GNN-Based MIMO Precoding for Low-Complexity Antenna Selection
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Solution Overview
Problem
Existing precoding optimization methods in wireless communication systems are sensitive to channel estimation errors and have high computational complexity, limiting their application in real-time networks, especially in MIMO systems.
Innovation Solution
A precoding method using a graph neural network (GNN) to determine a precoding matrix based on channel information, reducing implementation complexity by constructing a graph representation of the wireless communication system and updating features through multiple network layers to select a target receive antenna.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional iterative algorithms are used to resolve non-convex precoding optimization problems, then precoding performance can be improved, but computational complexity becomes high for large-scale problems
Solution Approach 1:
The patent replaces conventional iterative optimization algorithms with a deep learning-based precoding generation network. The network directly maps channel state information to precoding matrices through learned transformations, eliminating the need for iterative numerical optimization. This substitution of mechanical iterative computation with a trained neural network model significantly reduces computational complexity while maintaining precoding performance.
Solution Approach 2:
The patent pre-trains the precoding generation network offline using channel state information and corresponding optimal precoding matrices. Once trained, the network contains pre-computed optimization knowledge that can be rapidly applied during real-time operation without requiring iterative computation. This preliminary training action transfers the computational burden from online execution to offline preparation.
2Use of energy by moving object
If conventional optimization methods are applied to MIMO systems, then precoding can improve energy and spectral efficiency, but implementation complexity increases
Solution Approach 1:
The patent substitutes complex iterative optimization algorithms with a deep learning model that directly generates precoding matrices. The precoding generation network processes channel state information through learned transformations to produce optimal precoding solutions, replacing mechanical iterative computation with a trained neural network. This reduces implementation complexity while maintaining energy efficiency improvements in MIMO systems.
3Measurement precision
If iterative algorithms are used for precoding optimization, then solution accuracy can be achieved, but sensitivity to channel estimation errors increases
Solution Approach 1:
The patent employs a deep learning-based precoding generation network that learns optimal precoding strategies from training data encompassing various channel conditions and estimation errors. The network incorporates implicit feedback mechanisms through its trained weights, which capture robust mappings from channel state information to precoding matrices. This feedback-based learning approach reduces sensitivity to channel estimation errors compared to conventional iterative algorithms that directly process noisy channel estimates.
Data Source
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AI summary
A precoding method is provided, applied to a wireless communication system. The wireless communication system includes at least one transmit end device and at least one receive end device, the at least one transmit end device includes a plurality of transmit antennas, and the at least one receive end device includes a plurality of receive antennas. The method includes: obtaining channel information between the transmit antennas and the receive antennas; inputting the channel information into a GNN, where each piece of channel information serves as an initial feature of an edge corresponding to the channel information, and updating a feature of each edge by using a plurality of network layers in the GNN, to obtain an output feature of each edge, where the edge is an edge in a graph representation of the wireless communication system, the graph representation further includes transmit nodes representing the transmit antennas and receive nodes representing the receive antennas, where the transmit nodes and the receive nodes are connected by edges; determining a status of each receive node based on the feature of the edge connected to each receive node; and determining a precoding matrix based on output features of the plurality of edges and statuses of the plurality of receive nodes. This effectively reduces precoding implementation complexity.