Intellectual Property India Publishes Patent Application for 'Simulation Of An Optical Irs-Assisted Underwater Wireless Communication With Machine Learning Techniques' Filed by Srinivasa Ramanujan Institute Of Technology; M. Sivalingamaiah; S. Uday Kiran; S. Syed Basha; and C. Sudheer

MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641051521 A) filed by Srinivasa Ramanujan Institute Of Technology; M. Sivalingamaiah; S. Uday Kiran; S. Syed Basha; and C. Sudheer, Ananthapuramu, Andhra Pradesh, on April 22, for 'simulation of an optical irs-assisted underwater wireless communication with machine learning techniques.'

Inventor(s) include Srinivasa Ramanujan Institute Technology; M. Sivalingamaiah; S. Uday Kiran; S. Syed Basha; and C. Sudheer.

The application for the patent was published on May 1, under issue no. 18/2026.

According to the abstract released by the Intellectual Property India: "Underwater wireless communication (UWC) is crucial for operations such as deep-sea exploration, real-time environmental sensing, and autonomous underwater vehicle (AUV) coordination. Conventional acoustic and radio frequency (RF) communication methods suffer from limited bandwidth and excessive latency due to severe attenuation in aquatic media. Optical wireless communication (OWC) provides superior data rates and low-latency links, but its performance is significantly affected by absorption, scattering, and beam misalignment in turbid waters. To overcome these issues, this work presents a simulation-based optical intelligent reflecting surface (IRS)-assisted UWC architecture integrated with machine learning techniques. The IRS utilizes programmable reflecting elements to steer and phase-align optical signals, enhancing signal robustness and widening coverage. Simultaneously, machine learning algorithms are employed to optimize reflection configurations, predict channel variations, and dynamically adapt to underwater disturbances. Simulation results reveal notable improvements in reliability, energy efficiency, and link stability compared to conventional IRS-assisted systems. This intelligent hybrid framework highlights the transformative potential of combining IRS and machine learning for next-generation autonomous underwater communication networks."

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