Computational Methods for Cognitive Radio and Spectrum Sensing

Dear Colleagues,

Cognitive radio has emerged as a promising paradigm to address the ever-increasing demand for wireless spectrum, which is driven by the proliferation of mobile devices, Internet of Things (IoT), and next-generation communication systems. Spectrum sensing—the task of detecting vacant frequency bands and characterizing interference—is a fundamental component of cognitive radio networks. However, the dynamic, heterogeneous, and increasingly congested nature of the radio environment poses significant challenges for accurate, fast, and robust spectrum awareness. Traditional sensing techniques often fall short in low signal-to-noise ratio (SNR) conditions, wideband scenarios, or the presence of fading and hidden node problems. Computational methods, ranging from statistical signal processing and machine learning to optimization and game theory, have become indispensable tools for advancing cognitive radio and spectrum sensing capabilities.

This Special Issue is dedicated to showcasing recent advances in computational methods applied to cognitive radio and spectrum sensing. We invite contributions that develop novel algorithms, models, or frameworks to improve the efficiency, reliability, and intelligence of spectrum access and management. Topics of interest include, but are not limited to, energy detection enhancements, cooperative sensing, compressed sensing for wideband spectrum monitoring, deep learning and reinforcement learning for spectrum prediction and decision making, adversarial robustness in sensing, and real-time implementations on software-defined radio (SDR) platforms. Papers may report original research, present methodological innovations, provide comprehensive reviews, or offer forward-looking perspectives.

Specific methods and fields of application include, but are not limited to, the following:

• Energy detection and matched filter techniques;
• Cyclostationary feature detection;
• Compressed and sub‑Nyquist sampling methods;
• Cooperative and distributed spectrum sensing;
• Game theory and optimization for spectrum sharing;
• Machine learning (support vector machines, neural networks, deep learning, federated learning);
• Reinforcement learning for dynamic spectrum access;
• Bayesian and sequential detection methods;
• Signal classification and modulation recognition;
• Multi‑band joint sensing and decision fusion;
• Robust sensing against malicious attacks (primary user emulation, spectrum sensing data falsification);
• Hardware‑aware and low‑power sensing algorithms;
• Real‑time implementation on SDR and FPGA platforms.

Dr. Jun Wu
Dr. Jian Dang
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 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. Computation 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 1800 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.

• cognitive radio
• spectrum sensing
• wideband sensing
• compressed sensing
• cooperative sensing
• machine learning
• deep learning
• reinforcement learning
• dynamic spectrum access
• software-defined radio