Neural Network Channel Estimation for MIMO Systems
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Solution Overview
Problem
Current transmission systems face challenges in accurately estimating channel transfer functions, especially in multiple input multiple output (MIMO) systems, where existing methods do not effectively exploit relationships between channels, leading to suboptimal performance.
Innovation Solution
The proposed solution involves an apparatus and method that includes a channel estimator module, which uses training data pairs to generate and update parameters for estimating the channel transfer function. This module converts received bits into estimated transmission symbols, generates an estimated channel transfer function, and provides training data pairs for updating parameters, potentially using neural networks for improved estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional channel estimation methods are used in MIMO systems, then device complexity is reduced, but measurement precision deteriorates due to inability to exploit relationships between multiple channels
Solution Approach 1:
The patent combines multiple channel estimates from different transmit antennas into a single multi-dimensional channel state representation. The neural network processes these combined channel estimates together with received symbols to produce improved channel predictions, exploiting the relationships between multiple channels rather than treating them separately.
Solution Approach 2:
The neural network acts as an intermediary between the raw channel estimates and the final channel prediction. It processes the input channel estimates through learned transformations to produce improved predictions, serving as a mediator that captures complex relationships between channels that traditional linear methods miss.
2Measurement precision
If neural network-based channel estimation is implemented, then measurement precision improves through learning from training data, but device complexity increases due to additional processing requirements
Solution Approach 1:
The neural network is pre-trained offline using simulated training data that models various channel conditions. This preliminary training phase allows the network to learn optimal processing strategies before deployment. During actual operation, the pre-trained network requires only inference computations, significantly reducing real-time processing complexity while maintaining high estimation accuracy.
Solution Approach 2:
The system uses simulated training data that copies the statistical properties of real channel conditions to train the neural network. This allows the network to learn from a wide variety of channel scenarios without requiring actual real-time data collection and processing during the training phase.
3Measurement precision
If channel estimates are treated separately without exploiting relationships, then device complexity is minimized, but measurement precision deteriorates due to loss of inter-channel information
Solution Approach 1:
The patent transforms the channel estimation problem from treating each channel independently to processing channels as a multi-dimensional array. The neural network accepts channel estimates from multiple transmit antennas as a structured input and produces predictions that exploit correlations across the spatial dimension, effectively adding the dimension of inter-channel relationships to the processing.
Data Source
AI summary
An apparatus, method and computing program is described including: receiving one or more received symbols and one or more received bits, wherein the received symbols are received at a receiver of a transmission system including a transmitter, a channel, and the receiver; converting one or more of the received bits that are deemed to be correct into one or more estimated transmission symbols; generating an estimated channel transfer function based on one or more of the estimated transmission symbols and corresponding received symbols; and providing training data pairs, each training data pair including a first element based on the estimated channel transfer function and a second element based on the corresponding received symbols.