TDD Precoder Prediction Using Joint SRS Channel Estimation
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Solution Overview
Problem
Existing channel estimation and precoder prediction methods in TDD MIMO cellular communication systems incur high overhead due to frequent CSI updates and low SNR conditions, particularly for edge UEs, leading to inefficiencies in data transmission.
Innovation Solution
A joint sounding reference signal (SRS) channel estimation and precoder prediction model is trained using a neural network to predict precoders based on SB-level PMI-based and subcarrier-level noisy SRS-based sequences, reducing overhead by leveraging the reciprocity of UL and DL channels and improving frequency-domain resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent CSI updates are performed to maintain reliable communication link, then communication reliability is improved, but system overhead increases
Solution Approach 1:
The system performs preliminary channel estimation using SRS signals and uses this information to predict future precoders, reducing the need for frequent CSI updates. The neural network model is trained in advance to predict precoders based on historical PMI sequences and current SRS measurements, allowing the system to maintain reliability with less frequent updates.
Solution Approach 2:
The patent replaces the traditional mechanical approach of frequent CSI measurements and updates with an AI-based prediction system. Instead of continuously measuring and updating CSI, the system uses a neural network to predict future precoders based on historical data, substituting the mechanical measurement-update cycle with an intelligent prediction mechanism.
2Loss of information
If traditional CE and CSI feedback methods are used to maintain communication link, then channel state information is obtained, but overhead increases reducing resources for data transmission
Solution Approach 1:
The patent merges channel estimation and precoder prediction into a unified neural network model. Instead of separately performing CE and obtaining CSI feedback, the system combines these functions and uses a single integrated model that processes SRS signals and historical PMI sequences to directly predict precoders, reducing overhead while maintaining necessary channel information.
Solution Approach 2:
The system creates a virtual representation of the channel state through neural network predictions rather than relying on direct CSI measurements. The predicted precoders serve as a copy or approximation of the actual channel state, sufficient for maintaining communication reliability while reducing the overhead associated with direct CSI acquisition.
3Reliability
If low SNR conditions are handled with traditional methods, then edge UEs maintain connection, but system overhead increases due to frequent updates
Solution Approach 1:
The system changes the approach parameters for handling low SNR conditions by using AI-based prediction instead of traditional measurement-update methods. The neural network model is specifically designed to work with the limited and noisy SRS signals from edge UEs, extracting meaningful channel information even in low SNR conditions while reducing the frequency of updates needed.
Data Source
AI summary
A base station (BS) includes a processor configured to identify a joint sounding reference signal (SRS) channel estimation (CE) and precoding matrix indicator (PMI)-based precoder prediction model trained with a training data set. The BS also includes a transceiver operatively coupled to the processor, the transceiver configured to receive at least one subband (SB)-level PMI-based precoder sequence and at least one subcarrier-level noisy SRS-based sequence. The processor is further configured to provide, to the trained joint SRS CE and PMI-based precoder prediction model, the at least one SB-level PMI-based precoder sequence and the at least one subcarrier-level noisy SRS-based sequence, and receive, from the trained joint SRS CE and PMI-based precoder prediction model, a predicted precoder generated by the trained joint SRS CE and PMI-based precoder prediction model based on the at least one SB-level PMI-based precoder sequence and the at least one subcarrier-level noisy SRS-based sequence.