Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots
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Numerical evaluations suggest that WESN can significantly improve the symbol detection performance as well as effectively mitigate model mismatch effects using very limited training symbols.Abstract:
In this paper, we introduce a reservoir computing (RC) structure, namely, windowed echo state network (WESN), for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection. We show that adding buffers in input layers is able to bring an enhanced short-term memory (STM) to the standard echo state network. A unified training framework is developed for the introduced WESN MIMO-OFDM symbol detector using both comb and scattered patterns, where the training set size is compatible with those adopted in 3GPP LTE/LTE-Advanced standards. Complexity analysis demonstrates the advantages of WESN based symbol detector over state-of-the-art symbol detectors when the number of OFDM sub-carriers is large, where the benchmark methods are chosen as linear minimum mean square error (LMMSE) detection and sphere decoder. Numerical evaluations suggest that WESN can significantly improve the symbol detection performance as well as effectively mitigate model mismatch effects using very limited training symbols.read more
Citations
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Journal ArticleDOI
Deep Residual Learning Meets OFDM Channel Estimation
TL;DR: It outperforms other deep learning based estimation method with comparable to minimum mean square error (MMSE) estimation performance and is compatible with any downlink pilot patterns making it compatible for modern wireless systems.
Journal ArticleDOI
Deep Reservoir Computing Meets 5G MIMO-OFDM Systems in Symbol Detection
TL;DR: The introduced deep RC framework can provide a decent generalization performance using the same amount of pilots as conventional model-based methods in 5G systems and effectively mitigate unknown non-linear radio frequency (RF) distortion.
Journal ArticleDOI
RCNet: Incorporating Structural Information Into Deep RNN for Online MIMO-OFDM Symbol Detection With Limited Training
TL;DR: Numerical experiments demonstrate that the introduced RCNet can offer a faster learning convergence and as much as 20% gain in bit error rate over a shallow RC structure by compensating for the nonlinear distortion of the MIMO-OFDM signal.
Journal ArticleDOI
Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network
TL;DR: A Deep Echo State Network (DESN) is introduced to serve as the symbol detector for 5G communication networks and employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation.
Journal ArticleDOI
Machine Learning for MU-MIMO Receive Processing in OFDM Systems
TL;DR: This work proposes an machine learning (ML)-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture, which preserves the interpretability and scalability of the LMMSE receiver, while improving its accuracy in two ways.
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Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.