Iterative Covariance Matrix Estimation for Wireless Receiver Interference Mitigation
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Solution Overview
Problem
Existing methods for estimating covariance matrices of interference plus noise in wireless communication systems are not satisfactory, particularly in multi-cell scenarios with high interference, where necessary information for joint detection is unavailable, leading to degraded receiver performance.
Innovation Solution
An iterative method for estimating covariance matrices, which computes a reference symbol covariance matrix estimate and an updated data covariance matrix estimate through iterations, combining them to improve the estimation quality, and filters these estimates using a window-based approach to handle abrupt changes in precoding matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative methods are used to improve covariance matrix estimation, then receiver performance is improved, but computational complexity increases
Solution Approach 1:
The covariance matrix estimation process is segmented into two distinct parts: reference symbol-based estimation and data symbol-based estimation. This segmentation allows each part to be optimized independently and combined to achieve better overall performance while managing computational complexity.
Solution Approach 2:
The reference symbol covariance matrix estimate is computed in advance before data symbol processing. This preliminary estimation provides a foundation that reduces the computational burden during the main detection process, as the reference symbols are available beforehand and can be processed separately.
2Reliability
If joint detection of designated signal and interference signals is performed, then receiver performance is improved, but feasibility deteriorates due to unavailable information
Solution Approach 1:
The interference signals are extracted and processed separately from the designated signal. By computing the covariance matrix of interference plus noise independently using reference symbols and regenerated data symbols, the method avoids the need for joint detection while still achieving interference mitigation.
Solution Approach 2:
The covariance matrix estimate serves as an intermediary that captures interference characteristics without requiring direct detection of interference signals. This intermediary representation allows the receiver to mitigate interference effects without needing to decode or fully process the interfering signals themselves.
3Device complexity
If non-iterative methods are used for covariance matrix estimation, then computational complexity is reduced, but performance deteriorates
Solution Approach 1:
The method merges the reference symbol covariance matrix estimate with the data symbol covariance matrix estimate through a filtering operation. This combination allows the receiver to achieve performance close to iterative methods while avoiding the full computational burden of iteration, as the filtering operation is computationally lighter than complete iterative re-estimation.
4Measurement precision
If filtering window is applied to handle abrupt changes in precoding matrices, then estimation accuracy is improved, but processing time increases
Solution Approach 1:
The filtering operation dynamically adapts to abrupt changes in precoding matrices by using a sliding window approach. When changes are detected, the filter adjusts its averaging window to maintain accuracy without requiring complete re-estimation, thus balancing precision and processing time.
Data Source
AI summary
The present invention relates to an iterative method for estimating covariance matrices of communication signals comprising a) computing a reference symbol covariance matrix estimate R RS k �¢ l ; b) inputting said reference symbol covariance matrix estimate R RS k �¢ l to a detector or a decoder, else inputting the covariance matrix estimate output from e) as input to the detector or the decoder; c) inputting the demodulated or decoded communication signal to a symbol generator; d) computing an updated data covariance matrix estimate R data k �¢ l for each iteration based on data symbols of said regenerated communication signal; e) combining said reference symbol covariance matrix estimate R RS k �¢ l and said updated data covariance matrix estimate R data k �¢ l ; and f) forwarding said covariance matrix estimate output to the detector or decoder in b) to obtaining an updated demodulated or decoded communication signal for each iteration.