Complex-domain Joint Spectrum Sensing Method for UAV Swarms in Complex Electromagnetic Environments

QIAN Hui; CHEN Li; YIN Huarui; WANG Weidong

doi:10.11999/JEIT260499

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QIAN Hui, CHEN Li, YIN Huarui, WANG Weidong. Complex-domain Joint Spectrum Sensing Method for UAV Swarms in Complex Electromagnetic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260499

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QIAN Hui, CHEN Li, YIN Huarui, WANG Weidong. Complex-domain Joint Spectrum Sensing Method for UAV Swarms in Complex Electromagnetic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260499

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Complex-domain Joint Spectrum Sensing Method for UAV Swarms in Complex Electromagnetic Environments

doi: 10.11999/JEIT260499 cstr: 32379.14.JEIT260499

Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China

Funds: National Science Foundation for Young Scientists of China (No. 62522126), General Program of the National Natural Science Foundation of China (No. 62471449), Anhui Provincial Natural Science Foundation (No. 2308085J24)

Received Date: 2026-04-23
Accepted Date: 2026-08-10
Rev Recd Date: 2026-08-10

Available Online: 2026-08-20

Abstract

Abstract

Objective The rapid development of the low-altitude economy is increasing the use of unmanned aerial vehicle (UAV) swarms in emergency communication, urban logistics, reconnaissance, and low-altitude network coverage. These applications require reliable spectrum awareness to support cooperative communication, dynamic spectrum access, and interference avoidance. However, low-altitude electromagnetic environments often contain low-SNR signals, multipath propagation, non-cooperative interference, and multiple coexisting transmissions. Conventional energy, cyclostationary-feature, and matched-filter detectors are sensitive to noise uncertainty, computational cost, or prior waveform knowledge. Learning-based methods can improve robustness, but many rely on power spectral density (PSD) or short-time Fourier transform (STFT) representations. These representations may weaken phase information or require costly two-dimensional time-frequency preprocessing. Existing methods also focus mainly on spectrum occupancy and provide limited information about overlapping transmissions. This study therefore develops a low-latency joint sensing method that preserves magnitude and phase information while estimating spectrum occupancy and interference overlap for each frequency bin. The method is intended for local spectrum sensing at UAV nodes under resource and latency constraints. Methods The proposed RadioSEUnet pipeline contains two stages: magnitude-phase feature construction and joint spectrum-state estimation. First, each complex baseband in-phase/quadrature (I/Q) sequence is multiplied by a Hann window and transformed using a one-dimensional fast Fourier transform (FFT). The resulting complex spectrum is decomposed into logarithmic magnitude and phase components. The two components are normalized separately and stacked as a two-channel feature tensor. Binary labels indicate spectrum occupancy and multi-signal overlap at each frequency bin. RadioSEUnet adopts a U-shaped encoder-bottleneck-decoder architecture with four encoder stages containing 64, 128, 256, and 512 channels. Each RadioSEBlock combines a complex-parameterized convolution, squeeze-and-excitation channel attention, and a residual connection. The convolution couples the magnitude and phase feature streams through constrained cross-channel operations. Two prediction heads convert the shared representation into a spectrum-occupancy probability mask and an interference-overlap probability mask. The model is optimized using an equally weighted sum of two binary cross-entropy losses. Training uses AdamW, cosine-annealing learning-rate scheduling, early stopping, a batch size of 64, and at most 200 epochs. The complete data collection contains 72,000 complex I/Q records, including 48,000 simulated records and 24,000 measured records. The simulated subset covers Wi-Fi, BLE, ZigBee, LoRa, QPSK/16QAM, FM, and AM signals. Signal-to-noise ratios range from –15 dB to 10 dB under additive white Gaussian noise and Rayleigh fading. The measured subset was collected using a USRP N310 in the 2.4–2.5 GHz ISM band at 100 MS/s over a 1 ms observation interval. The controlled quantitative evaluation uses an 8:1:1 split of the simulated subset. A separate simulated-to-measured protocol is defined in the main text to examine cross-domain generalization. RadioSEUnet is compared with six PSD- or STFT-based baselines under matched data splits and hardware conditions. Performance is measured using intersection over union (IoU), precision, recall, preprocessing time, inference time, and total sensing latency. Results and Discussions The SNR-dependent quantitative results reported here are obtained using the controlled simulated-data protocol. At -15 dB, RadioSEUnet achieves an IoU of 0.768 and a recall of 0.846 for spectrum occupancy detection. Compared with the second-best STFT-RADN baseline, these values correspond to absolute improvements of 0.186 and 0.166, respectively. For interference-overlap detection, RadioSEUnet achieves an IoU of 0.456 and a precision of 0.768 at –15 dB. The corresponding improvements over STFT-RADN are 0.246 and 0.275. The lower IoU for interference-overlap detection indicates that weak overlap boundaries remain difficult to separate from strong-signal sidelobes and background noise. The latency evaluation is conducted on the workstation specified in the main text. Magnitude-phase preprocessing requires 21.04 ms, and network inference requires 4.77 ms, producing a total sensing latency of approximately 25.81 ms. STFT-YOLOv3 requires 98.9 ms under the same hardware setting, so the proposed pipeline is approximately 3.8 times faster in this comparison. Ablation experiments show that magnitude-phase preprocessing, complex-parameterized feature coupling, and channel attention each improve low-SNR sensing performance. Removing the magnitude-phase preprocessing produces the largest degradation. These results indicate that preserving complementary magnitude and phase information is useful for weak-signal and interference-overlap detection. They do not, however, establish performance on airborne hardware or across unreported radio environments. Conclusions RadioSEUnet combines a magnitude-phase representation, constrained cross-channel feature coupling, channel attention, multiscale feature fusion, and dual-head prediction. It jointly estimates spectrum occupancy and interference-overlap states while avoiding two-dimensional STFT preprocessing. Under the controlled simulated-data protocol, the method provides higher point estimates than the six evaluated baselines at low SNR and reduces total sensing latency on the evaluated workstation. The present evidence is limited to the reported signal types, channel models, hardware configuration, and the 2.4–2.5 GHz measurement band. Quantitative simulated-to-measured results, tests on wider bands, additional interference types, repeated trials, and deployment on airborne edge hardware are still required. Future work will therefore focus on cross-domain validation, lightweight deployment, boundary-aware interference modeling, and integration with spectrum resource management for UAV networks.
- Electromagnetic spectrum sensing,
- UAV swarms,
- Complex electromagnetic environment,
- Complex-domain neural network

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References(21)

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