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

By Hao Ye, Geoffrey Y. Li, Biing-Hwang Fred Juang. IEEE Wireless Communications Letters, 2017

1,736 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0f5c57903d7ce4d7e7651d19b9bcd8243b003e16

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Citations in the corpus

Model-Driven Deep Learning for Physical Layer Communications (2018, methodology): “With the emergence of the widely adapted DL, a new OFDM receiver architecture has been introduced in [13].”
Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, positive): “Following the success of DL in the individual block optimization in the physical layer [46], [47], [48], [49], [50], DL-based end-to-end communication systems have emerged as another potential direction to outperform the conventional communication structure in block error rate (BLER) and BER perform”
Model-Driven Deep Learning for MIMO Detection (2020, methodology): “In particular, a ﬁve-layer fully connected deep neural network (DNN) is embedded into an orthogonal frequency-division multiplexing (OFDM) system for joint channel estimation and signal detection (JCESD) by treating the receiver as a black box and without exploiting domain knowledge [18].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “The scope of DL as an end-to-end solution to channel estimation and symbol detection tasks in OFDM systems has been investigated in [20].”
Resource Allocation for Multi-Channel Underlay Cognitive Radio Network Based on Deep Neural Network (2018, methodology): “As shown in previous studies [8], [9], the use of DNN can enable us to exploit an optimal control strategy for communication systems without the need to solve complex problems explicitly.”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “In [129], the authors provided an DL mechanism to estimate the CSI in an Orthogonal Frequency Division Multiplexing (OFDM) system, where the estimator performs better for pilot training and channel distortions.”
Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems (2018, background): “Deep learning has been recently applied to solve compressive image recovery [5] and wireless communications [7], [8].”
Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels (2019, methodology): “In addition, deep learning based methods have also shown impressive improvement by jointly optimizing the conventional communication blocks, including joint channel estimation and detection [12], joint channel encoding and source encoding [13].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “As for learning from the environment, we are able to harness DNNs trained by the data gleaned over the air for the sake of channel estimation [312], interference identification [313], localization [314]–[318], etc.”
Deep learning for wireless physical layer: Opportunities and challenges (2017, methodology): “…the conventional communication system (e.g., modulation recognition [1], channel encoding and decoding [25], [26], [27], [28], [29], [30], and channel estimation and detection [6], [4], [31], [32]) and to replace the total system with a novel architecture on the basis of an autoencoder [1], [33].”
A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs (2024, background): “Note that recent study of channel estimation/extrapolation using deep learning has primarily focused on mechanisms such as MLP-like [138], [139], [140], [141], [142], attention [143], CNN-like [144], long short-term memory (LSTM)-like [145], and etc.”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, positive): “The authors in [184] propose to use the deep learning for the symbol detection in OFDM systems.”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, methodology): “[14] considered all physical models as the black box and proposed a DBN-based deep-learning algorithm for channel estimation and signal detection.”
Deep Learning in Physical Layer Communications (2018, methodology): “A joint channel estimation and signal detection approach has been proposed in [12].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[364] Channel estimation and signal detection in OFDM systsms.”
Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN (2018, methodology): “In addition, deep learning based methods also show potential improvement by jointly optimizing the processing blocks, including joint channel estimation and detection [6], joint channel encoding and source encoding [7].”
6G and Beyond: The Future of Wireless Communications Systems (2020, background): “For example, in channel estimation and symbol detection, deep learning approaches reported in [78], [79] have shown that the proposed deep learning-based symbol detection algorithms can provide robust and accurate results with reduced complexity.”
Deep Joint Source-channel Coding for Wireless Image Transmission (2018, neutral): “Some examples of such designs include decoder design for existing channel codes [12], [13], blind channel equalization [14], learning physical layer signal representation for SISO [11] and MIMO [15] systems, OFDM systems [16], [17], JSCC of text messages [18] and JSCC for MNIST images for analog sto”