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Wireless Communications: Signal Processing Perspectives

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

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 12582

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Prof. Dr. Sébastien Roy

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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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Prof. Dr. Julian Cheng

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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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jean-Yves Chouinard

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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

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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

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Related Special Issue

Wireless Communications: Signal Processing Perspectives, 2nd Edition in Entropy

Published Papers (10 papers)

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Research

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23 pages, 3783 KiB

Open AccessArticle

Design of Covert Communication Waveform Based on Phase Randomization

by Wenjie Zhou, Zhenyong Wang, Jun Shi and Qing Guo

Entropy 2025, 27(5), 520; https://doi.org/10.3390/e27050520 - 13 May 2025

Viewed by 177

Abstract

Covert wireless communication is designed to securely transmit hidden information between two devices. Its primary objective is to conceal the existence of transmitted data, rendering communication signals difficult for unauthorized parties to detect, intercept, or decipher during transmission. In this paper, we propose a Noise-like Multi-Carrier Random Phase Communication System (NRPCS) to enhance covert wireless communication by significantly complicating the detection and interception of transmitted signals. The proposed system utilizes bipolar modulation and Cyclic Code Shift Keying (CCSK) modulation, complemented by a random sequence generation mechanism, to increase the randomness and complexity of the transmitted signals. A mathematical model of the NRPCS waveform is formulated, and detailed analyses of the system’s time-domain basis functions, correlation properties, and power spectral characteristics are conducted to substantiate its noise-like behavior. Simulation results indicate that, compared to traditional fixed-frequency transmission methods, NRPCS substantially improves both the Low Probability of Detection (LPD) and the Low Probability of Interception (LPI). Further research results demonstrate that unauthorized eavesdroppers are unable to effectively demodulate signals without knowledge of the employed modulation scheme, thus significantly enhancing the overall security of communication. Full article

(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)

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22 pages, 3503 KiB

Open AccessArticle

Beamspace Spatial Smoothing MUSIC DOA Estimation Method Using Dynamic Metasurface Antenna

by Lilong Hou, Liang Jin, Kaizhi Huang, Shuaifang Xiao, Yangming Lou and Yajun Chen

Entropy 2025, 27(4), 335; https://doi.org/10.3390/e27040335 - 24 Mar 2025

Viewed by 385

Abstract

The Direction-of-Arrival (DOA) estimation method using traditional array antennas cannot dynamically adjust the observation angle range based on the Region of Interest (ROI), which leads to limited estimation accuracy and high computational complexity. To address the above issue, this paper proposes a Beamspace Spatial Smoothing MUltiple SIgnal Classification (BSS-MUSIC) DOA estimation method using a Dynamic Metasurface Antenna (DMA). Specifically, we propose a new DMA model with a single RF chain and exploit its flexibility to design a time-division data reception scheme. Based on this scheme, we dynamically select the ROI and increase the beam density in the ROI with an appropriate number of beam patterns. Next, a BSS algorithm is proposed to decohere the multipath signals in beamspace without reverting to the element space. Subsequently, we convert the 2D DOA estimation into two 1D beamspace MUSIC DOA estimations. After pairing the elevation and azimuth angles, the complex gains of each path are derived. Simulation results show that the proposed method can achieve higher estimation accuracy with lower computational complexity. Full article

(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)

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17 pages, 660 KiB

Open AccessArticle

User-Centric Cell-Free Massive Multiple-Input-Multiple-Output System with Noisy Channel Gain Estimation and Line of Sight: A Beckmann Distribution Approach

by Danilo B. T. Almeida, Marcelo S. Alencar, Wamberto J. L. Queiroz, Rafael M. Duarte and Francisco Madeiro

Entropy 2025, 27(3), 223; https://doi.org/10.3390/e27030223 - 21 Feb 2025

Viewed by 505

Abstract

This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the k-th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel state information (CSI) estimation and line of sight (LoS). Additionally, it is shown how the Beckmann probability density function (PDF) can be used to derive the PDF and the cumulative density function (CDF) of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the UC CF k-th UE, followed by applications in the ergodic capacity (EC) and outage probability (OP) expression derivations. It is shown that, regardless of the type of distribution considered for the channel gain between each access point (AP) and UE links, the effective gain presents a Beckmann distribution. Full article

(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)

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20 pages, 572 KiB

Open AccessArticle

Channel Estimation for Massive MIMO Systems via Polarized Self-Attention-Aided Channel Estimation Neural Network

by Shuo Yang, Yong Li, Lizhe Liu, Jing Xia, Bin Wang and Xingjian Li

Entropy 2025, 27(3), 220; https://doi.org/10.3390/e27030220 - 21 Feb 2025

Viewed by 774

Abstract

Research on deep learning (DL)-based channel estimation for massive multiple-input multiple-output (MIMO) communication systems has attracted considerable interest in recent years. In this paper, we propose a DL-assisted channel estimation algorithm that transforms the original channel estimation problem into an image denoising problem, contrasting it with traditional experience-based channel estimation methods. We establish a new polarized self-attention-aided channel estimation neural network (PACE-Net) to achieve efficient channel estimation. This approach addresses the limitations of the conventional methods, particularly their low accuracy and high computational complexity. In addition, we construct a channel dataset to facilitate the training and testing of PACE-Net. The simulation results show that the proposed DL-assisted channel estimation algorithm has better normalization mean square error (NMSE) performance compared with the traditional algorithms and other DL-assisted algorithms. Furthermore, the computational complexity of the proposed DL-assisted algorithm is significantly lower than that of the traditional minimum mean square error (MMSE) channel estimation algorithm. Full article

