Artificial Intelligence-Enabled Spectrum Sensing and Sharing for Future Communication Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".
Deadline for manuscript submissions: 15 February 2027 | Viewed by 55
Editors
Interests: spectrum sensing; dynamic spectrum sharing; cognitive radio; 6G wireless networks
Interests: 5G networks; IoT networks; network security; machine-to-machine protocols; routing algorithms; network simulations/emulations; physical layer security; machine learning; graph theory; edge/fog computing
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Special Issue Information
Dear Colleagues,
Next-generation wireless networks, such as 6G, are expected to support massive connectivity and ubiquitous coverage, and highly demand requirements in terms of latency and efficiency, with the latter encompassing not only energy consumption but also spectrum utilization. Indeed, conventional static spectrum allocation policies are becoming increasingly inefficient, as some licensed bands remain underutilized while others experience severe congestion. Dynamic Spectrum Sharing (DSS) and Cognitive Radio Networks (CRNs) therefore represent promising paradigms to address this issue by enabling real-time spectrum access.
In parallel, Artificial Intelligence (AI) is expected to play a fundamental role in several critical 6G functions, including signal detection, multiple access, and resource allocation. By learning from data, AI-driven approaches can support key DSS tasks, such as spectrum sensing (SS) and spectrum allocation, enabling adaptation to dynamic environments, real-time decision-making, signal classification, spectrum availability prediction, and spectrum utilization optimization without relying on predefined analytical models.
This Special Issue therefore aims to collect original research and review articles on recent advances in the field of AI-enabled spectrum sensing and sharing for future wireless networks.
Potential topics include, but are not limited to, the following:
- Reinforcement Learning (RL) for distributed Cooperative Spectrum Sensing (CSS);
- Single-Agent Reinforcement Learning (SARL) and Multi-Agent Reinforcement Learning (MARL) for DSS and CSS optimization;
- Federated Learning (FL) for communication-efficient distributed SS across secondary users;
- Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) for SS, signal detection, and noise estimation;
- Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and CNN-LSTM architectures for spectrum prediction, primary user (PU) activity recognition;
- Stacked Autoencoders (SAEs) and Variational Autoencoders (VAEs) for modulation classification and robust detection in low-SNR and non-Gaussian environments;
- Graph Neural Network (GNN)-based models for energy-efficient distributed cooperative sensing in large-scale CRNs;
- Large Language Models (LLMs) for PU activity forecasting and reduction in unnecessary sensing operations.
Dr. Andrea Tani
Dr. Tommaso Pecorella
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- spectrum sensing
- dynamic spectrum sharing
- cognitive radio
- 6G wireless networks
- reinforcement learning
- federated learning
- deep learning
- graph neural networks
- large language models
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