Neural Network Receiver Configuration Switching
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Solution Overview
Problem
Neural networks (NNs) used in communication receivers are not configurable for different tasks, requiring multiple NNs to be trained for various modulation schemes and channel conditions, leading to increased hardware resources and inefficiency.
Innovation Solution
Implementing a method that allows a single neural network to represent multiple receiver configurations through superposition of models using quantized context matrices, which reduces memory and computational requirements by using the same set of weights for different receiver configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple dedicated neural networks are trained for different modulation schemes and channel conditions, then receiver performance across various settings is improved, but hardware resources (memory, FPGA surface) increase significantly
Solution Approach 1:
The patent merges multiple dedicated neural networks into a single unified neural network that can handle multiple modulation schemes and channel conditions. This is achieved by training one neural network with diverse training data covering different modulation schemes (BPSK, QPSK, 16QAM, 64QAM) and channel conditions, allowing the network to generalize across tasks without requiring separate hardware implementations for each configuration.
Solution Approach 2:
The unified neural network is designed to be universal, performing multiple functions by adapting to different modulation schemes and channel conditions through its training data. The network structure remains constant while its learned parameters enable it to function as different specialized receivers depending on the input signal characteristics, eliminating the need for multiple dedicated networks.
2Reliability
If multiple dedicated neural networks are implemented for different system configurations, then task-specific performance is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple task-specific neural networks into a single multi-functional network. Instead of implementing separate networks for BPSK, QPSK, 16QAM, and 64QAM demodulation, the system uses one unified network trained on all these modulation schemes, reducing the number of network instances from multiple to one while maintaining task-specific performance through its generalized learning capability.
Solution Approach 2:
The unified neural network dynamically adapts its behavior based on the input signal characteristics. By analyzing the received signal properties, the network automatically adjusts its processing to match the appropriate modulation scheme and channel conditions, providing dynamic task switching without requiring multiple static network implementations.
3Quantity of substance
If a single neural network is used for all receiver configurations, then hardware resources are reduced, but the network must be trained to generalize across different tasks which increases training complexity
Solution Approach 1:
The patent applies preliminary action by preparing comprehensive training data in advance that covers all desired modulation schemes and channel conditions. The training dataset is constructed to include diverse scenarios (different SNR levels, fading channels, various modulations) so that when the single neural network is trained on this pre-prepared data, it automatically learns to generalize across all tasks without requiring complex runtime decision-making or multiple specialized networks.
Data Source
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AI summary
Aspects and embodiments provide an apparatus and method which can be used to assist with selection of one of a plurality of available receiver configurations to demap a received signal at a receiver in a wireless communication network. Apparatus described may comprise trainable logic which may take the form of a neural network. The trainable logic is configured to quantize a plurality of receiver configurations. The trainable logic comprises: determination logic configured to determine a plurality of available receiver configurations and associate each receiver configuration with a context matrix; codebook logic configured to select a quantisation codebook to be used by the trainable logic for the context matrices; and learning logic configured to learn from a training dataset comprising a plurality of received signal samples relevant to reconstruction of a transmitted message; the learning logic being configured to generate from the training dataset: a set of superposed parameters and context matrices corresponding to the plurality of available receiver configurations and a set of contextual parameters for each context, the contextual parameters being generatable from the superposed parameters by combination with the context matrix associated with each receiver configuration quantize the context matrices according to the quantisation codebook; and repeat the generation of superposed parameters, context matrices and quantization of context matrices until a stop criterion is met. Arrangements may recognise that by providing context matrices for each receiver configuration and aligning those context matrices with a simple codebook, it becomes more computationally efficient to perform a context (receiver configuration) switch.