Inference Model Segmentation for Distributed MIMO Receiver Training
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Solution Overview
Problem
Current communication networks face challenges in efficiently training and deploying inference models in distributed MIMO systems, particularly in optimizing signal processing across multiple receiver units and logical units.
Innovation Solution
A method for training an inference model that comprises sub-models for each receiver unit and a logical unit, using a neural network architecture that performs joint training across these units. This method involves evaluating the model using loss functions such as cross-entropy and mean squared error, and modifying parameters through stochastic gradient descent to optimize signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed MIMO systems are deployed with multiple receiver units, then system capacity and communication performance are improved, but device complexity and training difficulty increase
Solution Approach 1:
The inference model is segmented into multiple independent sub-models, each corresponding to a specific receiver unit. Each sub-model processes signals from its associated receiver unit independently, which simplifies the overall system architecture and reduces training complexity while maintaining the benefits of distributed MIMO capacity enhancement.
2Productivity
If joint training across multiple receiver units is performed, then spectral efficiency is optimized, but training time and computational resources increase
Solution Approach 1:
The training process is segmented into independent sub-training processes for each receiver unit sub-model. Each sub-model can be trained independently using local signal data, and the training can be parallelized across multiple devices. This segmentation dramatically reduces training time and computational resource requirements while still achieving optimized spectral efficiency through the coordinated operation of the segmented sub-models.
3Adaptability or versatility
If neural network models are used for signal processing, then flexibility and performance are improved, but hardware requirements and computational burden increase
Solution Approach 1:
The neural network model is segmented into multiple small sub-models, each handling signal processing for a specific receiver unit. This segmentation reduces the computational burden on any single device, making the system more suitable for deployment in resource-constrained environments while maintaining the flexibility and performance benefits of neural network-based signal processing.
Data Source
AI summary
A method for an apparatus in a communications network is provided. The apparatus includes at least two receiver units configured to receive signals from user equipment (UEs) in the communication network and a logical unit communicatively coupled to each of the receiver units. The logical unit receives a signal from each of the receiver units and output outputs a sequence of data corresponding to a sequence of transmitted data. The method includes receiving a signal at the receiver units and obtaining a sequence of data based on an output of an inference model that is trained to receive an input including a signal received at the receiver units and outputs a sequence of data corresponding to a sequence of transmitted data from a UE. The inference model includes sub-models corresponding to each of the receiver units and a sub-model corresponding to the logical unit.