ScholarWorks@중앙대학교

초록

We propose an artificial intelligence (AI)-based interference mitigation method to enable the coexistence of phase-modulated continuous wave (PMCW) and orthogonal frequency-division multiplexing (OFDM) systems in spectrum-sharing internet of things (IoT) environments. Existing mitigation techniques struggle to address the complex mutual interference arising from the structural mismatches of heterogeneous systems in dense IoT deployments. To overcome this challenge, we first formulate a signal model that accounts for PMCW–OFDM coexistence and characterizes bidirectional interference between the two systems. Subsequently, we propose a U-Net-based deep learning model that first applies time-domain thresholding as a pre-processing step. The model uses complex-valued inputs and an atrous spatial pyramid pooling bottleneck to capture multi-scale contextual features in the latent space, where ambiguous structures caused by interference can be distinguished from true target components. The proposed model is trained across various vehicular scenarios to enhance robustness under varying interference conditions. Simulation results for both systems under diverse vehicular scenarios demonstrate that the proposed method achieves signal-to-interference-plus-noise ratio improvements of up to 14.47 dB and masked peak signal-to-noise ratio improvements of up to 22.19 dB across various signal-to-noise ratios, compared to conventional baselines such as denoising convolutional neural network (DnCNN) and denoising diffusion probabilistic model (DDPM). In addition, the proposed method reduces latency by 12.5% and 58.7% compared to DnCNN and DDPM, respectively, indicating improved computational efficiency. These results highlight its potential for reliable sensing and offer spatial awareness to facilitate coexistence strategies in large-scale IoT networks.

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