Neural Network Precoding for Dynamic Beamforming
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Solution Overview
Problem
Existing radio transmitter systems face challenges in performing accurate beamforming when the channel between uplink and downlink time slots is not static, especially due to user equipment or object movement, and inaccurate channel estimates, leading to suboptimal performance or failure.
Innovation Solution
A radio transmitter device equipped with a processor, memory, and a transmit antenna array uses a neural network to determine resource element specific precoding matrices for the downlink channel based on received uplink channel information, employing convolutional or transformer neural networks with residual connections and zero-forcing transformations, and trains through simulated channels to improve beamforming accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional beamforming algorithms are used with static channel assumptions, then implementation is simpler, but performance degrades when channel conditions change due to user equipment or object movement
Solution Approach 1:
The patent applies dynamics by transitioning from static channel assumptions to dynamic channel estimation using neural networks. The neural network continuously adapts to changing channel conditions caused by user equipment or object movement, making the beamforming system responsive to temporal variations while maintaining implementation feasibility through learned models.
Solution Approach 2:
The patent replaces traditional mechanical/mathematical beamforming algorithms with a neural network-based system. This substitution enables the system to handle non-linear and time-varying channel conditions that traditional algorithms cannot accommodate, improving reliability without significantly increasing implementation complexity.
2Measurement precision
If accurate channel estimation is performed to improve beamforming precision, then beamforming performance improves, but the system becomes more complex and difficult to implement
Solution Approach 1:
The patent introduces a neural network as an intermediary between raw channel measurements and beamforming decisions. This intermediary learns optimal estimation strategies from data, achieving high measurement precision while encapsulating the complexity within the trained model rather than the implementation logic.
Solution Approach 2:
The patent changes the operational parameters of channel estimation by using neural network weights and learned features instead of traditional estimation parameters. This transformation enables accurate channel estimation under varying conditions while maintaining a unified implementation approach that does not significantly increase system complexity.
3Manufacturing precision
If resource element specific precoding matrices are determined using neural networks, then beamforming accuracy improves in dynamic channels, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network offline to learn optimal precoding strategies for various channel conditions. During actual operation, the pre-trained network performs rapid inference to determine resource element specific precoding matrices, achieving high accuracy without real-time computational burden.
Solution Approach 2:
The patent uses dynamics to enable the neural network to adapt precoding matrices dynamically based on current channel conditions while maintaining computational efficiency through the learned model structure. This allows accurate resource element specific precoding without linearly increasing computational complexity for each resource element.
Data Source
AI summary
Radio transmitter devices and related methods and computer programs are disclosed. Uplink channel information is received at a radio transmitter device. The radio transmitter device determines resource element specific precoding matrices for a downlink channel based on the received uplink channel information. The radio transmitter device generates transmit antenna specific output signals for the transmit antenna array based on the determined resource element specific precoding matrices and symbols to be transmitted. The determining of the resource element specific precoding matrices for the downlink channel based on the received uplink channel information is performed with applying a neural network to the received uplink channel information. The neural network includes at least one neural network layer executable to process the received uplink channel information to output the resource element specific precoding matrices for the downlink channel.