Block-Based Signal Demodulation for Low-Complexity ISI Detection
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Solution Overview
Problem
In DS-CDMA systems, high data rates lead to intersymbol interference (ISI) due to dispersive transmission channels, which existing equalization techniques like linear equalization and decision feedback equalization struggle to address effectively, especially at peak uplink rates above 11 Mbps, resulting in performance gaps and increased complexity.
Innovation Solution
The method combines block-based equalization with maximum likelihood (ML) processing for joint detection of symbols in CDMA signals, using feedforward filtering to suppress inter-block interference and feedback filtering to address intra-block interference, thereby simplifying the detection process while maintaining performance comparable to full MLSE processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If linear equalization or decision feedback equalization is used to suppress ISI, then interference suppression is improved, but processing complexity increases and performance gap remains at high data rates
Solution Approach 1:
The patent segments the interference suppression task into two distinct phases: (1) block-based equalization that processes symbols in blocks to suppress inter-symbol interference, and (2) separate interference suppression that specifically targets residual inter-block interference. This segmentation allows each phase to be optimized independently, reducing overall complexity while maintaining effectiveness at high data rates
Solution Approach 2:
The patent introduces an intermediary processing stage between conventional equalization and final detection. This intermediate stage performs block-based processing that prepares signals for subsequent interference suppression, acting as a bridge that simplifies the overall processing chain while improving performance
2Measurement precision
If maximum likelihood sequence estimation is used to address both inter-block and intra-block interference, then detection accuracy is improved, but processing complexity increases significantly
Solution Approach 1:
The patent divides the detection process into two stages: block-based equalization that handles intra-block interference, followed by separate interference suppression that addresses inter-block interference. This segmentation avoids the need for complex MLSE that would simultaneously handle both interference types, achieving comparable accuracy with reduced complexity
Solution Approach 2:
Instead of applying full MLSE processing to all symbols simultaneously, the patent applies block-based processing to subsets of symbols, performing partial action that is computationally manageable. This partial processing approach is then combined with separate interference suppression to achieve near-MLSE performance without the full computational burden
3Object-generated harmful factors
If feedforward filtering is used to suppress inter-block interference, then interference suppression is improved, but signal energy is traded off
Solution Approach 1:
The patent employs feedback mechanisms in the separate interference suppression stage, where detected symbols are fed back to reconstruct and subtract interference from received signals. This feedback approach suppresses inter-block interference more efficiently than feedforward filtering alone, reducing the signal energy trade-off while maintaining effective interference suppression
Data Source
AI summary
Teachings presented herein combine the relative simplicity of equalization with the performance of maximum likelihood (ML) processing. These teachings are applied to the detection of symbols in a stream of symbol blocks. In one or more embodiments, block-based equalization, including feedforward filtering, suppresses inter-block interference and produces detection statistics for the symbols in each symbol block, and joint detection addresses intra-block interference by jointly detecting the most likely combination of symbols within each symbol block, based on the corresponding detection statistics. The joint detection obviates the need to address intra-block interference within the equalization filters, while, at the same time, the block-based equalization produces detection statistics for each symbol block thereby simplifying the joint detection process. Overall complexity is less than would be needed for full MLSE processing of the symbol blocks without equalization preprocessing, while performance is close to or on par with full MLSE processing.