Vector Based Deep Learning Beamforming for 5G Spatial Accuracy
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Solution Overview
Problem
5G millimeter wave communication systems face challenges such as higher path losses, complex signal routing, and interference in heterogeneous networks, leading to quality of service and latency issues, which conventional CNN-based beamforming methods struggle to address effectively due to invariance-related misclassifications.
Innovation Solution
The implementation of vector-based deep learning (VBDL) models, specifically Hinton Capsule Networks (HCN) and Coordinate Convolution (CoordConv), which preserve spatial relationships and positional information, along with pruning methods to optimize neural networks for reduced size and runtime performance, enhancing beamforming vector prediction accuracy and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional CNN-based beamforming methods are used, then processing speed is improved, but prediction accuracy deteriorates due to invariance-related misclassifications
Solution Approach 1:
The patent changes the fundamental parameters of the neural network approach by transitioning from CNN to VBDL models. This involves changing the mathematical representation from scalar convolutions to vector-based operations that preserve spatial relationships, thereby improving prediction accuracy while maintaining computational efficiency through optimized vector operations
Solution Approach 2:
The patent introduces a new dimension to the processing by using vector-based representations that capture spatial relationships and orientations. This dimensional enhancement allows the model to distinguish between different spatial configurations that CNNs would misclassify, improving accuracy without sacrificing speed
2Measurement precision
If VBDL models are used to improve prediction accuracy, then computational complexity increases
Solution Approach 1:
The patent segments the VBDL model into distinct functional components including coordinate convolution layers, vector normalization modules, and routing mechanisms. This segmentation allows for optimized computation of each component separately and enables parallel processing, reducing overall computational complexity while preserving the accuracy benefits of vector-based approaches
Data Source
AI summary
Systems and methods for forming radio frequency beams in communication systems are provided. Signals from one or more devices are received at a base station and are processed using a vector based deep learning (VBDL) model or network. The VBDL model can receive and process vector and/or spatial information related to or part of the received signals. An optimal beamforming vector for a received signal is determined by the VBDL model, without reference to a codebook. The VBDL model can incorporate parameters that are pruned during training to provide efficient operation of the model.