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Spatial Attention Deep Residual Network Based Fine-Grained Electromagnetic Spectrum Map Construction Method
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Jia-wei XIE1, 2, Zhi-yong YU1, *, Yu-jie ZHANG1, Jun-jie CAO1, 3, YANG-Jian1
Science Technology and Engineering | 2025, 25(14) : 5905 - 5912
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Science Technology and Engineering | 2025, 25(14): 5905-5912
• Papers·Electronic and Communicational Technology •
Spatial Attention Deep Residual Network Based Fine-Grained Electromagnetic Spectrum Map Construction Method
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Jia-wei XIE1, 2, Zhi-yong YU1, *, Yu-jie ZHANG1, Jun-jie CAO1, 3, YANG-Jian1
Affiliations
  • 1. School of Operational Support, Rocket Force University of Engineering, Xi'an 710025, China
  • 2. Chinese People's Liberation Army Unit 96743, Tianshui 741020, China
  • 3. College of Information and Communication, National University of Defence Technology, Wuhan 430000, China
Published: 2025-05-18 doi: 10.12404/j.issn.1671-1815.2405481
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The efficient utilization of electromagnetic spectrum resources has become a significant concern in the domain of wireless communications, with EMSM(electromagnetic spectrum map) playing a crucial role in visually representing spectrum usage within a specific task area and providing valuable support for the optimization of wireless networks. To address the challenges associated with generating fine-grained EMSMs under conditions of complex scenes and limited spatial point monitoring data, an improved DRN(deep residual network) model, ES-AFB(enhanced with a spatial attention feature block), was proposed. This model drew inspiration from image super-resolution techniques and leveraged the strong spatial characteristics of EMSMs to design a deep residual network capable of extracting the correlation and spectral features of EMSMs. The enhanced spatial attention feature block was utilized to mine the intrinsic implicit spatial features of coarse-grained EMSMs. Subsequently, the data size was reconfigured through the network’s multilayer up-sampling module, enabling the achievement of a more effective fine-grained image restoration. This approach allows for the generation of high-quality fine-grained EMSMs using limited coarse-grained monitoring data. The effectiveness of the algorithm is validated through simulation experiments, with the root-mean-square error of the EMSMs generated from actual data being found to be no more than 3%.

electromagnetic spectrum map  /  spectral map  /  deep residual network  /  spatial attention  /  fine-grained
Jia-wei XIE, Zhi-yong YU, Yu-jie ZHANG, Jun-jie CAO, YANG-Jian. Spatial Attention Deep Residual Network Based Fine-Grained Electromagnetic Spectrum Map Construction Method[J]. Science Technology and Engineering, 2025 , 25 (14) : 5905 -5912 . DOI: 10.12404/j.issn.1671-1815.2405481
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Year 2025 volume 25 Issue 14
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Article Info
doi: 10.12404/j.issn.1671-1815.2405481
  • Receive Date：2024-07-22
  • Online Date：2025-07-09
  • Published：2025-05-18
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History
  • Received：2024-07-22
  • Revised：2025-02-28
Funding
Affiliations
    1. School of Operational Support, Rocket Force University of Engineering, Xi'an 710025, China
    2. Chinese People's Liberation Army Unit 96743, Tianshui 741020, China
    3. College of Information and Communication, National University of Defence Technology, Wuhan 430000, China
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