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Wireless Communications: Signal Processing Perspectives

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 17 July 2024 | Viewed by 3885

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Interests: array processing; MIMO systems; massive MIMO; signal processing; wireless communications; radio propagation and channel models

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Guest Editor
School of Engineering, The University of British Columbia, Kelowna, BC V1Y 8L6, Canada
Interests: wireless digital communications theory; optical wireless communications theory; 5G wireless networks and beyond; quantum information processing and communications; machine learning; deep learning; wireless location technology

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Laval University Québec, QC G1V 0A6, Canada
Interests: broadband wireless communication systems; error-correcting codes; information encryption; distributed source coding; high-resolution wide-swath synthetic aperture radar processing

Special Issue Information

Dear Colleagues,

We are in the digital age. Today, humans live and work within an increasingly pervasive digital fabric comprised of multitudes of heterogeneous computing nodes acting as hubs in worldwide interconnected networks of various types. The wireless portion of these networks is of paramount importance, since it enables mobility, connectedness through various portable devices, and machine-to-machine communications in the so-called Internet of Things (IoT). In addition to wireless LANs (WiFi), IoT communications (through LoRa or other radio interfaces), and satellite, there are more than 10 billion active cell phone connections worldwide, which is more than the number of humans.

However, high-bandwidth communication over the air is notoriously difficult, given the fact that the EM spectrum is a limited and congested resource. The relentless evolution of wireless has been made possible through increasingly efficient spectrum usage, thanks to sophisticated spectrum processing, especially by leveraging the spatial dimension. Indeed, staggering gains in spectrum efficiency since 2005 have been achieved through the improved integration of adaptive antenna arrays and the MIMO concept. In fact, massive MIMO is a keystone technology of 5G cellular.

Going forward, data volume will continue to increase rapidly, as will the logistic complexity of wireless networks, which are becoming increasingly heterogeneous and unpredictable. Furthermore, there is a push for ultra-reliable and low-latency communications, which imposes further constraints on the wireless infrastructure. In fact, the need for extremely low-latency responses implies that much of the processing will be pushed towards the network edge, thus radically changing the nature of the wireless domain and its cybersecurity aspects.

Meeting these challenges requires continuous innovation in the signal processing domain to continue leveraging the spatial dimension with increasing efficiency in conjunction with other techniques to yield the desirable traits of ultra-reliability, ultra-low latency, self-organization, scalability, and adaptability to changing environments, operating conditions and network demands. The scope of this Special Issue covers such innovations and the underlying challenges.

We therefore welcome unpublished original papers and comprehensive surveys on the above theme, specifically on the following, non-exhaustive, list of topics:

  • Beamforming, diversity, and MIMO techniques, including for IoT and energy efficiency;
  • Massive MIMO;
  • Cell-free and clustered cell-free MIMO;
  • Antenna selection and antenna subset selection in large arrays;
  • Reconfigurable intelligent surfaces (RISs);
  • The use of unmanned aerial vehicles (UAVs) for wireless networking;
  • Channel estimation and its impact on network performance;
  • Physical-layer security;
  • Relaying and cooperation;
  • Self-organizing networks;
  • Energy efficiency in wireless networks;
  • Machine learning applied to any of the above, especially within some formal mathematical framework;
  • Sound analytical signal processing techniques and/or information theoretic framework applied to any of the above.

Prof. Dr. Sébastien Roy
Prof. Dr. Julian Cheng
Prof. Dr. Jean-Yves Chouinard
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. Entropy is an international peer-reviewed open access monthly 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.

Keywords

  • antenna selection
  • massive MIMO
  • reconfigurable intelligent surfaces (RISs)
  • physical-layer security
  • cell-free MIMO
  • green communications
  • machine learning
  • unmanned aerial vehicles (UAVs)
  • relaying and cooperation
  • self-organization

Published Papers (3 papers)

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Research

19 pages, 3471 KiB  
Article
Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
by Hua Fu, Hao Dong, Jian Yin and Linning Peng
Entropy 2024, 26(1), 38; https://doi.org/10.3390/e26010038 - 29 Dec 2023
Viewed by 1558
Abstract
The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of [...] Read more.
The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform. Full article
(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)
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17 pages, 867 KiB  
Article
Performance Analysis of Artificial Noise-Assisted Location-Based Beamforming in Rician Wiretap Channels
by Hua Fu, Xiaoyu Zhang and Linning Peng
Entropy 2023, 25(12), 1626; https://doi.org/10.3390/e25121626 - 6 Dec 2023
Viewed by 854
Abstract
This paper studies the performance of location-based beamforming with the presence of artificial noise (AN). Secure transmission can be achieved using the location information of the user. However, the shape of the beam depends on the number of antennas used. When the scale [...] Read more.
This paper studies the performance of location-based beamforming with the presence of artificial noise (AN). Secure transmission can be achieved using the location information of the user. However, the shape of the beam depends on the number of antennas used. When the scale of the antenna array is not sufficiently large, it becomes difficult to differentiate the performance between the legitimate user and eavesdroppers nearby. In this paper, we leverage AN to minimize the area near the user with eavesdropping risk. The impact of AN is considered for both the legitimate user and the eavesdropper. Closed-form expressions are derived for the expectations of the signal to interference plus noise ratios (SINRs) and the bit error rates. Then, a secure beamforming scheme is proposed to ensure a minimum SINR requirement for the legitimate user and minimize the SINR of the eavesdropper. Numerical results show that, even with a small number of antennas, the proposed beamforming scheme can effectively degrade the performance of eavesdroppers near the legitimate user. Full article
(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)
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16 pages, 341 KiB  
Article
Nested Variational Chain and Its Application in Massive MIMO Detection for High-Order Constellations
by Qiwei Wang
Entropy 2023, 25(12), 1621; https://doi.org/10.3390/e25121621 - 5 Dec 2023
Viewed by 729
Abstract
Multiple input multiple output (MIMO) technology necessitates detection methods with high performance and low complexity; however, the detection problem becomes severe when high-order constellations are employed. Variational approximation-based algorithms prove to deal with this problem efficiently, especially for high-order MIMO systems. Two typical [...] Read more.
Multiple input multiple output (MIMO) technology necessitates detection methods with high performance and low complexity; however, the detection problem becomes severe when high-order constellations are employed. Variational approximation-based algorithms prove to deal with this problem efficiently, especially for high-order MIMO systems. Two typical algorithms named Gaussian tree approximation (GTA) and expectation consistency (EC) attempt to approximate the true likelihood function under discrete finite-set constraints with a new distribution by minimizing the Kullback–Leibler (KL) divergence. As the KL divergence is not a true distance measure, ’exclusive’ and ’inclusive’ KL divergences are utilized by GTA and EC, respctively, demonstrating different performances. In this paper, we further combine the two asymmetric KL divergences in a nested way by proposing a generic algorithm framework named nested variational chain. Acting as an initial application, a MIMO detection algorithm named Gaussian tree approximation expectation consistency (GTA-EC) can thus be presented along with its alternative version for better understanding. With less computational burden compared to its counterparts, GTA-EC is able to provide better detection performance and diversity gain, especially for large-scale high-order MIMO systems. Full article
(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)
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