reliable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If CSI reporting frequency for SCC is reduced, then signaling overhead is reduced, but channel state estimation accuracy deteriorates
Solution Approach 1:
The patent introduces a deep neural network (DNN) as an intermediary system that receives CSI reports from SCC at reduced frequency and generates predicted CSI values. This DNN mediator bridges the gap between reduced reporting frequency and maintained channel state accuracy, allowing the system to operate with lower overhead while preserving estimation quality through the intelligence of the prediction model.
Solution Approach 2:
The system performs preliminary actions by collecting and storing historical CSI reports from both PCC and SCC before generating predictions. The DNN model is trained in advance on this historical data to learn temporal patterns and channel characteristics, enabling accurate predictions to be made without requiring continuous high-frequency reporting, thus reducing overhead while maintaining accuracy.
2Measurement precision
If CSI reporting frequency is increased, then channel state estimation accuracy is improved, but cell throughput deteriorates
Solution Approach 1:
The patent implements periodic action by establishing a regular pattern where CSI reports are collected at specific intervals rather than continuously. The DNN model processes these periodic reports and generates predictions at appropriate intervals, maintaining channel state awareness without requiring constant reporting. This periodic approach reduces the overhead burden on the uplink while preserving sufficient accuracy for effective channel management and throughput optimization.
3Loss of energy
If dynamic adjustment of reporting frequency is implemented, then overhead optimization is improved, but system complexity increases
Solution Approach 1:
The system introduces dynamics by enabling the reporting frequency to be adjusted based on actual channel conditions and prediction accuracy. The DNN model continuously learns from new data and adapts its predictions, allowing the system to dynamically optimize the balance between reporting frequency and overhead. This dynamic adjustment mechanism, while adding some complexity, enables the system to adapt to varying network conditions and optimize performance in real-time.
Data Source
AI summary
A method comprises receiving from a user equipment supporting Carrier Aggregation, CA, Channel State Information, CSI, reports for a Primary Component Carrier, PCC, for one or more previous time slots and for a current time slot. CSI reports for a Secondary Component Carrier, SCC are received from the user equipment. The CSI reports include a CSI report for a previous time slot. Each CSI report includes a value of a channel state indicator. The method further comprises predicting a value of the channel state indicator for the SCC for the current time slot; the value is predicted based upon one or more values of the channel state indicator reported for the PCC for the one or more previous time slots and for the current time slot, and at least one value of the channel state indicator reported for the SCC for the previous time slot.