Neural Network Signal Processing for Wireless Baseband
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Solution Overview
Problem
Wireless baseband processing receivers face high power consumption and latency due to the complexity of channel estimation and demodulation algorithms, which require computationally intensive operations and high precision matrix inversion.
Innovation Solution
The implementation of a dual signal processing chain system, where one chain uses traditional high-accuracy algorithms and the other chain uses lower-accuracy algorithms combined with trained neural networks to reduce processing resources, with parameters updated based on comparison of output data to achieve equivalent results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional high-precision algorithms (MMSE, zero forcing) are used for channel estimation, then measurement precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent replaces traditional mechanical/mathematical signal processing algorithms (MMSE, zero forcing involving high dimensional matrix inversion) with a neural network-based system. The neural network is trained offline to perform channel estimation, substituting the computationally intensive matrix operations with neural network inference that has lower runtime complexity while maintaining estimation precision.
Solution Approach 2:
The neural network parameters are trained in advance using training data that includes channel characteristics. This preliminary training phase allows the network to learn optimal estimation mappings, so that during actual operation, only forward propagation is needed rather than real-time matrix inversion, significantly reducing processing complexity.
2Measurement precision
If traditional high-precision algorithms are used for channel estimation and demodulation, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent substitutes energy-intensive traditional algorithms (MMSE, zero forcing with high dimensional matrix inversion) with a neural network-based approach. The neural network, once trained, requires significantly less computational power for inference, directly reducing power consumption while maintaining the same channel estimation precision.
3Measurement precision
If traditional high-precision algorithms are used for channel estimation and demodulation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces time-consuming traditional algorithms involving high dimensional matrix inversion with a neural network-based system. The neural network performs channel estimation and demodulation through efficient forward propagation operations that have lower computational complexity, thereby reducing processing latency while maintaining precision.
Solution Approach 2:
The complex computational work is performed in advance during the training phase, where the neural network learns the optimal mappings from training data. During actual operation, only lightweight inference is needed, significantly reducing real-time processing time and latency compared to performing matrix inversion operations on-the-fly.
4Device complexity
If neural networks are used to reduce processing complexity, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The neural network is trained offline using training data that represents the channel characteristics. This preliminary training allows the network to learn optimal estimation mappings, ensuring that when deployed, it achieves precision comparable to traditional algorithms despite having lower runtime complexity.
Solution Approach 2:
The patent employs a feedback mechanism where the neural network output is compared with the actual channel state or reference values. This feedback is used to fine-tune or retrain the network parameters, ensuring that precision requirements are met while maintaining the benefits of reduced processing complexity.
Data Source
AI summary
An apparatus for processing a received radio signal includes at least one processor and at least one memory. The at least one memory storing computer program code. The at least one memory and the computer program code being configured to, with the at least one processor, cause the apparatus to at least in part perform processing (received radio signal data with first and second signal processing chains, which respectively include first and second processing modules configured to respectively determine first output and an estimation of the first output data, and determine second output data using a neural network based on the estimation; updating parameters of the neural network based on the first output data and the second output data; and after the updating, processing the received radio signal data with the second signal processing chain, without applying the first processing module.