Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
Reads0
Chats0
TLDR
The proposed deep learning-based approach to handle wireless OFDM channels in an end-to-end manner is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists.Abstract:
This letter presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we exploit deep learning to handle wireless OFDM channels in an end-to-end manner. Different from existing OFDM receivers that first estimate channel state information (CSI) explicitly and then detect/recover the transmitted symbols using the estimated CSI, the proposed deep learning-based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from simulation based on channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach can address channel distortion and detect the transmitted symbols with performance comparable to the minimum mean-square error estimator. Furthermore, the deep learning-based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortion and interference.read more
Citations
More filters
Journal ArticleDOI
Low-complexity detection for uplink massive MIMO SCMA systems
Posted Content
Deep Learning-based Channel Estimation for Beamspace mmWave Massive MIMO Systems
TL;DR: In this article, a learned denoising-based approximate message passing (LDAMP) network was proposed for channel estimation in mmWave massive MIMO systems, which can learn channel structure and estimate channel from a large number of training data.
Proceedings ArticleDOI
Towards Data-Driven Simulation of End-to-End Network Performance Indicators
TL;DR: This paper presents a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks and achieves a significantly better match with the real world evaluations.
Journal ArticleDOI
Deep-Learning-Based Frame Format Detection for IEEE 802.11 Wireless Local Area Networks
TL;DR: This study presents a novel, deep-learning-based frame format detection method based on a deep learning network to replace conventional detection procedures for IEEE 802.11 WLANs, and confirms that the proposed method exhibits significantly higherframe format detection performance than that of the conventional method.
Journal ArticleDOI
Blind identification of convolutional codes based on deep learning
TL;DR: The proposed deep residual network-based deep learning approach can blindly identify the convolutional codes without the need for the prior information about its coding parameters, and it achieves over 88% of recognition accuracy for 17 forms of convolutionAL codes when SNR exceeds or equals zero.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Posted Content
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: Qualitatively, the proposed RNN Encoder‐Decoder model learns a semantically and syntactically meaningful representation of linguistic phrases.
Journal ArticleDOI
Effects of clipping and filtering on the performance of OFDM
Xiaodong Li,Leonard J. Cimini +1 more
TL;DR: This work investigates, through extensive computer simulations, the effects of clipping and filtering on the performance of OFDM, including the power spectral density, the crest factor, and the bit-error rate.
Book ChapterDOI
WINNER II Channel Models
TL;DR: In this article, the authors present an introduction to channel models and channel models, and a discussion of channel model usage and models and models' models' parameters. But this chapter contains sections titled: Introduction Modelling Considerations Channel Modelling Approach Channel Models and Parameters Channel Model Usage Conclusion