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Mathematics
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Computational Methods in Wireless Communications with Applications
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Computational Methods in Wireless Communications with Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 2051

Special Issue Editor

Dr. Peng Qin

E-Mail Website
Guest Editor
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
Interests: satellite internet & 6G; resource optimization in wireless networks; power system information communications; integration of sensing communication computing and AI; collaboration between computing power and electric power

Special Issue Information

Dear Colleagues,

This Special Issue seeks to showcase state-of-the-art and emerging computational techniques, such as non-convex optimization, stochastic geometry, and AI-based optimization, and to highlight their applications to the next-generation wireless networks, including space–air–ground-integrated network planning, beamforming design, and resource allocation. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: mathematical modeling for wireless communication systems; computational methods for space–air–ground-integrated network planning and sensing, communication, computing integration; non-convex and non-smooth optimization theories and algorithms for multi-antenna beamforming design; stochastic and distributed optimization frameworks for radio resource allocation; applications of AI-driven optimization techniques in wireless communications.

Over the past several decades, wireless communications have undergone remarkable transformations, evolving from the first-generation (1G) system in the 1980s to the anticipated sixth generation (6G) expected to enter commercialization around 2030. This technological progression has profoundly influenced society and become an indispensable component of modern life. Computational methods have long been regarded as essential tools for modeling, analyzing, and optimizing wireless networks. Nevertheless, the continual advancement of wireless systems has fundamentally altered the structure and characteristics of the associated optimization problems, presenting substantial challenges in their formulation and resolution. These shifts have created an urgent demand for, and simultaneously stimulated, the development of advanced computational methods, ranging from novel mathematical modeling frameworks to sophisticated optimization algorithms.

We are pleased to invite contributions that examine the pivotal role of computational methods in advancing wireless communications. This Special Issue seeks to showcase state-of-the-art and emerging computational techniques, such as non-convex optimization, stochastic geometry, and AI-based optimization, and to highlight their applications to the next-generation wireless networks, including space–air–ground-integrated network planning, beamforming design, and resource allocation.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: mathematical modeling for wireless communication systems; computational methods for space–air–ground-integrated network planning and sensing, communication, computing integration; non-convex and non-smooth optimization theories and algorithms for multi-antenna beamforming design; stochastic and distributed optimization frameworks for radio resource allocation; applications of AI-driven optimization techniques in wireless communications.

I look forward to receiving your contributions.

Dr. Peng Qin
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics 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.

Keywords

  • mathematical modeling for wireless communication systems
  • power control and beamforming design
  • spectrum management and resource allocation
  • space–air–ground-integrated network planning
  • sensing, communication, and computing integration
  • secure, trustworthy, and privacy-preserving wireless communication design
  • non-convex optimization methods
  • stochastic geometry methods
  • distributed optimization methods
  • AI-driven optimization methods

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Published Papers (5 papers)

