Neural Network Beamforming for Wireless Signal Optimization
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Solution Overview
Problem
Existing signal processing techniques in wireless communications often require significant memory, time, or computing resources, leading to suboptimal signal generation due to computational limitations.
Innovation Solution
A neural network is trained using reinforcement learning techniques to infer hybrid beamforming parameters and transmit powers, optimizing signal-to-noise ratio (SNR) and signal-to-interference-and-noise ratio (SINR) by jointly modifying analog and digital beamforming parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signal processing techniques are used, then signal generation can be performed, but significant memory, time, and computing resources are consumed leading to suboptimal signals
Solution Approach 1:
The patent replaces traditional mechanical/computational signal processing systems with a neural network-based system. The neural network is trained offline to learn optimal signal processing patterns, and during operation, it performs rapid inference to generate beamforming parameters and transmit powers, substituting the computationally intensive traditional processing with a more efficient neural network-based approach.
Solution Approach 2:
The neural network is trained in advance using reinforcement learning techniques to learn optimal signal processing strategies. This preliminary training phase allows the network to capture complex signal processing patterns, so that during actual operation, the network can quickly apply learned knowledge without requiring extensive real-time computation, thus resolving the contradiction between signal quality and computational efficiency.
2Speed
If computational resources are limited, then processing speed can be maintained, but signal generation becomes suboptimal
Solution Approach 1:
The patent substitutes traditional sequential signal processing algorithms with a parallel neural network architecture that can rapidly process input signals. The neural network's structure enables simultaneous computation of multiple beamforming parameters, achieving high processing speed while maintaining or improving signal quality through learned optimization patterns.
Solution Approach 2:
The patent changes the operational parameters of signal processing by using a trained neural network that has learned optimal parameter configurations during training. The neural network dynamically adjusts beamforming parameters and transmit powers based on input conditions, achieving high-speed processing while maintaining signal quality through adaptive parameter optimization rather than exhaustive computation.
3Ease of operation
If traditional beamforming methods are used, then signal processing can be performed, but computational overhead is high
Solution Approach 1:
The patent replaces complex traditional beamforming computation with a neural network that has been pre-trained to perform these functions. The neural network encapsulates the complex processing logic within its weights and structure, allowing the system to maintain full signal processing capability while reducing real-time computational overhead to simple matrix multiplications and activation functions.
Solution Approach 2:
The patent creates a computational model (neural network) that copies and generalizes the optimal signal processing behavior learned during training. Instead of repeatedly executing complex traditional algorithms, the system uses the trained neural network copy that captures the essential processing logic, thereby maintaining ease of operation while significantly reducing computational overhead during deployment.
Data Source
AI summary
Apparatuses, systems, and techniques that utilize a neural network to infer signal parameters to direct and transmit wireless signals. In at least one embodiment, one or more neural networks are trained, using reinforcement learning techniques, to infer a beam direction to be used by a first device to transmit a signal based, at least in part, on characteristics of another signal being transmitted by a second device.