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<p>Underwater visible light communication (UVLC) is a method that uses visible light as a carrier to transmit information through an underwater channel and offers the key advantages of fast speed, low cost and large capacity. The main challenges faced by UVLC systems include the power constraints of underwater terminals and signal distortions due to light attenuation in complex and changing underwater environments. In order to improve the efficiency and robustness of image transmission process in UVLC systems, we propose a novel image compression and reconstruction solution that jointly optimizes coding complexity and image reconstruction quality using neural architecture search (NAS) mechanism. Experimental validations on an UVLC system demonstrate that the proposed scheme achieves increased transmission efficiency with reduced algorithmic complexity and offers robustness to errors caused by the underwater channel distortions. For bit error rate (BER) values as high as 1.58 × 10<sup>− 2</sup>, the peak signal-to-noise ratio (PSNR) remains above 20 dB while structure similarity index measure (SSIM) value is above 0.67, for a compression ratio (CR) of 0.33.</p>

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Robust and efficient image transmission in power-constrained underwater visible light communication systems using neural architecture search

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Qiujun Jin,
  • Bohua Deng,
  • H. Y. Fu,
  • Faisal Nadeem Khan

摘要

Underwater visible light communication (UVLC) is a method that uses visible light as a carrier to transmit information through an underwater channel and offers the key advantages of fast speed, low cost and large capacity. The main challenges faced by UVLC systems include the power constraints of underwater terminals and signal distortions due to light attenuation in complex and changing underwater environments. In order to improve the efficiency and robustness of image transmission process in UVLC systems, we propose a novel image compression and reconstruction solution that jointly optimizes coding complexity and image reconstruction quality using neural architecture search (NAS) mechanism. Experimental validations on an UVLC system demonstrate that the proposed scheme achieves increased transmission efficiency with reduced algorithmic complexity and offers robustness to errors caused by the underwater channel distortions. For bit error rate (BER) values as high as 1.58 × 10− 2, the peak signal-to-noise ratio (PSNR) remains above 20 dB while structure similarity index measure (SSIM) value is above 0.67, for a compression ratio (CR) of 0.33.