Multi-Antenna Receiver Interference Reduction Using MAP Weighting
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Solution Overview
Problem
Existing multi-antenna receivers in OFDM systems face challenges in effectively reducing interference, leading to high demodulation and decoding errors due to noise and interference, particularly because conventional methods impose constraints on weighting vectors that limit performance.
Innovation Solution
The method employs the Maximum A Posterior (MAP) approach to determine weighting vectors using covariance matrices that represent time and frequency constraints of the propagation channel, simplifying computations by decomposing these matrices into eigenvectors and retaining the largest eigenvalues to minimize performance loss and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional interference reduction methods are used with constraints on weighting vectors, then device complexity is reduced, but interference reduction performance deteriorates
Solution Approach 1:
The patent changes the parameter representation by using covariance matrices to characterize channel statistics instead of directly constraining weighting vectors. This allows the receiver to adapt to channel conditions by estimating covariance matrices from pilot symbols and using them to compute optimal weighting vectors without imposing restrictive constraints, thereby improving interference reduction performance while maintaining manageable computational complexity through efficient matrix operations.
2Reliability
If optimal weighting vectors are determined without constraints, then interference reduction performance is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by estimating the covariance matrices of the propagation channel and the convolved channel in advance using pilot symbols before determining the optimal weighting vectors. This preliminary estimation allows the system to capture channel statistics and use them to compute weighting vectors that optimize interference reduction without requiring complex real-time constraints, thereby achieving high performance with manageable computational complexity.
3Reliability
If covariance matrices are fully decomposed into eigenvectors, then interference reduction performance is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential information from the full covariance matrix decomposition by using the eigendecomposition to identify and retain the dominant eigenvalues and eigenvectors that capture the most significant channel characteristics. This extraction approach allows the system to achieve near-optimal interference reduction performance by focusing computational resources on the most important components of the channel covariance structure, thereby reducing overall computational complexity while maintaining high SNIR optimization.
Data Source
AI summary
This interference reduction method in a receiver (2) comprising at least two antennas (4, 6), each receiving a signal transmitted through a radio propagation channel, comprises the following steps: - weighting (20) of each of the signals received with a weighting vector associated respectively with a respective antenna of the receiver; - combination (22) of the weighted signals received to obtain a combined received signal; - weighting (24) of a reference signal with another weighting vector; - comparison (26) of the combined received signal and the weighted reference signal to obtain an error; and - determination (28) of the weighting vectors with the help of the maximum a posteriori criterion by maximising the probability of realisation of the said weighting vectors conditionally with the error obtained.