Machine Learning in Communication Systems and Networks, 2nd Edition

Topic Information

Dear Colleagues,

Recent advances in machine learning, including the availability of powerful computing platforms, have received huge attention from related academic, research, and industry communities. Machine learning is considered a promising tool to tackle the challenge in increasingly complex, heterogeneous, and dynamic communication environments. Machine learning would be able to contribute to the intelligent management and optimization of communication systems and networks by enabling them to predict changes, find patterns of uncertainties in the communication environment, and make data-driven decisions.

This Topic will focus on machine learning-based solutions to manage complex issues in communication systems and networks across various layers and within various ranges of communication applications. The objective of the Topic is to share and discuss recent advances and future trends of machine learning for intelligent communication. Original research (unpublished and not currently under review by another journal) is welcome in relevant areas, including (but not limited to) the following:

• Fundamental limits of machine learning in communication;
• Design and implementation of advanced machine learning algorithms (including distributed learning) in communication;
• Machine learning for physical layer and cross-layer processing (e.g., channel modeling and estimation, interference avoidance, beamforming and antenna configuration, etc.);
• Machine learning for adaptive radio resource allocation and optimization;
• Machine learning for network slicing, virtualization, and software-defined networking;
• Service performance optimization and evaluation of machine learning-based solutions in various vertical applications (e.g., healthcare, transport, aquaculture, farming, etc.);
• Machine learning for anomaly detection in communication systems and networks;
• Security, privacy, and trust of machine learning over communication systems and networks.

Prof. Dr. Yichuang Sun
Dr. Haeyoung Lee
Dr. Oluyomi Simpson
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Journal of Sensor and Actuator Networks jsan	3.3	7.9	2012	22.6 Days	CHF 2000	Submit
Photonics photonics	2.1	2.6	2014	14.8 Days	CHF 2400	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit
Telecom telecom	2.1	4.8	2020	22.7 Days	CHF 1200	Submit

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Published Papers (4 papers)

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All Applied Sciences Electronics JSAN Photonics Sensors Telecom

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26 pages, 3455 KiB  

Open AccessArticle

Energy-Efficient Online Path Planning for Internet of Drones Using Reinforcement Learning

by Zainab AlMania, Tarek Sheltami, Gamil Ahmed, Ashraf Mahmoud and Abdulaziz Barnawi

J. Sens. Actuator Netw. 2024, 13(5), 50; https://doi.org/10.3390/jsan13050050 - 29 Aug 2024

Viewed by 850

Abstract

Unmanned aerial vehicles (UAVs) have recently been applied in several contexts due to their flexibility, mobility, and fast deployment. One of the essential aspects of multi-UAV systems is path planning, which autonomously determines paths for drones from starting points to destination points. However, [...] Read more.

Unmanned aerial vehicles (UAVs) have recently been applied in several contexts due to their flexibility, mobility, and fast deployment. One of the essential aspects of multi-UAV systems is path planning, which autonomously determines paths for drones from starting points to destination points. However, UAVs face many obstacles in their routes, potentially causing loss or damage. Several heuristic approaches have been investigated to address collision avoidance. These approaches are generally applied in static environments where the environment is known in advance and paths are generated offline, making them unsuitable for unknown or dynamic environments. Additionally, limited flight times due to battery constraints pose another challenge in multi-UAV path planning. Reinforcement learning (RL) emerges as a promising candidate to generate collision-free paths for drones in dynamic environments due to its adaptability and generalization capabilities. In this study, we propose a framework to provide a novel solution for multi-UAV path planning in a 3D dynamic environment. The improved particle swarm optimization with reinforcement learning (IPSO-RL) framework is designed to tackle the multi-UAV path planning problem in a fully distributed and reactive manner. The framework integrates IPSO with deep RL to provide the drone with additional feedback and guidance to operate more sustainably. This integration incorporates a unique reward system that can adapt to various environments. Simulations demonstrate the effectiveness of the IPSO-RL approach, showing superior results in terms of collision avoidance, path length, and energy efficiency compared to other benchmarks. The results also illustrate that the proposed IPSO-RL framework can acquire a feasible and effective route successfully with minimum energy consumption in complicated environments. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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20 pages, 5255 KiB  

Open AccessArticle

Tackling Few-Shot Challenges in Automatic Modulation Recognition: A Multi-Level Comparative Relation Network Combining Class Reconstruction Strategy

by Zhao Ma, Shengliang Fang, Youchen Fan, Shunhu Hou and Zhaojing Xu

Sensors 2024, 24(13), 4421; https://doi.org/10.3390/s24134421 - 8 Jul 2024

Viewed by 763

Abstract

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR [...] Read more.

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios. Therefore, this paper endeavored to address the challenge of AMR with limited data and proposed a novel meta-learning method, the Multi-Level Comparison Relation Network with Class Reconstruction (MCRN-CR). Firstly, the method designs a structure of a multi-level comparison relation network, which involves embedding functions to output their feature maps hierarchically, comprehensively calculating the relation scores between query samples and support samples to determine the modulation category. Secondly, the embedding function integrates a reconstruction module, leveraging an autoencoder for support sample reconstruction, wherein the encoder serves dual purposes as the embedding mechanism. The training regimen incorporates a meta-learning paradigm, harmoniously combining classification and reconstruction losses to refine the model’s performance. The experimental results on the RadioML2018 dataset show that our designed method can greatly alleviate the small sample problem in AMR and is superior to existing methods. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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18 pages, 4835 KiB  

Open AccessArticle

VLCMnet-Based Modulation Format Recognition for Indoor Visible Light Communication Systems

by Xin Zheng, Ying He, Chong Zhang and Pu Miao

Photonics 2024, 11(5), 403; https://doi.org/10.3390/photonics11050403 - 26 Apr 2024

Viewed by 1076

Abstract

In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called [...] Read more.

In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called VLCMnet. Within this model, a temporal convolutional network and a long short-term memory (TCN-LSTM) module are utilized for direct channel equalization, effectively enhancing the quality of the constellation diagrams for modulated signals. A multi-mixed attention network (MMAnet) module integrates single- and mixed-attention mechanisms within a convolutional neural network (CNN) framework specifically for constellation image classification. This allows the model to capture fine-grained spatial structure features and channel features within constellation diagrams, particularly those associated with high-order modulation signals. Experimental results obtained demonstrate that, compared to a CNN model without attention mechanisms, the proposed model increases the recognition accuracy by 19.2%. Under severe channel distortion conditions, our proposed model exhibits robustness and maintains a high level of accuracy. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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35 pages, 1273 KiB  

Open AccessReview

A Survey of PAPR Techniques Based on Machine Learning

by Bianca S. de C. da Silva, Victoria D. P. Souto, Richard D. Souza and Luciano L. Mendes

Sensors 2024, 24(6), 1918; https://doi.org/10.3390/s24061918 - 16 Mar 2024

Cited by 4 | Viewed by 2013

Abstract

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power [...] Read more.

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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