Wireless Multimodal Communications for Integrated Heterogeneous Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 644

Special Issue Editors


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Guest Editor
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: wireless cooperative communication; multimodal communication; 6G
School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
Interests: digital integrated circuit design
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Guest Editor
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121,China
Interests: mobile edge computing; integrated sensing and communication; software defined radio; wireless communications

Special Issue Information

Dear Colleagues,

The deep integration of 5G/6G with the digital economy has driven ubiquitous expansion of emerging networks—mobile internet, industrial IoT, satellite internet, vehicular networks, and low-altitude intelligent networks—into space-air-ground-sea domains. To meet the demands of pervasive intelligent IoT in integrated scenarios, communication systems must transcend traditional terrestrial boundaries and establish multidimensional architectures spanning deep space, aerial, terrestrial, and maritime environments. In emerging fields such as smart grid digitization, aerospace vehicle networking, and maritime energy IoT, multimodal communications—integrating radio frequency/optical/acoustic transmission, integrated sensing, communication, and computation (ISAC), and semantic communication—are redefining information exchange paradigms for intelligent power system maintenance, low-altitude traffic management, and marine resource exploitation. Current challenges include cross-domain coordination (e.g., dynamic spectrum adaptation, heterogeneous protocol interoperability, and edge-cloud resource orchestration), necessitating an innovation ecosystem combining self-organizing networks, intelligent coding, edge AI, and hardware acceleration. This work focuses on multimodal communication advancements in vertical domains (e.g., power systems, aerospace, marine development), aiming to advance dynamic reconfiguration of smart grids, space-air-ground intelligent communication control, and autonomous coordination in UAV self-organizing networks. By developing software-defined radio (SDR)-compatible platforms with intelligent reflecting surfaces (IRS) and hybrid multimodal wireless transmission, we accelerate the engineering application of multimodal technologies in strategic areas such as next-generation power systems, low-altitude economy, and deep-space exploration.

Dr. Chao Ren
Dr. Biao Pan
Dr. Boyang Liu
Guest Editors

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Keywords

  • multimodal networks
  • ISAC integration
  • cross-domain orchestration
  • intelligent reflecting surfaces

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Published Papers (1 paper)

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Research

20 pages, 5744 KB  
Article
Decoupling Rainfall and Surface Runoff Effects Based on Spatio-Temporal Spectra of Wireless Channel State Information
by Hao Li, Yin Long and Tehseen Zia
Electronics 2025, 14(20), 4102; https://doi.org/10.3390/electronics14204102 - 20 Oct 2025
Viewed by 390
Abstract
Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff [...] Read more.
Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff (water accumulation). These two physical phenomena become intertwined in the received signals, resulting in severe feature ambiguity. This not only greatly limits the accuracy of environmental sensing but also hinders communication systems from performing effective channel compensation. How to disentangle these combined effects from a single wireless link represents a fundamental scientific challenge for achieving high-precision wireless environmental sensing and ensuring communication reliability under harsh conditions. To address this challenge, we propose a novel signal processing framework that aims to effectively decouple the effects of rainfall and surface runoff from Channel State Information (CSI) collected using commercial Wi-Fi devices. The core idea of our method lies in first constructing a two-dimensional CSI spatiotemporal spectrogram from continuously captured multicarrier CSI data. This spectrogram enables high-resolution visualization of the unique “fingerprints” of different physical effects—rainfall manifests as smooth background attenuation, whereas surface runoff appears as sparse high-frequency textures. Building upon this representation, we design and implement a Dual-Decoder Convolutional Autoencoder deep learning model. The model employs a shared encoder to learn the mixed CSI features, while two distinct decoder branches are responsible for reconstructing the global background component attributed to rainfall and the local texture component associated with surface runoff, respectively. Based on the decoupled signal components, we achieve simultaneous and highly accurate estimation of rainfall intensity (mean absolute error below 1.5 mm/h) and surface water accumulation (detection accuracy of 98%). Furthermore, when the decoupled and refined channel estimates are applied to a communication receiver for channel equalization, the Bit Error Rate (BER) is reduced by more than one order of magnitude compared to conventional equalization methods. Full article
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