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Emerging Trends in Next-Generation mmWave Cognitive Radio Networks
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Emerging Trends in Next-Generation mmWave Cognitive Radio Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 217

Special Issue Editor

Dr. Gyanendra Prasad Joshi

E-Mail Website
Guest Editor
Department of Electronic and AI System Engineering, Kangwon National University, Samcheok, Republic of Korea
Interests: MAC; routing protocols for next-generation wireless networks; wireless sensor networks; cognitive radio networks; RFID systems; IoT; smart city; deep learning; digital convergence; CRN; 5G beyond and 6G; wireless networks; wireless security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Millimeter-wave (mmWave) communications and cognitive radio networks (CRNs) are at the forefront of next-generation wireless systems, offering high data rates and dynamic spectrum access. However, challenges such as rapid channel variations, mobility management, interference control, and efficient resource allocation remain critical for real-world deployment. This Special Issue aims to gather cutting-edge research on advanced techniques and innovative solutions for resource management and mobility support in mmWave CRNs. Topics of interest include, but are not limited to, spectrum sensing, beamforming, user association, handover mechanisms, AI-based optimization, and cross-layer design strategies.

We invite high-quality original research papers, review articles, and case studies addressing theoretical and practical aspects of mmWave CRNs.

Topics of interest include (but are not limited to) the following:

  • Dynamic spectrum access in mmWave CRNs.
  • Beamforming and directional antenna design for mobile CRNs.
  • Intelligent handover and mobility management techniques.
  • AI/ML-driven resource allocation and optimization.
  • Spectrum sensing and prediction in high-frequency bands.
  • Energy-efficient protocols for mmWave CRNs.
  • Joint MAC and PHY layer optimization.
  • Interference mitigation and coexistence strategies.
  • Edge computing and distributed intelligence in mmWave CRNs.
  • Security and privacy in cognitive mmWave communications.
  • Use cases in vehicular networks, smart cities, and IoT.

Dr. Gyanendra Prasad Joshi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors 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

  • millimeter-wave (mmWave)
  • cognitive radio networks (CRNs)
  • resource management
  • mobility support
  • beamforming
  • spectrum sensing
  • interference management
  • handover optimization
  • machine learning
  • 5G/6G
  • network slicing
  • energy efficiency

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Published Papers (1 paper)

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Research

18 pages, 1138 KiB  
Article
Intelligent Priority-Aware Spectrum Access in 5G Vehicular IoT: A Reinforcement Learning Approach
by Adeel Iqbal, Tahir Khurshaid and Yazdan Ahmad Qadri
Sensors 2025, 25(15), 4554; https://doi.org/10.3390/s25154554 - 23 Jul 2025
Abstract
Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning [...] Read more.
Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article
(This article belongs to the Special Issue Emerging Trends in Next-Generation mmWave Cognitive Radio Networks)
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