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Spectrum Sensing and Access Technologies for Drones

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

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 711

Special Issue Editors

School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: spectrum sensing and management; drone trajectory and energy efficiency; cognitive radio; network security; wireless sensor network; machine learning

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Guest Editor
School of Software Engineering, Tongji University, Shanghai 200092, China
Interests: wireless communication; sensing positioning; data security and privacy computing; heterogeneous network fusion

Special Issue Information

Dear Colleagues,

With the increasing deployment of sensors and drones in various sectors such as logistics, agriculture, surveillance, and emergency response, the demand for efficient spectrum sensing and access technologies is growing rapidly. The research activity brings together leading experts in the field to share their insights and research findings, thereby promoting advancements in drone communications and spectrum management. The contributions from this Special Issue will have a significant impact on the development of future sensor systems and their broader integration into drone communications.

This Special Issue aims to provide a comprehensive platform for researchers, engineers, and practitioners to present and discuss the latest advancements in spectrum sensing and access technologies for drones. This publication is dedicated to original research articles, review papers, and case studies that cover, but are not limited to, the following topics:

  • Advanced signal processing algorithms for spectrum sensing;
  • Collaborative spectrum sensing among multiple drones;
  • Cognitive radio-based spectrum sensing for dynamic environments;
  • Efficient channel access protocols for drone networks;
  • Multi-drone coordination for spectrum sharing;
  • Integration of drone communications with existing wireless networks;
  • Dynamic spectrum allocation and optimization for drones;
  • Real-time spectrum monitoring and management systems;
  • Interference mitigation techniques for drone communications;
  • Spectrum sensing and access technologies in drone-based delivery systems;
  • Communications for drone-based surveillance and monitoring;
  • Integration of drones in cellular and beyond-5G networks;
  • Spectrum regulations and policies for drone communications;
  • Standardization efforts for spectrum sensing and access technologies;
  • Security and privacy considerations in drone communications.

Dr. Jun Wu
Dr. Yaping Zhu
Guest Editors

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Keywords

  • drone communications
  • cooperative spectrum sensing
  • spectrum resource allocation
  • data fusion algorithm
  • dynamic spectrum management
  • spectrum sharing
  • flight trajectory
  • security and privacy
  • multi-drone coordination

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Published Papers (1 paper)

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Research

15 pages, 4891 KiB  
Article
Blind Recognition Algorithm of Multi-Carrier Composite Modulation Signal Based on Multi-Dimensional Time-Frequency Superimposed Spectrum
by Shoubin Wang, Huan Li, Xiaolong Zhang, Hao Jiang and Lei Shen
Sensors 2025, 25(13), 4007; https://doi.org/10.3390/s25134007 - 27 Jun 2025
Viewed by 258
Abstract
The existing multi-carrier composite modulation recognition methods have failed to effectively integrate inner and outer modulation characteristics, thereby limiting the potential for improving recognition performance under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a multi-carrier composite signal modulation [...] Read more.
The existing multi-carrier composite modulation recognition methods have failed to effectively integrate inner and outer modulation characteristics, thereby limiting the potential for improving recognition performance under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a multi-carrier composite signal modulation recognition algorithm based on a multi-dimensional time-frequency superimposed spectrum (MD-TFSS) with integrated inner and outer features, which can recognize composite modulation signals in the set {BPSK-PM, QPSK-PM, BPSK-QPSK-PM, BPSK-BPSK-PM, QPSK-QPSK-PM}. The proposed method constructs a dual spectrum through multiplying an inner modulation spectrum and a squared spectrum, then combines the inner modulation dual spectrum with the outer modulation time-frequency diagram in dual-channel mode to form MD-TFSS features. Based on the MD-TFSS, a blind recognition algorithm is implemented using the dual-channel input ECA-ResNet18 (DECA-ResNet18) incorporating the ECA attention mechanism. The proposed algorithm first converts the complex features of multi-carrier composite modulation signals into visually interpretable image features (including the quantity and concentration of bright spots and lines) through the MD-TFSS, achieving intuitive representation of multiple modulation characteristics. Meanwhile, the dual-channel input mechanism enables collaborative expression of outer modulation time-frequency diagram and inner modulation dual spectrum features, ensuring tight integration of inner and outer characteristics while avoiding feature isolation issues in traditional multi-diagram concatenation methods. Secondly, the DECA-ResNet18 network dynamically allocates weights through an adaptive regulation mechanism based on input feature differences, autonomously adjusting channel attention levels to effectively capture complementary characteristics from both inner and outer modulation features, thereby enhancing recognition accuracy and generalization capability for multi-carrier composite modulation signals. Theoretical analysis and simulation results demonstrate that, compared with the existing methods that use isolated outer and inner features or conventional multi-feature diagram construction approaches, the proposed algorithm achieves superior recognition performance under low SNR conditions. Additionally, DECA-ResNet18 demonstrates enhanced recognition performance for multi-carrier composite modulated signals compared to the traditional ResNet18. Full article
(This article belongs to the Special Issue Spectrum Sensing and Access Technologies for Drones)
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