Iterative Receiver for MIMO OFDM Interference Cancellation
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Solution Overview
Problem
Existing wireless communication networks face challenges in efficiently decoding bits in SFBC/STBC-based or SFBC-FSTD-based MIMO OFDM systems due to inter-cell interference, with existing algorithms being sub-optimal or limited in performance and complexity, particularly in scenarios with high data traffic and multiple users.
Innovation Solution
An iterative receiver architecture that employs Minimum Mean Square Error (MMSE) or MMSE-Interference Rejection Combining (IRC) decoding algorithms, along with dynamic iteration control based on channel quality and CRC, to effectively decode bits by estimating and canceling interference, while maintaining low implementation complexity and high user throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If SFBC/STBC-based or SFBC-FSTD-based MIMO OFDM techniques are used to improve spectral efficiency and data rates, then user throughput is improved, but decoding complexity increases significantly
Solution Approach 1:
The receiver is divided into multiple processing iterations, where each iteration performs partial decoding and interference cancellation. The first iteration decodes one layer, cancels its interference, and the second iteration decodes the remaining layer with reduced interference, breaking down the complex simultaneous decoding task into manageable sequential steps
Solution Approach 2:
The receiver performs preliminary interference cancellation by decoding and reconstructing the signal from one layer before attempting to decode the other layer. This preliminary action removes a significant portion of the interference beforehand, making the subsequent decoding much simpler and more reliable
2Measurement precision
If advanced decoding algorithms like MMSE-IRC are used to improve performance in colored interference, then decoding accuracy is improved, but computational requirements increase
Solution Approach 1:
The receiver dynamically adapts its processing based on channel conditions and interference characteristics. It uses iterative processing where the complexity of each iteration can be adjusted, and the number of iterations is optimized based on the actual channel quality indicator (CQI) and cyclic redundancy check (CRC) results, allowing the system to use more computational resources only when necessary
Solution Approach 2:
The system changes processing parameters dynamically based on channel conditions. The number of iterations, the type of equalization applied, and the interference cancellation strategy are all adjusted based on measured channel quality metrics, allowing the receiver to optimize between accuracy and complexity in real-time
3Productivity
If the number of wireless network users and data traffic demand increase, then network capacity utilization is improved, but inter-cell interference increases
Solution Approach 1:
The receiver converts the harmful inter-cell interference into a beneficial signal by decoding and reconstructing it from one layer, then subtracting it from the received signal. This interference cancellation technique transforms the harmful interference into a useful component that can be removed to improve the detection of the desired signal
Solution Approach 2:
The system uses feedback from channel quality indicators (CQI) and cyclic redundancy checks (CRC) to dynamically adjust the interference cancellation strategy. The receiver monitors the quality of decoded signals and uses this feedback to optimize the number of iterations and the aggressiveness of interference cancellation, improving performance in high-interference scenarios
Data Source
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AI summary
An iterative receiver (200) is proposed for receiving in a cell (105c) a signal (A) and for providing information (b 1 ,b 2 ,r 1 ,r 2 ) carried on said signal (A) by execution of at least one processing iteration. The receiver (200) comprises: an estimate assembly (210-235) for receiving the signal (A) and providing, at each one of said processing iterations, a respective information estimate (LLR b1 ,LLR b2 ,LLR r1 ,LLR r2 ); a regeneration assembly (250-265) for receiving, at each processing iteration, said information estimate (LLR b1 ,LLR b2 ,LLR r1 ,LLR r2 ) provided by the estimate assembly (210-235) at that iteration, and for providing a regenerated signal (B) therefrom based on said information estimate (LLR b1 ,LLR b2 ,LLR r1 ,LLR r2 ) and on attenuation of radio channels over which the signal (A) has been transmitted; an interference estimate unit (265) for providing, at each iteration, an interference estimate (C) based on the signal (A) and the regenerated signal (B), the estimate assembly (210-235) providing, starting from a second processing iteration of said processing iterations, said information estimate (LLR b1 ,LLR b2 ,LLR r1 ,LLR r2 ) based on said interference estimate; and an extraction unit (240) for extracting said information (b 1 , b 2 , r 1 , r 2 ) from said information estimate (LLR b1 ,LLR b2 ,LLR r1 ,LLR r2 ).