Blind Demodulation via Error Retro-propagation in Neural Signal Networks
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Solution Overview
Problem
Existing methods for blind demodulation of digital telecommunication signals, particularly in non-cooperative scenarios, face challenges in processing high-rate signals in real-time and compensating for signal distortions such as synchronization and phase variations, especially when dealing with multiple polarizations.
Innovation Solution
A real-time method utilizing a network of specialized neurons with 'Next', 'Propagate', and 'Update' logic sub-blocks for error propagation and parameter updating, enabling precise demodulation of digital telecommunication signals by sampling and retropropagation, which compensates for channel effects and signal deformations like amplification, phase, and carrier leaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EM algorithm-based blind demodulation methods are used, then parameter estimation accuracy is improved, but real-time processing capability deteriorates
Solution Approach 1:
The patent segments the blind demodulation process into distinct functional blocks: a signal model definition block that establishes mathematical relationships, an error computation block that calculates discrepancies between predicted and actual signals, and a parameter update block that iteratively refines estimates. This segmentation enables parallel processing and optimization of each module independently, achieving real-time performance while maintaining accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where computation errors are calculated by comparing the actual received signal with the signal predicted by the current parameter estimates. These errors are then fed back to update the parameters iteratively, creating a closed-loop system that converges to accurate parameter values in real-time, resolving the contradiction between accuracy and processing speed.
2Adaptability or versatility
If blind demodulation without pilot sequences is used, then system adaptability is improved, but signal distortion compensation capability deteriorates
Solution Approach 1:
The patent enables the system to perform self-service by automatically estimating channel parameters and signal characteristics directly from the received signal without external pilot assistance. The signal itself serves as the reference for computing errors and updating parameters, allowing the system to adapt to unknown channels while maintaining compensation capability through iterative refinement of parameter estimates.
3Productivity
If high-rate signal processing is implemented, then communication throughput is improved, but computational complexity deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining the signal model and mathematical relationships between parameters before processing begins. The computational framework, including error computation formulas and parameter update rules, is established in advance, allowing high-rate signals to be processed through optimized, pre-configured computational paths that reduce real-time complexity while maintaining high throughput.
Data Source
AI summary
The present invention concerns a real-time method for the blind demodulation of digital telecommunication signals, based on the observation of a sampled version of this signal. The method comprises the following steps: —acquisition, by a sampling, of a first plurality of signals in order to each constitute an input of a network of L processing blocks (G, F, H), also referred to here as “specialized neurons”, each neuron being simulated by the outputs of the preceding block, the first plurality of signals being input into the first block simulating a first neuron of the network in order to generate a plurality of outputs of the first block; each neuron F being simulated by the outputs of an upstream chain G and stimulating a downstream chain H; each set of samples passes through the same processing chain; —the outputs of the last blocks of the network ideally correspond to the demodulated symbols; —addition of a nonlinearity to each of the outputs of the last block of the network making it possible to calculate an error signal and propagation of this error in the reverse direction of the processing chain (“retropropagation”); —estimation, upon receipt of the error by each neuron (i), of a corrective term δθi and updating, in each block, of the value of the parameter θi according to θi+=δθi.