Adaptive Receiver Demodulation and Quantization for Communication Systems
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Solution Overview
Problem
Current communication systems face challenges in achieving high-performance demodulation and decoding due to computational complexity, especially when dealing with quantized log-likelihood ratios and non-ideal channel conditions, which degrades bit error rates and requires complex and inefficient algorithms.
Innovation Solution
The system incorporates trainable parameters in the demodulation, quantization, and decoding modules, utilizing neural networks or differentiable parametric functions, and employs a loss function based on error rates to optimize weights through stochastic gradient descent or reinforcement learning, allowing for adaptive improvement of demodulation and quantization processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional demodulation and decoding algorithms are used, then the system can process communication signals, but the computational complexity is high and bit error rates degrade under non-ideal channel conditions
Solution Approach 1:
The patent transforms the fixed-parameter conventional algorithms into adaptive systems with trainable parameters. Neural network-based demodulation and quantization modules learn optimal parameters from training data, enabling the system to adapt to non-ideal channel conditions and reduce bit error rates without proportionally increasing computational complexity.
Solution Approach 2:
The patent replaces traditional mechanical/mathematical algorithms with neural network-based systems. The demodulation module uses neural networks to replace conventional signal processing algorithms, and the quantization module uses learnable quantization functions instead of fixed quantization rules, achieving better performance through data-driven approaches.
2Adaptability or versatility
If the quantization module uses fixed quantization rules, then the system structure is simple, but the system cannot adapt to varying channel conditions and performance degrades
Solution Approach 1:
The patent makes the quantization module dynamic by introducing trainable parameters that are updated during training. The quantization function transitions from a static, fixed rule to a dynamic, adaptive function that can adjust its behavior based on learned patterns from training data, enabling adaptation to varying channel conditions.
Solution Approach 2:
The quantization module performs self-optimization through the training process. By using a learnable quantization function with trainable parameters, the module automatically adjusts its quantization strategy to optimize performance for specific channel conditions without requiring manual intervention or complex external control mechanisms.
3Reliability
If trainable parameters are introduced in demodulation and quantization modules, then the system can adaptively optimize performance, but the training process requires additional computational resources and time
Solution Approach 1:
The patent performs the computationally intensive training process in advance during system deployment or initialization. Once trained, the learned parameters are stored and reused during actual communication operations, eliminating the need for continuous training. This preliminary action separates the one-time training cost from the ongoing operational performance, making the system efficient during actual use.
Data Source
AI summary
An apparatus, method and computer program is described including initialising trainable parameters of a receiver of a transmission system, wherein the receiver includes a demodulation module for demodulating received symbols, a quantization module for generating quantized versions of the demodulated symbols and a decoder for generating a decoded output derived from the quantized versions of the demodulated symbols, wherein the demodulation module has at least some trainable weights and the quantization module has at least some trainable weights; receiving a first training sequence of messages at the receiver; obtaining or generating a loss function; and updating at least some of the trainable parameters of receiver based on the loss function, wherein updating at least some of the trainable parameters of receiver includes updating at least some of the trainable weights of the demodulation module and updating at least some of the trainable weights of the quantization module.