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AI-Based Communications
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AI-Based Communications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 14622

Special Issue Editors

Dr. Jeong Woo Lee

E-Mail Website
Guest Editor
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea
Interests: wireless communications; error control coding; cognitive radio; deep learning; AI-based communications
Dr. Jingon Joung

E-Mail Website
Guest Editor
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea
Interests: IoT; 5G; UAV communications; energy efficient communications; machine learning technology
Special Issues, Collections and Topics in MDPI journals
Dr. Cheol-Ho Hong

E-Mail Website
Guest Editor
Electrical and Electronics Engineering, Chung-Ang University, Seoul, Republic of Korea
Interests: system virtualization; data center networking; fog/edge/cloud computing; machine learnig
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Wireless communications have experienced a tremendous growth of data traffic as well as the appearance of new applications such as smart devices, autonomous systems, and the Internet of Things (IoT). To provide mobile users with high quality of service (QoS) in a complex environment, wireless communications encounter high needs for the intelligent operation, design, and optimization of communication systems. As a result, artificial intelligence (AI) began to be actively adopted in the design and operation of wireless communication systems. The earlier stage of this trend has focused on the upper layers of communications, while recent research works extended the scope to the physical layer as well.

A variety of learning algorithms and artificial neural networks have been studied in recent research works on wireless communications. These include machine learning, deep learning, reinforcement learning, deep reinforcement learning, federated learning, deep neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. These technologies are utilized for signal detection, sparse signal recovery, channel modeling, network optimization, resource management, routing, transport protocol design, etc.

The goal of this Special Issue is to disseminate the latest research results on AI-based (or AI-aided) communications. Potential topics include, but are not limited to:

  • AI-based signal detection, estimation, interference mitigation;
  • AI-based MIMO, massive-MIMO, mmWave, beamforming;
  • AI-based wireless sensor network (WSN), device-to-device (D2D) networks;
  • AI-based Internet-of-Things (IoT), vehicular networks;
  • AI-based resource and network optimization;
  • AI-based fog/edge/cloud computing.

Wireless sensor networks (WSN), Internet-of-Things (IoT), device-to-device (D2D) networks, and vehicle-to-everything (V2X) networks are well-known emerging applications of wireless communications. To operate WSN, IoT, D2D, and V2X, it is necessary to detect signals, estimate channels, optimize routes, and allocate communication resources to participating devices in an efficient and robust manner. As the wireless environment gets more complex, the intelligent operation of communication systems and participating devices is required. Artificial Intelligence (AI) technologies are known to enable the sub-optimal design and operation of wireless communication systems in a randomly varying environment. Thus, AI-based communication is considered a good solution under the circumstances of increasing data traffic, limited communication resources, and the continuous appearance of new applications. Thus, this Special Issue “AI-Based Communications” is very timely and fits within the scope of Sensors.

Dr. Jeong Woo Lee
Dr. Jingon Joung
Dr. Cheol-Ho Hong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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13 pages, 479 KiB  
Article
FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs
by Sunghwan Park, Yeryoung Suh and Jaewoo Lee
Sensors 2021, 21(2), 600; https://doi.org/10.3390/s21020600 - 16 Jan 2021
Cited by 31 | Viewed by 4953
Abstract
Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited [...] Read more.
Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network. Full article
(This article belongs to the Special Issue AI-Based Communications)
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18 pages, 1156 KiB  
Article
Proactive Congestion Avoidance for Distributed Deep Learning
by Minkoo Kang, Gyeongsik Yang, Yeonho Yoo and Chuck Yoo
Sensors 2021, 21(1), 174; https://doi.org/10.3390/s21010174 - 29 Dec 2020
Cited by 9 | Viewed by 4219
Abstract
This paper presents “Proactive Congestion Notification” (PCN), a congestion-avoidance technique for distributed deep learning (DDL). DDL is widely used to scale out and accelerate deep neural network training. In DDL, each worker trains a copy of the deep learning model with different training [...] Read more.
This paper presents “Proactive Congestion Notification” (PCN), a congestion-avoidance technique for distributed deep learning (DDL). DDL is widely used to scale out and accelerate deep neural network training. In DDL, each worker trains a copy of the deep learning model with different training inputs and synchronizes the model gradients at the end of each iteration. However, it is well known that the network communication for synchronizing model parameters is the main bottleneck in DDL. Our key observation is that the DDL architecture makes each worker generate burst traffic every iteration, which causes network congestion and in turn degrades the throughput of DDL traffic. Based on this observation, the key idea behind PCN is to prevent potential congestion by proactively regulating the switch queue length before DDL burst traffic arrives at the switch, which prepares the switches for handling incoming DDL bursts. In our evaluation, PCN improves the throughput of DDL traffic by 72% on average. Full article
(This article belongs to the Special Issue AI-Based Communications)
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Review

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26 pages, 1197 KiB  
Review
Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey
by Ida Nurcahyani and Jeong Woo Lee
Sensors 2021, 21(19), 6542; https://doi.org/10.3390/s21196542 - 30 Sep 2021
Cited by 17 | Viewed by 4018
Abstract
The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more [...] Read more.
The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study. Full article
(This article belongs to the Special Issue AI-Based Communications)
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