Computational Methods in Wireless Communication

Dear Colleagues,

Computational Methods in Wireless Communication is a specialized field that focuses on the development of advanced computational techniques for the design, analysis, and optimization of wireless communication systems. This domain plays a critical role in enhancing the performance, efficiency, and reliability of modern wireless networks by leveraging sophisticated mathematical models, optimization algorithms, and machine learning methodologies. This interdisciplinary domain is indispensable for the evolution of future wireless communication technologies, underpinning innovations in ultra-reliable, high-speed, and intelligent networking systems.

We welcome submissions in the following research areas:

1. Optimization Techniques

• Application of advanced mathematical optimization frameworks to address complex challenges such as power allocation, spectrum management, and beamforming;
• Utilization of methodologies including Convex and Non-Convex Optimization, as well as Metaheuristic Algorithms (e.g., Genetic Algorithms, Particle Swarm Optimization, etc.);

2. Machine Learning and AI-Driven Approaches

• Integration of deep learning and reinforcement learning techniques for intelligent signal detection, predictive channel modelling, and dynamic network traffic management;
• Implementation of cutting-edge architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Federated Learning to enhance wireless system intelligence.

3. Theoretical Modelling and Stochastic Optimization

• Development of probabilistic frameworks for characterizing wireless channel behaviour and optimizing network performance under uncertainty;
• Deployment of techniques such as Markov Decision Processes (MDPs), Bayesian Inference, and Stochastic Control for adaptive resource allocation.

4. Advanced Signal Processing Algorithms

• Design and implementation of sophisticated signal processing methodologies for next-generation wireless technologies, including MIMO, OFDM, and adaptive beamforming;
• Exploration of techniques such as compressive sensing, blind source separation, and advanced filtering methods to enhance spectral efficiency and interference mitigation.

5. Quantum Computing and Next-Generation Computational Paradigms

• Investigation of quantum computing’s potential to revolutionize wireless network optimization through quantum algorithms and quantum-assisted signal processing;
• Development of low latency computing frameworks for mission-critical and high-speed wireless communication scenarios.

Dr. Taehyoung Kim
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.

• next-generation wireless communication systems
• optimization techniques
• convex/non-convex optimization
• artificial intelligence applications
• deep learning
• large language model
• stochastic optimization
• signal processing
• mathematical modelling
• statistics applications
• quantum computing