Special Issue "Machine Learning for Wireless Networks - Recent Advances and Future Trends"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 6383

Special Issue Editors

Dr. Shankar Kathiresan
E-Mail Website
Guest Editor
Department of Computer Applications, Alagappa University, Karaikudi 630 002, India
Interests: healthcare applications; secret image sharing scheme; digital image security; cryptography; internet of things; optimization algorithms
Dr. Deepak Gupta
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Maharaja Agrasen institute of Technology (GGSIPU), Delhi 110086, India
Interests: software engineering; software usability; human computer interaction; algorithm computing; soft computing; neural networks; testing
Special Issues, Collections and Topics in MDPI journals
Dr. Gyanendra Prasad Joshi
E-Mail Website
Guest Editor
Intelligent Computing and Communication Lab, Sejong University, Sejong 05006, Korea
Interests: sensor localization; image sensors; MAC and routing protocols for wireless sensor networks; cognitive radio Wireless Sensor Networks; RFID system; IoT; smart city; deep learning and digital convergence
Special Issues, Collections and Topics in MDPI journals
Dr. Vicente García-Díaz
E-Mail Website
Guest Editor
Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain
Interests: computers for education; eLearning; health informatics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our society is experiencing a digitization revolution, with a drastic growth of Internet users and connected devices. Next-generation wireless networks should provide ultra-reliable, low-latency communication and intelligently control the internet of things (IoT) devices in real-time scenarios. Wireless network applications like in real-time traffic data, sensor reading from driverless cars, or Netflix entertainment recommendations generate extreme volumes of data that must be collected and processed in real time. These communication requirements and core intelligence can only be achieved through the integration of machine learning techniques in the wireless infrastructure and end-user devices.

In recent times, machine learning algorithms have gained significant interest in the area of wireless networking and communication. Machine learning-driven algorithms and models can enable wireless network analysis and resource management and be of advantage in handling the development in volume of communication and computation for evolving networking applications. Nevertheless, the application of machine learning techniques for heterogeneous wireless networks is still under debate. More endeavors are needed to link the gap between machine learning and wireless networking research.

The objective of this Special Issue is to explore recent advancements in machine learning concepts to address practical challenges in wireless networks. This Special Issue will bring together researchers and academics to present new results in network modeling and architecture, networking applications, security and privacy, resource management, load balancing, and various challenges related to the design for future wireless networks with the help of machine learning.

This Special Issue “Machine Learning for Wireless Networks – Recent Advances and Future Trends” will solicit papers on various disciplines, including but not limited to the following:

  • machine learning algorithms for network scheduling and control;
  • machine learning based energy-efficient networking techniques;
  • machine learning-based network resource allocation and optimization in wireless networks;
  • new supervised machine learning methods for wireless networks; new unsupervised machine learning methods for wireless networks;
  • novel reinforcement learning methods for wireless networks; new optimization methods for machine learning for wireless networks;
  • machine learning-based innovative intelligent computing architecture/algorithms for wireless networks;
  • machine learning based big data analytic frameworks for networking data;
  • machine learning-based intelligent routing algorithms for traffic management in wireless networks;
  • machine learning-based resource allocation for shared/virtualized networks using machine learning;
  • machine learning-based quality of service (QoS) management in wireless networks;
  • nature-inspired algorithms for wireless networks;
  • machine learning-based blockchain for wireless networks; machine learning-based node localization in wireless networks

Dr. Shankar Kathiresan 
Dr. Deepak Gupta
Dr. Gyanendra Prasad Joshi
Prof. Dr. Chi-Hua Chen
Dr. Vicente García-Díaz
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. Electronics 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 2000 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.

Keywords

  • machine learning
  • deep learning
  • wireless networks
  • optimization
  • routing
  • traffic management
  • big data
  • blockchain
  • energy efficiency

Published Papers (5 papers)

