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In recent years, ultraviolet (UV) communication systems operate spectrum wavelength in atmospheric conditions to absorb UV signals from ambient light. In channel estimation and equalization, UV spectrum is critical due to unique propagation, path loss, and turbulence. Hence, this research proposes pilot-based estimation with zero forcing-compressive sensing (PBE-ZF-CS) for estimating channel parameters and coefficients by improving quality of signals. Initially, deep multiple input multiple output (Deep MIMO) dataset is employed as it contains channel measurements, transmitter, and receiver signals. Then, preprocessing step includes signal normalization through min–max scaling (MMS) for adjusting signal amplitudes for effective transmission and reception, and error-correcting codes (ECC) is used for removing unwanted noise from received signals. Finally, PBE-ZF is employed for efficient transmission in UV systems of signals for better channel estimation. The proposed PBE-ZF-CS achieved better results such as mean squared error (MSE) is 0.9245%, mean absolute error (MAE) is 1.7517%, and symbol detection error rate (SER) is 0.0598% when compared to existing approach none-line-of-sight (NLOS).

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Piolet-Based Channel Estimation and Equalization for Ultraviolet Communication Systems Using Zero Forcing and Compressive Sensing

  • 会议论文

  • 发表日期：

    2026年

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  • Pavan Srikanth SubbaRaju Patchamatla,
  • Prateeksha Siddhanti

摘要

In recent years, ultraviolet (UV) communication systems operate spectrum wavelength in atmospheric conditions to absorb UV signals from ambient light. In channel estimation and equalization, UV spectrum is critical due to unique propagation, path loss, and turbulence. Hence, this research proposes pilot-based estimation with zero forcing-compressive sensing (PBE-ZF-CS) for estimating channel parameters and coefficients by improving quality of signals. Initially, deep multiple input multiple output (Deep MIMO) dataset is employed as it contains channel measurements, transmitter, and receiver signals. Then, preprocessing step includes signal normalization through min–max scaling (MMS) for adjusting signal amplitudes for effective transmission and reception, and error-correcting codes (ECC) is used for removing unwanted noise from received signals. Finally, PBE-ZF is employed for efficient transmission in UV systems of signals for better channel estimation. The proposed PBE-ZF-CS achieved better results such as mean squared error (MSE) is 0.9245%, mean absolute error (MAE) is 1.7517%, and symbol detection error rate (SER) is 0.0598% when compared to existing approach none-line-of-sight (NLOS).