5,785 research outputs found
Deep HyperNetwork-Based MIMO Detection
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is
known to be an NP-hard problem. Conventional heuristic algorithms are either
too complex to be practical or suffer from poor performance. Recently, several
approaches tried to address those challenges by implementing the detector as a
deep neural network. However, they either still achieve unsatisfying
performance on practical spatially correlated channels, or are computationally
demanding since they require retraining for each channel realization. In this
work, we address both issues by training an additional neural network (NN),
referred to as the hypernetwork, which takes as input the channel matrix and
generates the weights of the neural NN-based detector. Results show that the
proposed approach achieves near state-of-the-art performance without the need
for re-training
Deep Learning-Based Decision Region for MIMO Detection
In this work, a deep learning-based symbol detection method is developed for multi-user multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold-based detection methods, which were designed for AWGN channels, are suboptimal in the context of MIMO fading channels. Furthermore, we propose a MIMO detection framework which replaces the linear thresholds with decision boundaries trained with neural network (NN) classifiers. The symbol error rate (SER) performance of the proposed detection model is compared against conventional methods under state-of-the-art system parameters. Here, we report to up to a 2 dB gain in SER performance using the proposed NN classifiers, allowing for exploiting higher-order modulation schemes, or transmitting with reduced power. The underlying gain in performance may be further enhanced from improvements to the NN architecture and hyper-parameter optimization
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