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Open AccessJournal ArticleDOI

Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

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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.

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Citations
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Proceedings ArticleDOI

A BBL-Net based OFDM Signal Detection in the Presence of RF Impairments

TL;DR: In this article , a batch normalized Bi-directional long short memory network (BBL-Net) based joint channel estimation and signal detection approach for orthogonal frequency division multiplexing (OFDM) system in the presence of carrier frequency offset (CFO) and phase offset (PO) at the receiver is proposed.
Book ChapterDOI

Transmission Over OFDM and SC-FDMA for LTE Systems

TL;DR: In this paper, the authors compared the performance of using OFDM with the addition of different schemes of modulation like BPSK, QPSK and QAM and compared the result with those obtained from SC-FDMA.
Journal ArticleDOI

DNN-based Signal Detection for Underwater OTFS Systems

TL;DR: A deep learning based signal detection method is proposed for UWA OTFS communication, where a deep neural network (DNN) can recover the received symbols after sufficient training and performs lower bit error rate (BER) than classical detectors.
Posted Content

Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

TL;DR: In this paper, a learned denoising-based generalized expectation consistent (LDGEC) signal recovery network is proposed to solve the problem of channel estimation in wideband mmWave systems.
Journal ArticleDOI

Harnessing Tensor Structures—Multi-Mode Reservoir Computing and Its Application in Massive MIMO

TL;DR: In this article , a multi-mode reservoir computing (Multi-Mode RC) was proposed for symbol detection in multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems with massive MIMO employed at the BSs.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

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Journal ArticleDOI

Effects of clipping and filtering on the performance of OFDM

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
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