Spectrum Sensing for Wireless Communication Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 7394
Special Issue Editors
Interests: signal processing; speech coding and recognition; biomedical signal processing; biometric identification; signal processing for telecommunications; wireless mesh network; voice transmission over IP; wireless sensor nodes
Special Issues, Collections and Topics in MDPI journals
Interests: reliability and availability analysis of distributed systems; wireless sensor networks; algorithms for management of opportunistic access in cognitive radio systems; algorithms for solution of non Markovian stochastic Petri net; phase type distributions; software performance evaluation techniques especially applied to distributed systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nowadays, billions of devices proliferate in the Internet of Things (IoT) ecosystem in an all-embracing perspective, including several sub-applications: smart cities, Industry 4.0, e-government, etc.
Most IoT devices operate in wireless mode and need to coexist with high-level mobile devices such as smartphones and tablets; therefore, they have to share the available telecommunication bandwidth to ensure their connectivity.
IoT-designed devices mainly operate in unlicensed and limited industrial, scientific, and medical (ISM) bands. With the proliferation of IoT devices, the ISM band is congested, and there is a need to explore the use of other bands.
In order to overcome this issue, software-defined radio (SDR) and cognitive radio (CR) technologies are considered important innovations in wireless communications and play an important role in 5G networks. In a CR scenario, communication transceivers can be divided into two categories: primary users (PUs) have the priority to use the spectrum band, while secondary users (SUs) are opportunistic users that can transmit on that band whenever it is left vacant by primary users.
Spectrum sensing (SS) allows SU devices to detect the presence or absence of a PU signal in the frequency band, and is classified under the more general problem of pattern recognition.
Recently, several approaches based on artificial intelligence (AI) and machine learning (ML) have been proposed in order to perform pattern recognition, which can also be performed by means of consolidated or innovative techniques based on classical statistical approaches.
This Special Issue aims to highlight advances in the development and comparisons of spectrum sensing techniques, specifically pointing out any support of IA and ML to solve this issue and its advantages/drawbacks compared to classical statistical approaches.
Potential topics include but are not limited to:
- Machine learning, deep learning or (deep) reinforcement learning algorithms for spectrum sensing;
- Statistical approaches to spectrum sensing;
- Artificial intelligence applied to spectrum sensing;
- Feature selection for spectrum sensing;
- Random sampling applied to spectrum sensing;
- Distributed algorithms applied to spectrum sensing;
- Federated learning and federated reinforcement learning for spectrum sensing;
- Performance analysis of spectrum sensing algorithms.
Dr. Salvatore Serrano
Dr. Scarpa Marco
Guest Editors
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Keywords
- spectrum sensing
- cognitive radio
- software-defined radio
- statistical classification
- artificial intelligence
- machine learning
- feature selection
- reinforcement learning
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