Bidirectional Equalizer Using Viterbi Metrics for Channel-Free ISI Recovery
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Solution Overview
Problem
Conventional bidirectional arbitrated decision-feedback (BAD) equalizers face challenges in accurately estimating channels due to limitations in training sequence length and frequent changes in channel characteristics, leading to high error risks during signal reconstruction in dynamic environments.
Innovation Solution
A bidirectional equalizer that selects the output with the lowest error probability using minimum path metrics or branch metrics without channel estimation, employing a forward and reverse Viterbi decoder to generate and compare candidate data sequences, and an arbitrator to determine the optimal output based on these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional BAD equalizers use training sequence-based channel estimation, then channel characteristics can be captured, but estimation accuracy deteriorates due to limited training sequence length and frequent channel changes in dynamic environments
Solution Approach 1:
The patent extracts and eliminates the channel estimation function from the equalization process. Instead of estimating channel characteristics using training sequences, the system directly processes received signals through forward and reverse equalizers, bypassing the unreliable channel estimation step entirely. This extraction resolves the contradiction by removing the source of estimation errors while maintaining equalization capability.
Solution Approach 2:
The equalization system performs self-correction by using bidirectional processing and Viterbi decoding to inherently compensate for channel effects without external training sequences. The forward and reverse equalizers work together with the Viterbi decoder to automatically track and correct signal distortions in real-time, making the system self-sufficient and adaptive to dynamic channel conditions.
2Device complexity
If unidirectional equalizers are used to simplify implementation, then device complexity is reduced, but equalization performance deteriorates due to inability to eliminate pre-ghost symbol interference and noise amplification
Solution Approach 1:
The patent introduces a reverse equalizer that processes the received signal in the opposite temporal direction (from end to start) compared to the conventional forward equalizer. This inversion allows the system to address pre-ghost symbol interference that forward-only equalizers cannot handle, while the Viterbi decoder arbitrates between forward and reverse results to maintain implementation feasibility.
Solution Approach 2:
The system merges forward and reverse equalization paths into a unified bidirectional structure, where both equalizers process the same received signal simultaneously. The Viterbi decoder combines the outputs of both paths through arbitration based on path metrics, achieving superior equalization performance while maintaining practical implementation through shared computational resources.
Data Source
AI summary
A bidirectional equalizer and an equalization method using the bidirectional equalizer. The bidirectional equalizer includes: a first equalizer to eliminate inter-symbol interference present in a data sequence transmitted from a transmitter and to generate a first candidate data sequence; a first time reverse operator to reverse the order of the transmitted data sequence and to generate a reversed data sequence; a second equalizer to eliminate inter-symbol interference present in the reversed data sequence and to generate a second candidate data sequence; a forward Viterbi decoder to receive the first candidate data sequence, and to decode and output the first candidate data sequence for every data symbol using a first minimum path metric calculated by a maximum likelihood (ML); a reverse Viterbi decoder to receive the second candidate data sequence, and to decode and output the second candidate data sequence for every data symbol using a second minimum path metric calculated by ML; a second time reverse operator to reverse the order of the decoded second candidate data sequence output from the reverse Viterbi decoder and to output the reversed decoded second candidate sequence; and an arbitrator to compare the first minimum path metric with the second minimum path metric, and to selectively output an output of the forward Viterbi decoder or an output of the second time reverse operator according to the comparison result.