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
Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm
TL;DR: A pair of dominant methodologies of using DL for wireless communications are investigated, including DL-based architecture design, which breaks the classical model-based block design rule of wireless communications in the past decades.
Journal ArticleDOI
Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning
TL;DR: In this article, a deep learning-based beamforming design approach was proposed and a BF neural network (BFNN) was trained to optimize the beamformer for maximizing the spectral efficiency with hardware limitation and imperfect CSI.
Journal ArticleDOI
Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication
TL;DR: Using simulations, it is shown that the proposed scheme can achieve a high SE of the DUE while properly regulating the interference caused to the CUE, with a low computation time.
Journal ArticleDOI
Deep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels
TL;DR: The proposedDL-based channel estimator can dynamically track the channel status without any prior knowledge about the channel model and statistic characteristics and has better Mean Square Error (MSE) performance compared with the traditional algorithms and some other DL-based architectures.
Posted Content
Scoring the Terabit/s Goal:Broadband Connectivity in 6G
Nandana Rajatheva,Italo Atzeni,Simon Bicais,Emil Björnson,Andre Bourdoux,Stefano Buzzi,Carmen D'Andrea,Jean-Baptiste Dore,Serhat Erkucuk,Manuel Fuentes,Ke Guan,Yuzhou Hu,Xiaojing Huang,Jari Hulkkonen,Josep Miquel Jornet,Marcos Katz,Behrooz Makki,Rickard Nilsson,Erdal Panayirci,Khaled M. Rabie,Nuwanthika Rajapaksha,Mohammad Javad Salehi,Hadi Sarieddeen,Shahriar Shahabuddin,Tommy Svensson,Oskari Tervo,Antti Tolli,Qingqing Wu,Wen Xu +28 more
TL;DR: The road to vastly improving the broadband connectivity in future 6G wireless systems is explored, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, and supporting broadband connectivity at railway speeds up to 1000 km/h.
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