Nonlinear Trellis Equalization for Satellite Channel Distortion
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Solution Overview
Problem
Existing signal equalization methods in satellite communication channels fail to effectively address non-linear distortions and additive noise, leading to suboptimal performance in bandwidth-efficient modulation signals.
Innovation Solution
A trellis-based iterative equalizer using non-linear models such as Volterra series decomposition, memory polynomial, Wiener, Hammerstein, and lookup table models is employed to compute branch metrics and correct non-linear distortions, incorporating the BCJR algorithm for maximum likelihood sequence estimation and reduced state sequence estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-linear models are used to correct distortions in satellite communication channels, then signal equalization performance is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex non-linear channel compensation task into multiple manageable iterations of the BCJR algorithm. Each iteration processes a portion of the trellis structure with updated a priori information, breaking down the overall computational burden into sequential steps that converge toward the optimal solution without requiring all computations simultaneously.
Solution Approach 2:
The patent employs preliminary action by pre-computing and storing the trellis structure, transition metrics, and non-linear channel characteristics before actual signal equalization. This preparation phase allows the iterative BCJR algorithm to operate more efficiently during execution, as the computational framework is already in place and only requires iterative refinement of probability estimates.
2Reliability
If iterative equalization with non-linear models is applied, then bit error rate performance is improved, but processing time increases
Solution Approach 1:
The patent implements periodic action through the iterative nature of the BCJR algorithm, where computations are performed in repeated cycles. Each iteration refines the a posteriori probability estimates by incorporating updated a priori information from previous iterations. This periodic refinement continues until convergence criteria are met or a maximum number of iterations is reached, balancing performance improvement with processing time constraints.
Data Source
AI summary
A method includes: generating a trellis; generating one or more predicted symbols using a first non-linear model; computing and saving two or more branch metrics using a priori log-likelihood ratio (LLR) information, a channel observation, and the one or more predicted symbols; if alpha forward recursion has not yet completed, generating alpha forward recursion state metrics using a second non-linear model; if beta backward recursion has not yet completed, generating beta backward recursion state metrics using a third non-linear model; if sigma forward recursion has not yet completed, generating sigma forward recursion state metrics using the branch metrics, the alpha state metrics, and the beta backward recursion state metrics; generating extrinsic information comprising a difference of a posteriori LLR information and the a priori LLR information; computing and feeding back the a priori LLR information; and calculating the a posteriori LLR information.