Deep Learning Beamforming for Wireless Interference Control
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Solution Overview
Problem
Existing beamforming technologies face challenges in controlling interference and maximizing sum data rate in wireless systems with multiple users and antennas, leading to increased computational complexity and difficulty in real-time solution derivation.
Innovation Solution
A deep learning-based beamforming method using a neural network that obtains channel information and transmit power limits to derive beamforming vectors, employing activation functions and training schemes to optimize beamforming performance, allowing for real-time operation and adaptation under various power constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional beamforming calculation methods are used to control interference and maximize sum data rate, then system performance is improved, but computational complexity increases and real-time solution derivation becomes difficult
Solution Approach 1:
The patent transforms the beamforming optimization problem from a complex computational task into a parameter prediction task by training a neural network offline. The network learns optimal beamforming parameters (weights and phases) during training, and during operation simply predicts these parameters based on current channel conditions, avoiding real-time complex calculations while maintaining optimal performance.
Solution Approach 2:
The patent performs preliminary training of the neural network offline using historical channel data and optimal beamforming solutions. This preliminary action pre-computes the complex optimization relationships, storing them in the network's weights. During real-time operation, the system only needs to perform simple forward propagation to get beamforming parameters, eliminating the need for complex real-time optimization calculations.
2Reliability
If deep learning models with many parameters are used to derive beamforming vectors, then beamforming performance is improved, but training complexity and time increase
Solution Approach 1:
The patent extracts and separates the complex training process from the real-time operation process. All computationally intensive parameter training is performed offline using recorded channel data, while the online system only performs lightweight inference. This extraction of the heavy computational burden to an offline stage significantly reduces real-time training time while maintaining high beamforming performance.
Solution Approach 2:
The patent uses a neural network with more parameters than strictly necessary (excessive action during training) to capture complex channel characteristics, but accepts the trade-off of longer offline training time. The key insight is that this excessive parameter capacity pays off by enabling extremely fast and accurate real-time beamforming parameter prediction, as the network has already learned all necessary relationships during the comprehensive offline training phase.
Data Source
AI summary
Disclosed is a beamforming method using a deep neural network. The deep neural network may include an input layer, L hidden layers, and an output layer, and the beamforming method may include: obtaining channel information h between a base station and K terminals and a transmit power limit value P of the base station, and inputting h and P into the input layer; and performing beamforming on signals to be transmitted to the K terminals using beamforming vectors derived using the output layer and at least one activation function, wherein the base station transmits the signals to the K terminals using M transmit antennas. Here, the output layer may be configured in a direct beamforming learning (DBL) scheme, a feature learning (FL) scheme, or a simplified feature learning (SFL) scheme.