Enhancement of Image Reconstruction in Orthogonal Frequency-Division Multiplexing (OFDM)-Based Communication System Using Conditional Diffusion Model of Generative AI
Source abstract
Abstract
The orthogonal frequency-division multiplexing (OFDM) transmission technique is well known to be efficient for data transmission but is susceptible to performance degradation due to factors such as high-order modulation schemes, multipath fading, and noise. In this paper, an approach for reconstructing images received by the OFDM transmission technique is proposed based on generative AI. The approach exploits a conditional diffusion model (CDM) that incorporates conditional factors reflecting the degree of distortion in the received images by the OFDM technique. Additionally, it employs a method to learn the variance in the reverse process during training, considering the level of distortion associated with various modulation schemes. Through this adaptability, the proposed model is experimentally demonstrated to optimize image reconstruction performance under various modulation schemes in low-SNR environments. The proposed conditional diffusion model can enhance the PSNR of OFDM-based received images by up to 8 dB in low-SNR conditions with various modulation schemes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Blind Source Separation Techniques · Image and Signal Denoising Methods · PAPR reduction in OFDM
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Research and Development Department, SMARTEVER, Co., Ltd., Seoul 01886, Republic of Korea
References (22)
Show source-provided references
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