Non-Orthogonal PSWFs Signal Detection Method Based on Adaptive Time-Space 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 Time-Space 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 Time-Space 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 Time-Space 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)

Accepted Date: 2026-07-21
Rev Recd Date: 2026-07-21

Available Online: 2026-07-30

Abstract

Abstract

Objective To address B5G/6G demands for high spectral efficiency and transmission reliability, non-orthogonal Prolate Spheroidal Wave Function (PSWF) modulation has drawn extensive research interest for its strong time-frequency energy concentration. However, severe mutual interference in PSWF signal multiplexing degrades conventional detection performance in complex channels. Limited by ideal channel assumptions or single-modal feature extraction, existing methods cannot fully exploit signal temporal-spatial information and lack adaptability to dynamic interference. This work proposes an intelligent detection architecture with adaptive temporal-spatial feature fusion (ATSFF) for accurate, robust non-orthogonal PSWF signal detection. Method A dual-path parallel framework extracts complementary features from time and transform domains. A Gated Recurrent Unit (GRU) extracts deep temporal features and captures long-range dependencies from 1D received signals. The other path converts 1D signals into 2D representations via the Gramian Angular Difference Field (GADF), and extracts hierarchical spatial features using ResNet50. An adaptive probability-weighted fusion mechanism dynamically adjusts feature contributions based on sub-network prediction uncertainty to generate robust detection outputs. Results and Discussion Simulations on a 32-class non-orthogonal PSWF dataset (Fig. 2) show the proposed ATSFF outperforms conventional coherent detection, cross-term suppression detection, Approximate Message Passing Interleave Division Multiple Access (AMP-IDMA) and Temporal Sparse Bayesian Learning Least Squares (TMSBL-LS) across the full Signal-to-Noise Ratio (SNR) range. t-SNE visualization (Fig. 4) confirms better inter-class separability and intra-class compactness of fused features. At a bit error rate of 4×10^–5, it achieves 0.2 dB gain (Fig. 6) over coherent detection. ATSFF has higher network overhead and weaker real-time performance than linear detection, yet it supports GPU batch inference with fixed single-sample computation cost, suiting accuracy-sensitive communication scenarios. Conclusions Targeting interference issues in non-orthogonal PSWF signal detection, this paper presents an adaptive temporal-spatial feature fusion detection method. It realizes dual-modal feature extraction via GRU and ResNet50, and adopts a prediction-uncertainty-based adaptive weighted fusion mechanism to integrate dual-path strengths, significantly improving detection accuracy and robustness. Simulations validate its superior performance and stable channel adaptability, providing an efficient solution and design reference for intelligent detection of non-orthogonal PSWF signals.
- prolate spheroidal wave functions,
- non-orthogonal signal detection,
- time-space feature fusion,
- adaptive weighted fusion

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References(27)

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