Model-Driven Deep Learning for Physical Layer Communications

By Hengtao He, Shi Jin, Chao-Kai Wen, F. Gao, Geoffrey Y. Li, Zongben Xu. IEEE wireless communications, 2018

438 citations recorded by the source corpus.

Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demonstrated an impressive performance improvement in recent years. However, most existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, model-driven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/42538ff7cd88d93dd6dec6580a6b0ded13b9e9c7

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Citations in the corpus

Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems (2019, background): “research can be also found in [19] and the references therein.”
Model-Driven Deep Learning for MIMO Detection (2020, background): “On the other hand, model-driven DL constructs the network topology based on known domain knowledge and has been successfully applied to image reconstruction [32], sparse signal recovery [33]–[36], and wireless communications recently [1], [12].”
Deep Learning in Physical Layer Communications (2018, methodology): “In [2], the iterative procedures are unfolded to a signal flow graph.”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, methodology): “How to exploit them to improve deep learning algorithms for URLLCs has drawn signiﬁcant attention as well, including in [21], [26], and [27].”
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, methodology): “This formalism applies to detection tasks [147], as well as to the computation of posterior probabilities by the belief propagation method, or to inference techniques aimed at estimating a distribution by minimizing its divergence from an approximate distribution [146].”