Reduced-State Sequence Estimation for Soft LLR Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems are power hungry and spectrally inefficient, particularly when dealing with phase noise and non-linear distortion, leading to a gap between maximum and actual spectral efficiency, especially with higher-order modulation schemes.
Innovation Solution
A method and system that improve bit error rate (BER) performance by using a sequence estimation algorithm and forward error correction (FEC), incorporating partial response pulse shaping filters and a non-linear model to manage inter-symbol interference and non-linearity, thereby enhancing spectral efficiency and tolerance to channel distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher-order modulation schemes are used to increase throughput, then spectral efficiency is improved, but sensitivity to non-linear distortion and phase noise increases, causing performance to fall further from the Shannon capacity limit
Solution Approach 1:
The system dynamically adjusts the modulation order and coding rate based on channel conditions to optimize the trade-off between throughput and reliability. The receiver adapts its demodulation and decoding parameters in real-time to maintain performance close to the Shannon capacity bound across varying channel quality.
Solution Approach 2:
The patent changes key parameters including modulation order, coding rate, and equalization tap lengths to optimize system performance. By adjusting these parameters based on channel characteristics, the system achieves higher spectral efficiency while maintaining reliability near the Shannon limit.
2Ease of manufacture
If conventional linear modulation schemes are used, then implementation is simple, but performance degrades significantly in the presence of phase noise and non-linear distortion
Solution Approach 1:
The system employs feedback mechanisms where the receiver estimates channel characteristics including phase noise and non-linear distortion effects, then feeds this information back to adjust transmission parameters. This closed-loop approach maintains simple implementation while significantly improving performance under adverse channel conditions.
Solution Approach 2:
The patent introduces an intermediary equalization and sequence estimation stage that mediates between the transmitted signal and the demodulation process. This intermediary processing compensates for phase noise and non-linear distortion effects, allowing simple linear modulation schemes to achieve reliable performance in challenging channels.
3Device complexity
If traditional equalization methods are used, then computational complexity is low, but ability to handle inter-symbol interference and non-linearity is insufficient
Solution Approach 1:
The equalization process is segmented into multiple stages including preliminary equalization, sequence estimation, and refined equalization. This segmentation allows the system to handle inter-symbol interference and non-linearity more effectively while keeping computational complexity manageable through staged processing.
Solution Approach 2:
The system applies partial equalization at multiple stages rather than attempting full equalization in a single step. This partial action approach at each stage accumulates to provide robust handling of inter-symbol interference and non-linearity while maintaining acceptable computational complexity.
Data Source
AI summary
A receiver may be operable to receive an inter-symbol correlated (ISC) signal, and generate a plurality of soft decisions as to information carried in the ISC signal. The soft decisions may be generated using a reduced-state sequence estimation (RSSE) process. The RSSE process may be such that the number of symbol survivors retained after each iteration of the RSSE process is less than the maximum likelihood state space. The plurality of soft decisions may comprise a plurality of log likelihood ratios (LLRs). Each of the plurality of LLRs may correspond to a respective one of a plurality of subwords of a forward error correction (FEC) codeword.