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Research

24 pages, 671 KB  
Article
Statistical Indistinguishability in Multi-User Covert Communications Without Secret Information
by Jinyoung Lee, Junguk Park and Sangseok Yun
Mathematics 2026, 14(7), 1227; https://doi.org/10.3390/math14071227 - 7 Apr 2026
Viewed by 280
Abstract
This paper proposes a novel covert communication paradigm in which covertness emerges from network-induced structural uncertainty, eliminating the traditional reliance on pre-shared secret pilots in multi-user cooperative networks. Unlike conventional schemes that create information asymmetry through secret training sequences, we show that structural [...] Read more.
This paper proposes a novel covert communication paradigm in which covertness emerges from network-induced structural uncertainty, eliminating the traditional reliance on pre-shared secret pilots in multi-user cooperative networks. Unlike conventional schemes that create information asymmetry through secret training sequences, we show that structural uncertainty naturally arises from user selection in spatially dispersed networks. Specifically, we consider a public pilot aided system under a worst-case adversarial assumption where Willie possesses full knowledge of all individual channel state information (CSI) but remains uncertain about the active subset of cooperative users. We prove that this selection-induced structural uncertainty renders different transmission states statistically indistinguishable from Willie’s perspective, thereby forcing the optimal detector to reduce to an energy-based test. The proposed framework demonstrates that robust covertness can be achieved without secrecy-based coordination, providing a scalable and practically viable alternative to secret pilot management in future wireless networks. Full article
(This article belongs to the Special Issue Computational Methods in Wireless Communications with Applications)
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21 pages, 1404 KB  
Article
Deep Learning-Enhanced Hybrid Beamforming Design with Regularized SVD Under Imperfect Channel Information
by S. Pourmohammad Azizi, Amirhossein Nafei, Shu-Chuan Chen and Rong-Ho Lin
Mathematics 2026, 14(3), 509; https://doi.org/10.3390/math14030509 - 31 Jan 2026
Cited by 2 | Viewed by 349
Abstract
We propose a low-complexity hybrid beamforming method for massive Multiple-Input Multiple-Output (MIMO) systems that is robust to Channel State Information (CSI) estimation errors. These errors stem from hardware impairments, pilot contamination, limited training, and fast fading, causing spectral-efficiency loss. However, existing hybrid beamforming [...] Read more.
We propose a low-complexity hybrid beamforming method for massive Multiple-Input Multiple-Output (MIMO) systems that is robust to Channel State Information (CSI) estimation errors. These errors stem from hardware impairments, pilot contamination, limited training, and fast fading, causing spectral-efficiency loss. However, existing hybrid beamforming solutions typically either assume near-perfect CSI or rely on greedy/black-box designs without an explicit mechanism to regularize the error-distorted singular modes, leaving a gap in unified, low-complexity, and theoretically grounded robustness. We unfold the Alternating Direction Method of Multipliers (ADMM) into a trainable Deep Learning (DL) network, termed DL-ADMM, to jointly optimize Radio-Frequency (RF) and baseband precoders and combiners. In DL-ADMM, the ADMM update mappings are learned (layer-wise parameters and projections) to amortize the joint RF/baseband optimization, whereas Regularized Singular Value Decomposition (RSVD) acts as an analytical regularizer that reshapes the observed channel’s singular values to suppress noise amplification under imperfect CSI. RSVD is integrated to stabilize singular modes and curb noise amplification, yielding a unified and scalable design. For σe2=0.1, the proposed DL-ADMM-Reg achieves approximately 8–11 bits/s/Hz higher spectral efficiency than Orthogonal Matching Pursuit (OMP) at Signal-to-Noise Ratio (SNR) =20–40 dB, while remaining within <1 bit/s/Hz of the digital-optimal benchmark across both (Nt,Nr)=(32,32) and (64,64) settings. Simulations confirm higher spectral efficiency and robustness than OMP and Adaptive Phase Shifters (APSs). Full article
(This article belongs to the Special Issue Computational Methods in Wireless Communications with Applications)
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17 pages, 4010 KB  
Article
Blind Channel Estimation Based on K-Means Clustering with Resource Grouping in Fading Channel
by Yumin Kim, Jonghyun Bang and Taehyoung Kim
Mathematics 2026, 14(3), 400; https://doi.org/10.3390/math14030400 - 23 Jan 2026
Viewed by 395
Abstract
This paper proposes a novel blind channel estimation method based on K-means clustering algorithm with efficient time–frequency resource grouping. Existing K-means-based blind channel estimation techniques assume that received symbols within the coherence time and coherence bandwidth experience the same channel response, which is [...] Read more.
This paper proposes a novel blind channel estimation method based on K-means clustering algorithm with efficient time–frequency resource grouping. Existing K-means-based blind channel estimation techniques assume that received symbols within the coherence time and coherence bandwidth experience the same channel response, which is not valid under fading channel with severe time variation or frequency selectivity. To overcome this limitation, this paper proposes an efficient time–frequency resource grouping pattern selection algorithm. The proposed method introduces the concept of an effective number of data symbols, which eliminates patterns that are computationally expensive yet performance-irrelevant, thereby reducing the search space compared to exhaustive search. Two strategies are applied: Time-main, which prioritizes grouping in the time domain, and Freq-main, which prioritizes grouping in the frequency domain. Simulation results demonstrate that the proposed method consistently outperforms conventional and fixed-pattern approaches across various channel conditions. Full article
(This article belongs to the Special Issue Computational Methods in Wireless Communications with Applications)
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20 pages, 1244 KB  
Article
Learning-Based Cost-Minimization Task Offloading and Resource Allocation for Multi-Tier Vehicular Computing
by Shijun Weng, Yigang Xing, Yaoshan Zhang, Mengyao Li, Donghan Li and Haoting He
Mathematics 2026, 14(2), 291; https://doi.org/10.3390/math14020291 - 13 Jan 2026
Viewed by 270
Abstract
With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of [...] Read more.
With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of system QoS as well as user QoE. In the meantime, to build the environmentally harmonious transportation system and green city, the energy consumption of data processing has become a new concern in vehicles. Moreover, due to the fast movement of IoV, traditional GSI-based methods face the dilemma of information uncertainty and are no longer applicable. To address these challenges, we propose a T2VC model. To deal with information uncertainty and dynamic offloading due to the mobility of vehicles, we propose a MAB-based QEVA-UCB solution to minimize the system cost expressed as the sum of weighted latency and power consumption. QEVA-UCB takes into account several related factors such as the task property, task arrival queue, offloading decision as well as the vehicle mobility, and selects the optimal location for offloading tasks to minimize the system cost with latency energy awareness and conflict awareness. Extensive simulations verify that, compared with other benchmark methods, our approach can learn and make the task offloading decision faster and more accurately for both latency-sensitive and energy-sensitive vehicle users. Moreover, it has superior performance in terms of system cost and learning regret. Full article
(This article belongs to the Special Issue Computational Methods in Wireless Communications with Applications)
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17 pages, 910 KB  
Article
BER-Constrained Power Allocation for Uplink NOMA Systems with One-Bit ADCs
by Tae-Kyoung Kim
Mathematics 2025, 13(24), 4039; https://doi.org/10.3390/math13244039 - 18 Dec 2025
Cited by 1 | Viewed by 386
Abstract
This study investigates bit error rate (BER)-constrained power allocation for uplink non-orthogonal multiple access (NOMA) systems in which a base station employs one-bit analog-to-digital converters. Although one-bit quantization significantly reduces hardware costs and receiver power consumption, it also introduces severe nonlinear distortions that [...] Read more.
This study investigates bit error rate (BER)-constrained power allocation for uplink non-orthogonal multiple access (NOMA) systems in which a base station employs one-bit analog-to-digital converters. Although one-bit quantization significantly reduces hardware costs and receiver power consumption, it also introduces severe nonlinear distortions that degrade detection performance. To address this challenge, a pairwise error probability expression is first derived for the one-bit quantized uplink NOMA model, from which an analytical upper bound on the BER is obtained. Based on this characterization, a fairness-driven max–min power allocation strategy is formulated to minimize the BER of the worst-performing user. A closed-form solution for the optimal power allocation is obtained under binary phase-shift keying (BPSK) signaling. Simulation results verify the tightness of the analytical BER bound and demonstrate that the proposed power allocation scheme provides noticeable BER improvements that compensate for the performance degradation caused by one-bit quantization. Full article
(This article belongs to the Special Issue Computational Methods in Wireless Communications with Applications)
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