Blind Spatial Filtering for Co-Channel Interference Nulling
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Solution Overview
Problem
Existing wireless communication systems face challenges in multi-cellular networks due to co-channel interference, where the directions of desired signals and interference are often unknown, and their channels are highly correlated, especially in multipath environments, leading to suboptimal performance in spatial filtering and beamforming operations.
Innovation Solution
A blind spatial filtering scheme is implemented in base stations using a weighted sum signal vector and covariance matrix analysis to compute a combined receive beamforming and nulling weight vector, which is applied to received signals without prior knowledge of signal directions or channel correlations, effectively separating desired signals from interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spatial filtering techniques are used, then beamforming performance can be improved when signal directions are known, but performance deteriorates when directions are unknown and channels are highly correlated
Solution Approach 1:
The system performs self-service by automatically estimating signal and interference directions through eigenvalue decomposition of the covariance matrix, without requiring external input or prior knowledge of signal directions. The algorithm self-adapts to the multipath environment by computing eigenvectors that inherently capture the spatial characteristics of desired signals and interference.
Solution Approach 2:
The invention changes the parameter representation from known direction angles to eigenvalue-based spatial signatures. By transforming the problem into the eigenvalue domain, the system can effectively separate desired signals from interference even when traditional direction parameters are unknown or channels are highly correlated.
2Ease of operation
If blind spatial filtering is implemented, then no prior knowledge of signal directions is needed, but computational complexity increases
Solution Approach 1:
The computational process is segmented into distinct stages: covariance matrix computation, eigenvalue decomposition, eigenvector selection based on correlation criteria, and weight vector calculation. This segmentation allows the system to manage complexity systematically and apply optimizations at each stage rather than dealing with the full problem at once.
Solution Approach 2:
The algorithm computes all M eigenvectors but then selectively uses only those that meet the correlation threshold criteria. This partial action approach avoids the need to process all possible spatial signatures, reducing the effective computational load while maintaining robustness through the selective application of eigenvectors.
3Object-affected harmful factors
If eigenvalue decomposition is used to compute weight vectors, then interference can be nulled effectively, but processing time increases
Solution Approach 1:
The system performs preliminary eigenvalue decomposition on the covariance matrix to pre-compute the spatial signature basis before actual beamforming operations. This preliminary action allows subsequent beamforming weight calculations to be performed more quickly by working with the pre-computed eigenvectors rather than performing full decomposition each time.
Solution Approach 2:
The algorithm extracts only the relevant eigenvectors that correspond to desired signals based on correlation criteria, separating them from eigenvectors representing interference. This extraction process allows the system to focus computational resources on the essential components for beamforming, reducing processing time for interference nulling operations.
Data Source
AI summary
Techniques are provided to improve receive beamforming at a wireless communication device that receives energy in a frequency band at M plurality of antennas, where the received energy includes desired signals and interference signals. The wireless communication device has no knowledge of the spatial signatures of the desired signals and interference signals. A weighted sum signal vector is computed from the received signals and a covariance matrix is computed from the receive signals. Eigenvalue decomposition of the covariance matrix is computed to obtain M eigenvalues of corresponding M eigenvectors of the covariance matrix. A correlation rate is computed between the M eigenvectors and the weighted sum signal vector. A combined receive beamforming and nulling weight vector is computed from the M eigenvectors and the weighted sum signal vector and based further on the correlation rate. The combined receive beamforming and nulling weight vector is applied to the received signals so as to receive beamform the desired signals and null out the interference signals.