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Research

Article
OFDM PAPR Reduction via Time-Domain Scattered Sampling and Hybrid Batch Training of Synchronous Neural Networks
Electronics 2021, 10(14), 1708; https://doi.org/10.3390/electronics10141708 - 16 Jul 2021
Viewed by 611
Abstract
Peak-to-average power ratio (PAPR) reduction in multiplexed signals in orthogonal frequency division multiplexing (OFDM) systems has been a long-standing critical issue. Clipping and filtering (CF) techniques offer good performance in terms of PAPR reduction at the expense of a relatively high computational cost [...] Read more.
Peak-to-average power ratio (PAPR) reduction in multiplexed signals in orthogonal frequency division multiplexing (OFDM) systems has been a long-standing critical issue. Clipping and filtering (CF) techniques offer good performance in terms of PAPR reduction at the expense of a relatively high computational cost that is inherent in the repeated application of fast Fourier transform (FFT) operations. The ever-increasing demand for low-latency operation calls for the development of low-complexity novel solutions to the PAPR problem. To address this issue while providing an enhanced PAPR reduction performance, we propose a synchronous neural network (NN)-based solution to achieve PAPR reduction performance exceeding the limits of conventional CF schemes with lower computational complexity. The proposed scheme trains a neural network module using hybrid collections of samples from multiple OFDM symbols to arrive at a signal mapping with desirable characteristics. The benchmark NN-based approach provides a comparable performance to conventional CF. However, it can underfit or overfit due to its asynchronous nature which leads to increased out-of-band (OoB) radiations, and deteriorating bit error rate (BER) performance for high-order modulations. Simulations’ results demonstrate the effectiveness of the proposed scheme in terms of the achieved cubic metric (CM), BER, and OoB emissions. Full article
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Article
Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications
Electronics 2021, 10(8), 968; https://doi.org/10.3390/electronics10080968 - 19 Apr 2021
Cited by 2 | Viewed by 797
Abstract
Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techniques used in the IEEE 802.11 standard do not properly perform over vehicular [...] Read more.
Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techniques used in the IEEE 802.11 standard do not properly perform over vehicular channels. This is because vehicular communications are subject to non-stationary, time-varying, frequency-selective wireless channels. Therefore, the main goal of this work is the introduction of a new channel estimation and equalization technique based on a Semi-supervised Extreme Learning Machine (SS-ELM) in order to address the harsh characteristics of the vehicular channel and improve the performance of the communication link. The performance of the proposed technique is compared with traditional estimators, as well as state-of-the-art machine-learning-based algorithms over an urban scenario setup in terms of bit error rate. The proposed SS-ELM scheme outperformed the extreme learning machine and the fully complex extreme learning machine algorithms for the evaluated scenarios. Compared to traditional techniques, the proposed SS-ELM scheme has a very similar performance. It is also observed that, although the SS-ELM scheme requires the largest operation time among the evaluated techniques, its execution time is still far away from the latency requirements specified by the standard for safety applications. Full article
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Article
Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management
Electronics 2021, 10(7), 785; https://doi.org/10.3390/electronics10070785 - 26 Mar 2021
Cited by 15 | Viewed by 1373
Abstract
In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The [...] Read more.
In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Internet of Things (IoT) and it moves across the VANETs to analyze the traffic congestion status between the network nodes. The experimental analysis tests the effectiveness of DNN-IoT-BA in various machine or deep learning algorithms in VANETs. DNN-IoT-BA is validated through various network metrics, like packet delivery ratio, latency and packet error rate. The simulation results show that the proposed method provides lower energy consumption and latency than conventional methods to support real-time traffic conditions. Full article
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Article
Analysis of Interconnected Arrivals on Queueing-Inventory System with Two Multi-Server Service Channels and One Retrial Facility
Electronics 2021, 10(5), 576; https://doi.org/10.3390/electronics10050576 - 01 Mar 2021
Cited by 5 | Viewed by 792
Abstract
Present-day queuing inventory systems (QIS) do not utilize two multi-server service channels. We proposed two multi-server service channels referred to as T1S (Type 1 n-identical multi-server) and T2S (Type 2 m-identical multi-server). It includes an optional interconnected service connection [...] Read more.
Present-day queuing inventory systems (QIS) do not utilize two multi-server service channels. We proposed two multi-server service channels referred to as T1S (Type 1 n-identical multi-server) and T2S (Type 2 m-identical multi-server). It includes an optional interconnected service connection between T1S and T2S, which has a finite queue of size N. An arriving customer either uses the inventory (basic service or main service) for their demand, whom we call T1, or simply uses the service only, whom we call T2. Customer T1 will utilize the server T1S, while customer T2 will utilize the server T2S, and T1 can also get the second optional service after completing their main service. If there is a free server with a positive inventory, there is a chance that T1 customers may go to an infinite orbit whenever they find that either all the servers are busy or no sufficient stock. The orbital customer can request for T1S service under the classical retrial policy. Q(=Ss) items are replaced into the inventory whenever it falls into the reorder level s such that the inequality always holds n<s. We use the standard (s,Q) ordering policy to replace items into the inventory. By varying S and s, we investigate to find the optimal cost value using stationary probability vector ϕ. We used the Neuts Matrix geometric approach to derive the stability condition and steady-state analysis with R-matrix to find ϕ. Then, we perform the waiting time analysis for both T1 and T2 customers using Laplace transform technique. Further, we computed the necessary system characteristics and presented sufficient numerical results. Full article
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Article
Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control
Electronics 2021, 10(4), 466; https://doi.org/10.3390/electronics10040466 - 14 Feb 2021
Cited by 4 | Viewed by 930
Abstract
This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation [...] Read more.
This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control. Full article
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