Non-Orthogonal PSWFs Signal Detection Method Based on Adaptive Temporal-Spatial Feature Fusion

CHEN Wenhua; MAO Zhongyang; LU Faping; SUN Ye; GAO Yixuan

doi:10.11999/JEIT260024

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CHEN Wenhua, MAO Zhongyang, LU Faping, SUN Ye, GAO Yixuan. Non-Orthogonal PSWFs Signal Detection Method Based on Adaptive Temporal-Spatial Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260024

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CHEN Wenhua, MAO Zhongyang, LU Faping, SUN Ye, GAO Yixuan. Non-Orthogonal PSWFs Signal Detection Method Based on Adaptive Temporal-Spatial Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260024

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Non-Orthogonal PSWFs Signal Detection Method Based on Adaptive Temporal-Spatial Feature Fusion

doi: 10.11999/JEIT260024 cstr: 32379.14.JEIT260024

CHEN Wenhua^{1, 2, 3},
MAO Zhongyang^{1, 2},
LU Faping^{1, 2
,
,},
SUN Ye³,
GAO Yixuan⁴

1.
Naval Aviation University, People’s Liberation Army of China, Yantai 264001, China
2.
Shandong Provincial Key Laboratory of Sea-Air Information Perception and Processing Technology, Yantai 264001, China
3.
No. 91428 Force, People’s Liberation Army of China, Ningbo 315456, China
4.
Harbin Engineering University, Harbin 150001, China

Funds: China National Postdoctoral Program for Innovative Talents (BX20200039), The National Natural Science Foundation of Shandong Province (ZR2023MD045)

Received Date: 2026-01-08
Accepted Date: 2026-07-21
Rev Recd Date: 2026-07-16

Available Online: 2026-07-30

Abstract

Abstract

Objective To address the demands of B5G/6G systems for high spectral efficiency and transmission reliability, Prolate Spheroidal Wave Functions (PSWFs)-based non-orthogonal modulation has attracted extensive research interest because of its strong time-frequency energy concentration. However, severe mutual interference among multiplexed PSWF signals degrades the performance of conventional detection methods in complex channel environments. Existing methods are limited by ideal channel assumptions or single-modal feature extraction and therefore cannot fully exploit the temporal and spatial information of PSWF signals or adapt to dynamic interference. An Adaptive Temporal-Spatial Feature Fusion (ATSFF) architecture is proposed for accurate and robust detection of non-orthogonal PSWF signals. Method A dual-path parallel framework is constructed to extract complementary temporal and spatial features. A Gated Recurrent Unit (GRU) network extracts deep temporal features and captures long-term dependencies from one-dimensional received signals. In the other path, one-dimensional signals are transformed into two-dimensional representations using the Gramian Angular Difference Field (GADF), and hierarchical spatial features are extracted using ResNet50. An adaptive probability-weighted fusion mechanism dynamically adjusts the contributions of the two feature branches according to their prediction uncertainty, thereby integrating complementary temporal and spatial information and improving detection robustness. Results and Discussion Simulations on a 32-class non-orthogonal PSWF signal dataset (Fig. 2) show that the proposed ATSFF method outperforms coherent detection, cross-term detection, Approximate Message Passing-Interleave Division Multiple Access (AMP-IDMA), and Temporal Multiple Sparse Bayesian Learning-Least Squares (TMSBL-LS) over the full Signal-to-Noise Ratio (SNR) range. t-SNE visualization (Fig. 4) shows that the fused features achieve better inter-class separation and greater intra-class compactness. At a bit error rate of 4 × 10^–5, the proposed method achieves a gain of approximately 0.2 dB over cross-term detection (Fig. 6). Although ATSFF has higher computational overhead and lower real-time performance than conventional methods, its single-sample inference cost remains fixed after the network architecture is established, and GPU-based batch processing is supported. The method is therefore suitable for communication scenarios with high detection-accuracy requirements. Conclusions An adaptive temporal-spatial feature fusion method is proposed for non-orthogonal PSWF signal detection under severe mutual interference. Dual-path feature extraction is achieved using GRU and ResNet50, and a prediction-uncertainty-based adaptive probability-weighted fusion mechanism is used to integrate complementary temporal and spatial features. The simulation results demonstrate improved detection accuracy and robustness under complex channel conditions. The proposed method provides a feasible approach for high-accuracy detection of non-orthogonal PSWF signals.
- Prolate Spheroidal Wave Functions(PSWF),
- Non-orthogonal signal detection,
- Temporal-spatial feature fusion,
- Adaptive weighted fusion

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References(27)

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