Universal Neural Network Modem Chip for MIMO Precoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing modem chips face challenges in efficiently determining precoding matrices for multiple-input and multiple-output (MIMO) communication, particularly in reducing model storage capacity and model switching overhead.
Innovation Solution
A modem chip and method that utilize a single neural network model to determine precoding matrices corresponding to various pieces of channel state information, by generating a channel matrix, performing pre-processing to create an input matrix of a fixed size, using a universal neural network model to generate an output matrix, and then performing post-processing to determine the precoding matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple neural network models are used to determine precoding matrices for different channel state information, then the adaptability to various channel conditions is improved, but the model storage capacity and model switching overhead increase
Solution Approach 1:
The patent applies universality by designing a single neural network model that can handle multiple channel state information types. The model is trained to process diverse input configurations (different CSI formats, antenna configurations, and channel conditions) through a unified architecture, eliminating the need for separate models for each scenario. This single model performs the function of multiple specialized models while reducing storage requirements.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting input parameters and configuration settings within the single neural network model rather than switching between different models. The model accepts variable input parameters representing different channel states and adapts its processing accordingly, allowing flexibility and adaptability without requiring multiple fixed models.
2Adaptability or versatility
If multiple neural network models are used to determine precoding matrices for different channel state information, then the adaptability to various channel conditions is improved, but the model switching overhead increases
Solution Approach 1:
The single universal neural network model eliminates model switching overhead by being capable of handling all channel state information types within one model instance. The model dynamically adapts to different channel conditions through its unified architecture and parameter adjustments, removing the time-consuming process of switching between multiple specialized models.
Solution Approach 2:
The neural network model is pre-trained during the offline phase to handle various channel state information types and configurations. This preliminary training equips the single model with the capability to process diverse inputs without requiring runtime model switching, thus reducing operational overhead and latency.
3Quantity of substance
If a single neural network model is used to determine precoding matrices for various channel state information, then the model storage capacity and model switching overhead are reduced, but the input matrix size management becomes more complex
Solution Approach 1:
The patent manages input matrix size complexity by utilizing parameter changes and transformations. The system dynamically adjusts input parameters, performs padding or truncation operations, and transforms variable-sized channel state information into fixed-size input matrices that the single neural network model can process efficiently. This parameter management approach handles size variations without requiring multiple models.
Solution Approach 2:
The patent introduces intermediary processing layers that act as mediators between the variable channel state information inputs and the fixed-size neural network model. These intermediary components include padding mechanisms, truncation operations, and transformation layers that standardize inputs before they reach the core model, simplifying the management of size variations.
4Loss of time
If a single neural network model is used to determine precoding matrices for various channel state information, then the model switching overhead is reduced, but the input matrix size management becomes more complex
Solution Approach 1:
The system manages input matrix size complexity through parameter transformations and adjustments. By dynamically modifying input parameters, applying padding or truncation, and transforming variable-sized channel state information into fixed-size matrices, the system handles size variations efficiently within the single model framework, avoiding model switching overhead.
Solution Approach 2:
Intermediary processing components serve as mediators between diverse channel state information inputs and the single neural network model. These intermediaries include standardization layers, padding mechanisms, and transformation operations that convert variable inputs into the required fixed-size format, managing complexity without requiring multiple models or switching operations.
Data Source
AI summary
A method of operating a modem chip in a wireless communication device configured to perform multiple-input and multiple-output (MIMO)-based communication with an external device, includes: receiving channel state information for a channel between the wireless communication device and the external device; generating a channel matrix corresponding to the channel based on the channel state information; generating an input matrix of a preset first size based on a size of a fixed input of a universal neural network model, and the channel matrix; generating an output matrix of a preset second size based on the input matrix and the universal neural network model; and determining a precoding matrix based on the output matrix. The size of the fixed input of the universal neural network model is based on a maximum value of at least one of parameters adjustable in the MIMO-based communication.