# Model-Driven Deep Learning for Physical Layer Communications

arXiv: https://arxiv.org/abs/1809.06059 · HTML: https://mlsanity.com/paper/1809.06059/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 30 of 51)

- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [Variational Autoencoder Leveraged MMSE Channel Estimation](https://mlsanity.com/paper/2205.05345/) (2022)
- [Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems](https://mlsanity.com/paper/2302.06896/) (2023)
- [DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks](https://mlsanity.com/paper/2101.07848/) (2021)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)
- [The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches](https://mlsanity.com/paper/2209.12423/) (2022)
- [Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs](https://mlsanity.com/paper/2401.12627/) (2024)
- [Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork](https://mlsanity.com/paper/2101.04348/) (2021)
- [HybridDeepRx: Deep Learning Receiver for High-EVM Signals](https://mlsanity.com/paper/2106.16079/) (2021)
- [Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications](https://mlsanity.com/paper/2301.00516/) (2023)
- [Hybrid Neural Coded Modulation: Design and Training Methods](https://mlsanity.com/paper/2202.01972/) (2022)
- [Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks](https://mlsanity.com/paper/2205.04944/) (2022)
- [Graph Neural Network Aided MU-MIMO Detectors](https://mlsanity.com/paper/2206.09381/) (2022)
- [NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways](https://mlsanity.com/paper/2403.09861/) (2024)
- [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024)
- [Reservoir-Based Distributed Machine Learning for Edge Operation](https://mlsanity.com/paper/2104.00751/) (2021)
- [Theoretical Analysis of Deep Neural Networks in Physical Layer Communication](https://mlsanity.com/paper/2202.09954/) (2022)
- [Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning](https://mlsanity.com/paper/2211.11406/) (2022)
- [SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers](https://mlsanity.com/paper/2308.12591/) (2023)
- [Model-based Deep Learning for High-Dimensional Periodic Structures](https://mlsanity.com/paper/2309.12223/) (2023)
- [Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels](https://mlsanity.com/paper/2401.01155/) (2024)
- [Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning](https://mlsanity.com/paper/2505.19940/) (2025)
- [Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications](https://mlsanity.com/paper/2511.08416/) (2025)

## Abstract (arXiv)

Intelligent communication is gradually considered as the mainstream direction in future wireless communications. 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 of the 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 reviews the recent advancements in the application of model-driven DL approaches in physical layer communications, including transmission scheme, receiver design, and channel information recovery. Several open issues for further research are also highlighted after presenting the comprehensive survey.