(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)

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24 pages, 2460 KiB

Open AccessArticle

An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs

by Zhen Wang and Jin Duan

Entropy 2025, 27(2), 118; https://doi.org/10.3390/e27020118 - 24 Jan 2025

Cited by 1 | Viewed by 647

Abstract

Clustering-based routing techniques are key to significantly extending the lifetime of wireless sensor networks (WSNs). However, these approaches often do not address the common hotspot issue in multi-hop WSNs. To overcome this challenge and enhance network lifespan, this study presents FQ-UCR, a hybrid approach that merges unequal clustering based on fuzzy logic (FL) with routing optimized through Q-learning. In FQ-UCR, a tentative CH employs a fuzzy inference system (FIS) to compute its probability of being selected as the final CH. By using the Q-learning algorithm, the best forwarding cluster head (CH) is chosen to construct the data transmission route between the CHs and the base station (BS). The approach is extensively evaluated and compared with protocols like EEUC and CHEF. Simulation results demonstrate that FQ-UCR improves energy efficiency across all nodes, significantly extends network lifetime, and effectively alleviates the hotspot issue. Full article

(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)

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14 pages, 475 KiB

Open AccessArticle

Precise Error Performance of BPSK Modulated Coherent Terahertz Wireless LOS Links with Pointing Errors

by Mingbo Niu, Ruihang Ji, Hucheng Wang and Huan Liu

Entropy 2024, 26(8), 706; https://doi.org/10.3390/e26080706 - 20 Aug 2024

Viewed by 1088

Abstract

One of the key advantages of terahertz (THz) communication is its potential for energy efficiency, making it an attractive option for green communication systems. Coherent THz transmission technology has recently been explored in the literature. However, there exist few error performance results for such a wireless link employing coherent THz technology. In this paper, we explore a comprehensive terrestrial channel model designed for wireless line-of-sight communication using THz frequencies. The performance of coherent THz links is analyzed, and it is found to be notably affected by two significant factors, atmospheric turbulence and pointing errors. These could occur between the terahertz transmitter and receiver in terrestrial links. The exact and asymptotic solutions are derived for bit error rate and interrupt probability for binary phase-shift keying coherent THz systems, respectively, over log-normal and Gamma–Gamma turbulent channels. The asymptotic outage probability analysis is also performed. It is shown that the presented results offer a precise estimation of coherent THz transmission performance and its link budget. Full article

(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)

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19 pages, 3471 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 3730

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 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

Open AccessArticle

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 1497

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1283

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 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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Review

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74 pages, 3722 KiB

Open AccessReview

Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 2: Semi-Blind Receivers

by Gérard Favier and Danilo Sousa Rocha

Entropy 2024, 26(11), 937; https://doi.org/10.3390/e26110937 - 31 Oct 2024

Viewed by 943

Abstract

Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave communications, and, more recently, communications assisted using intelligent reflecting surfaces (IRSs), and unmanned aerial vehicles (UAVs). In a companion paper, we provided an overview of cooperative communication systems from a tensor modeling perspective. The objective of the present paper is to provide a comprehensive tutorial on semi-blind receivers for MIMO one-way two-hop relay systems, allowing the joint estimation of transmitted symbols and individual communication channels with only a few pilot symbols. After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). Some new variants of nested models are introduced. Uniqueness and identifiability conditions, depending on the algorithm used to estimate the parameters of these models, are established. Two families of algorithms are presented: iterative algorithms based on alternating least squares (ALS) and closed-form solutions using Khatri–Rao and Kronecker factorization methods, which consist of SVD-based rank-one matrix or tensor approximations. In a second part of the paper, the overview of cooperative communication systems is completed before presenting several two-hop relay systems using different codings and configurations in terms of relaying protocol (AF/DF) and channel modeling. The aim of this presentation is firstly to show how these choices lead to different nested tensor models for the signals received at destination. Then, by capitalizing on these models and their correspondence with the generic models studied in the first part, we derive semi-blind receivers to jointly estimate the transmitted symbols and the individual communication channels for each relay system considered. In a third part, extensive Monte Carlo simulation results are presented to compare the performance of relay systems and associated semi-blind receivers in terms of the symbol error rate (SER) and channel estimate normalized mean-square error (NMSE). Their computation time is also compared. Finally, some perspectives are drawn for future research work. Full article

(This article belongs to the Special Issue Wireless Communications: Signal Processing Perspectives)

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