QR-Based Space-Time Filter for GSM Co-Channel Interference
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Solution Overview
Problem
Current Global System for Mobile (GSM) cellular systems face challenges in addressing co-channel interference (CCI) on the mobile station side while meeting the requirements of Downlink Advanced Receiver Performance (DARP) standards, particularly in estimating channel impulse responses and designing effective filters for interference cancellation.
Innovation Solution
A multi-channel, space-time filter circuit is introduced that jointly estimates space-time filter weights and multi-channel impulse responses using QR decomposition, coupled with a multi-channel matched filter circuit and an auto-switching mechanism to adapt between standard and JSTOF-based filtering based on interference levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a multi-channel space-time filter circuit with QR decomposition is used to jointly estimate space-time filter weights and multi-channel impulse responses, then co-channel interference reduction performance is improved, but device complexity increases
Solution Approach 1:
The patent segments the interference cancellation problem into multiple independent channels, each processed by separate multiplier and delay circuits. The multi-channel filter structure divides the overall filtering task into parallel sub-tasks for each channel, reducing the computational burden on any single processing unit while maintaining overall interference cancellation performance.
Solution Approach 2:
The patent performs preliminary QR decomposition of the channel correlation matrix to obtain a unitary matrix Q and upper triangular matrix R before actual filtering operations. This preliminary action transforms the original complex filtering problem into a simplified form where the filter weights can be directly calculated from the decomposed matrices, reducing real-time computational complexity.
2Reliability
If joint estimation of space-time filter weights and multi-channel impulse responses is performed using QR decomposition, then frame error rate performance is improved by 1.6 dB to 6.9 dB, but computational complexity increases
Solution Approach 1:
The QR decomposition of the channel correlation matrix is performed in advance to obtain the unitary matrix Q and upper triangular matrix R. These decomposed matrices are then used to directly calculate the optimal filter weights through simple matrix operations, avoiding the need for complex real-time optimization computations while achieving the 1.6 dB to 6.9 dB FER performance improvement.
Solution Approach 2:
The patent replaces complex iterative optimization algorithms with direct matrix calculations based on QR decomposition. Instead of using computationally intensive mechanical optimization processes to find optimal filter weights, the system uses closed-form mathematical solutions derived from the decomposed channel matrices, significantly reducing computational complexity.
3Device complexity
If a standard filter is used when interference level is below threshold, then device complexity is reduced, but interference cancellation capability is worsened
Solution Approach 1:
The patent implements a dynamic switching mechanism that adapts the filter operation mode based on the measured interference level. When interference exceeds a predetermined threshold, the system activates the full multi-channel space-time filtering with joint estimation. When interference is below the threshold, the system switches to standard filtering operations. This dynamic adaptation optimizes the balance between computational complexity and interference cancellation capability according to real-time channel conditions.
Data Source
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AI summary
A filter for reducing co-channel interference within a communications receiver may include a multi-channel, space-time filter circuit that filters n signal parts that have been split from a communications signal by jointly estimating space-time filter weights and multi-channel impulse responses (CIRs) based upon QR and eigenvalue decompositions. The filter may further include a multi-channel, matched filter circuit that receives multi-channel signals from the multi-channel, space-time filter circuit and has a filter response that is provided by a channel impulse response estimation from the space-time filter circuit.