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Eng
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Signal Processing Challenges and Solutions in Mobile Communications
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Signal Processing Challenges and Solutions in Mobile Communications

A Special Issue of Eng (ISSN 2673-4117) belonging to the section "Electrical and Electronic Engineering".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1774

Editor

Dr. Mário Silva

E-Mail Website
Guest Editor
Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
Interests: telecommunications; wireless; transmission and reception schemes; networking; network security

Special Issue Information

Dear Colleagues,

It is with great pleasure that we invite you to contribute to this Special Issue of Eng, focused on "Signal Processing Challenges and Solutions in Mobile Communications". The continuous evolution of mobile networks, driven by the rollout of 5G and the burgeoning research into 6G, is constantly pushing the boundaries of wireless technology. These advancements bring forth a host of demanding challenges, particularly in the realm of signal processing, which is the cornerstone for achieving higher data rates, lower latency, massive connectivity, and superior energy and spectral efficiency.

This Special Issue aims to gather cutting-edge research that addresses the critical signal processing needs for current and future mobile communication systems. We welcome original contributions on new algorithms, innovative architectures, and practical implementations that tackle issues across various technologies. Key areas of interest include complex multi-antenna systems (such as Massive MIMO), the emerging field of reconfigurable intelligent surfaces (RISs) and large intelligent surfaces (LISs), interference management, channel estimation, resource allocation, and the integration of machine learning/AI for network optimization. By compiling high-quality theoretical and applied papers, this Issue will serve as a vital reference, charting the path towards the next generation of robust, secure, and ubiquitous mobile communications. We look forward to receiving your valuable manuscripts and fostering a fruitful exchange of ideas.

Dr. Mário Silva
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Eng 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 1400 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

  • signal processing for mobile communications
  • 5G and beyond (B5G)
  • 6G networks
  • massive MIMO
  • reconfigurable intelligent surfaces (RISs)
  • large intelligent surfaces (LISs)
  • integrated sensing and communication (ISAC)
  • channel estimation
  • interference management
  • AI/machine learning for wireless
  • spectral efficiency
  • energy efficiency
  • low latency communication
  • wireless security
  • resource allocation
  • digital signal processing (DSP)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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28 pages, 14867 KB  
Article
Dynamic Uplink Power Control for Cell-Free Massive MIMO
by Hussein A. Jasim, Mohd Fadlee A. Rasid, Fazirulhisyam Hashim and Syamsiah Mashohor
Eng 2026, 7(7), 357; https://doi.org/10.3390/eng7070357 - 22 Jul 2026
Viewed by 437
Abstract
Dynamic uplink power allocation is a critical challenge in cell-free massive MIMO (CF-mMIMO) networks, where distributed access points (APs) jointly serve multiple user equipment (UEs) under mobility, time-varying propagation conditions, and strong inter-user interference. Conventional optimization-based methods can improve fairness or spectral efficiency, [...] Read more.
Dynamic uplink power allocation is a critical challenge in cell-free massive MIMO (CF-mMIMO) networks, where distributed access points (APs) jointly serve multiple user equipment (UEs) under mobility, time-varying propagation conditions, and strong inter-user interference. Conventional optimization-based methods can improve fairness or spectral efficiency, but they often require repeated numerical solving and are usually designed for a specific objective. Learning-based approaches can reduce online decision time after training; however, their effectiveness depends strongly on the reward design and the selected operating objective. In response to these challenges, we propose a Deep Hybrid Intelligent (DHI) architecture designed to evaluate dynamic uplink power management within cell-free massive MIMO environments. The framework uses Soft Actor-Critic (SAC) learning to generate continuous uplink transmit-power decisions and evaluates objective-specific configurations for fairness, signal-to-interference-plus-noise ratio (SINR) improvement, and spectral-efficiency enhancement. In addition, three optimization-based strategies, namely max-min fairness, max-product SINR optimization, and max-sum-rate maximization, are incorporated to analyze the trade-off among fairness, signal quality, throughput, and computational cost. Limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) optimization is employed for the max-product and max-sum-rate objectives, while the max-min strategy is evaluated through a fairness-oriented feasibility procedure. Simulation results show that the fairness-oriented configuration achieves the highest Jain’s fairness index, reaching 0.989 at 120 access points, whereas the sum-rate-oriented configuration provides stronger SINR and user-rate performance. The results also indicate execution-time reductions of 51.6%, 83.7%, and 85.0% for the evaluated max-min, max-product, and max-sum-rate strategies, respectively, compared with conventional optimization-based implementations. These execution-time gains are accompanied by a clear performance trade-off: the max-min strategy provides the strongest fairness behavior, the max-sum-rate strategy improves total spectral efficiency and user-rate performance, and the max-product strategy offers a balanced operating point between collective SINR improvement and user-service balance. Therefore, the proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions. These results indicate that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks. Full article
(This article belongs to the Special Issue Signal Processing Challenges and Solutions in Mobile Communications)
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18 pages, 588 KB  
Article
Linear Canonical Transform Approach to the Characteristic Function of Real Random Variables
by Risnawati Ibnas, Mawardi Bahri, Nasrullah Bachtiar, Syamsuddin Toaha and Andi Tenri Ajeng Nur
Eng 2026, 7(1), 26; https://doi.org/10.3390/eng7010026 - 4 Jan 2026
Cited by 2 | Viewed by 757
Abstract
The present research demonstrates the utility of the linear canonical transform (LCT) in constructing the characteristic function of real random variables. We refer to this construction as the linear canonical characteristic function (LCCF). The proposed LCCF aims to address the limitations of the [...] Read more.
The present research demonstrates the utility of the linear canonical transform (LCT) in constructing the characteristic function of real random variables. We refer to this construction as the linear canonical characteristic function (LCCF). The proposed LCCF aims to address the limitations of the classical characteristic function in both theoretical and applied aspects. Using this approach, we investigate its properties, such as Hermitian symmetry, continuity, convolution, and derivatives, which are generalized forms of the classical characteristic function in the literature. Finally, we implement the obtained results by calculating several probability density functions in the LCCF domains. Full article
(This article belongs to the Special Issue Signal Processing Challenges and Solutions in Mobile Communications)
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