Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System

By Hongji Huang, Jie Yang, Hao Huang, Yiwei Song, Guan Gui. IEEE Transactions on Vehicular Technology, 2018

727 citations recorded by the source corpus.

The recent concept of massive multiple-input multiple-output (MIMO) can significantly improve the capacity of the communication network, and it has been regarded as a promising technology for the next-generation wireless communications. However, the fundamental challenge of existing massive MIMO systems is that high computational complexity and complicated spatial structures bring great difficulties to exploit the characteristics of the channel and sparsity of these multi-antennas systems. To address this problem, in this paper, we focus on channel estimation and direction-of-arrival (DOA) estimation, and a novel framework that integrates the massive MIMO into deep learning is proposed. To realize end-to-end performance, a deep neural network (DNN) is employed to conduct offline learning and online learning procedures, which is effective to learn the statistics of the wireless channel and the spatial structures in the angle domain. Concretely, the DNN is first trained by simulated data in different channel conditions with the aids of the offline learning, and then corresponding output data can be obtained based on current input data during online learning process. In order to realize super-resolution channel estimation and DOA estimation, two algorithms based on the deep learning are developed, in which the DOA can be estimated in the angle domain without additional complexity directly. Furthermore, simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/913e5461f6f330f94e554a75d157017c948a3688

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions (2019, methodology): “Concentrating on the direction of arrivals (DOA) estimation and channel estimation issues, we apply deep learning to a massive MIMO system, and the feasibility of this novel framework is verifi ed using extensive simulations [8].”
Deep Networks for Direction-of-Arrival Estimation in Low SNR (2020, background): “The authors in [20] proposed a DNN for channel estimation in massive MIMO systems; however, they focus on the high SNR regime and on channel estimation performance.”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “For instance, [126] used DL for high-resolution channel estimation in MIMO systems.”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “Deep learning is able to learn useful features, and it can be used for the DoA estimation as proposed in [183].”
A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges (2020, background): “…[171]–[175], network trafﬁc prediction [176]–[184], computing ofﬂoad-ing [185]–[192], physical wireless techniques [193]–[203], congestion control [204]–[208], direction-of-arrival estimation [209]–[216], and so on, but there is no learning-based scheme to resolve RA problems in HetNets.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “be generated from many sources, such as mobile users and IoT devices, and in different types) [349]. For example, ML and neural networks can be used for channel estimation and signal detection [350], [351], localization based on channels state information [352], [353], and channel decoding [354], wh”
Model-Driven Deep Learning for Physical Layer Communications (2018, methodology): “Different from [4] which proposes a DNN-based super-resolution channel estimation method, the channel matrix is regarded as a 2D natural image in [17], and a LDAMP neural network is developed for channel estimation, in which the denoising convolutional neural network is incorporated into the AMP alg”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, unknown): “[460] Super-resolution channel and direction-of-arrival estimation MLP”
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.”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, methodology): “Contributions ofML inMIMO technology includes channel estimation and direction of arrival (DOA) estimation by using deep neural networks (DNN) to learn the statistics of the wireless channel and the spatial structures in the angle domain [34].”
Massive MIMO Detection Techniques: A Survey (2019, methodology): “learning in communication system such as the detector design for the MIMO transmission [148], [149], [150], [151], the channel estimation based massive MIMO [152], [153], and the pilot allocation for the massive MIMO [154].”