Neural OFDM Signal Reconstruction for MU-MIMO Interference
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Solution Overview
Problem
Existing techniques for channel state information estimation and transmitted signal reconstruction in multi-user MIMO OFDM systems are inaccurate due to complex signal propagation and interference patterns, failing to account for intercarrier and intersymbol interference and non-linearities, especially in MU-MIMO systems.
Innovation Solution
Employing graph neural networks combined with convolutional neural layers to iteratively update tensors for accurate joint CSI estimation and transmitted signal reconstruction, utilizing positional encoding and channel state matrix estimation to capture correlations across subcarriers and symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional channel state information estimation techniques are used in multi-user MIMO OFDM systems, then the system can operate with standard processing methods, but the accuracy of signal reconstruction deteriorates due to unaccounted intercarrier and intersymbol interference
Solution Approach 1:
The patent replaces conventional mechanical signal processing methods (traditional channel estimation algorithms) with a neural network-based system. The neural network learns to model and compensate for complex interference patterns including intercarrier and intersymbol interference, achieving superior accuracy in both channel state information estimation and signal reconstruction without requiring explicit mathematical models of the interference mechanisms.
Solution Approach 2:
The patent transforms the approach by changing the fundamental parameters of signal processing. Instead of using fixed mathematical models and traditional algorithms, the system employs neural networks that can adapt to varying channel conditions. The neural network processes signals through multiple layers with different activation functions and parameter sets, enabling it to handle non-linear interference patterns that conventional linear methods cannot resolve.
2Measurement precision
If neural networks are employed to iteratively update tensors for joint CSI estimation and signal reconstruction, then measurement precision improves significantly, but device complexity increases
Solution Approach 1:
The patent segments the neural network into distinct functional components: an initialization stage that processes input signals, an inter-stream interference identification stage that detects and characterizes interference patterns, and a state update stage that iteratively refines channel state estimates. This segmentation allows each component to be optimized independently and facilitates easier implementation and debugging while maintaining overall system performance.
Solution Approach 2:
The patent applies preliminary action through the initialization stage, which performs preliminary processing of the input signal before the main iterative estimation process begins. This stage includes preliminary channel estimation and interference characterization, which guides subsequent iterative updates and reduces the computational burden during the main processing phases.
3Manufacturing precision
If graph neural networks with convolutional layers are used to capture correlations across subcarriers and symbols, then signal reconstruction accuracy improves, but processing time increases
Solution Approach 1:
The patent implements dynamics by using iterative state updates that adapt to the specific characteristics of each signal sample. The number of iterations and the degree of processing can be dynamically adjusted based on signal conditions, allowing the system to optimize between processing time and accuracy. The graph neural network structure enables parallel processing of multiple subcarriers and symbols, improving computational efficiency while maintaining accurate correlation capture.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques that may use machine learning for determining transmitted signals in communication systems that deploy orthogonal frequency division multiplexing. A system for performing the disclosed techniques includes receiving (RX) antennas to receive RX signals, each RX signal received over a respective resource element of a resource grid. Individual resource elements of the resource grid are associated with different radio subcarriers and/or data symbols. The RX signals include a combination of a plurality of transmitted (TX) streams. The system further includes a processing device to process the RX signals using one or more neural network models to determine TX data symbols transmitted via the plurality of TX streams.