Neural Channel Equalization for Finite-Filter Symbol Estimation
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Solution Overview
Problem
Existing channel equalization methods, such as the LMMSE estimator, face challenges in practical implementation due to the need for infinite filter sizes and reliance on jointly Gaussian distribution relationships, which are often not guaranteed in real-world environments, leading to suboptimal performance and difficulty in estimating accurate SNR values.
Innovation Solution
A channel equalization device and method utilizing a neural filter trained on reception symbol sequences to estimate transmission symbols, reducing multi-channel tap effects and enabling efficient frequency selective channel flattening, with the neural filter being generated through supervised learning or retrieved from a storage or cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LMMSE estimator is used for channel equalization, then frequency selective fading can be mitigated, but implementation becomes difficult due to requirement of infinite filter size
Solution Approach 1:
The patent uses a neural filter that learns to copy the optimal LMMSE equalization behavior from training data, replacing the impractical infinite filter size requirement with a finite neural network model that approximates the desired equalization performance
Solution Approach 2:
The patent transforms the channel equalization problem from a traditional filter design with fixed parameters to a neural network with learnable parameters, allowing the system to adapt to different channel conditions without requiring infinite filter sizes
2Measurement precision
If LMMSE estimator is used, then linear filtering can be applied, but accurate SNR estimation becomes difficult without jointly Gaussian distribution
Solution Approach 1:
The neural filter learns directly from training data the relationship between received signals and transmitted symbols, making the system self-adaptive to actual channel conditions without requiring external SNR estimation or Gaussian distribution assumptions
Solution Approach 2:
The patent replaces the traditional mathematical approach based on Gaussian assumptions with a data-driven neural network approach, substituting the mechanical calculation of SNR with learned patterns from training data
3Reliability
If block-wise demodulation with Viterbi algorithm is used, then optimal block error rate can be achieved, but computational power requirements increase
Solution Approach 1:
The neural filter focuses on estimating only the symbol at the position of interest rather than performing full block-wise optimal detection, achieving sufficient performance with reduced computational effort compared to Viterbi algorithm
Solution Approach 2:
The patent uses a computationally efficient neural filter that can be quickly trained and deployed, replacing the computationally intensive Viterbi algorithm with a lighter-weight solution that achieves good enough performance for practical applications
Data Source
AI summary
There is provided a channel equalization device. The channel equalization device comprises a receiver configured to receive a plurality of consecutive reception symbol sequences through multiple channels; a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the instructions, when executed by the processor, cause the processor to estimate a transmission symbol at a position of interest among a plurality of consecutive transmission symbol sequences based on the plurality of received reception symbol sequences using a neural filter trained by training reception symbol sequences.