The rapid progress of 5G technology has enabled ultra-fast data transmission, paving the way for new possibilities in real-time video streaming, autonomous systems, and telemedicine. The need for high-resolution, high-frame-rate video transmission continues to increase due to the increased bandwidth and decreased latency in 5G, particularly for applications that must analyze data in real-time, such as immersive virtual reality experiences, remote medical diagnostics, and autonomous cars.

However, the challenges lie in ensuring effective data transfer without increase in delay or power consumption, especially in components like image sensors, which are essential for recording and sending high-quality video. Image sensors handle vast amounts of data at high frame rates. Traditional readout methods in image sensors struggle to maintain power efficiency while delivering such high data rates. By distributing the pixel data into segments, the methodology involves calculating theA DC clock frequency for various segment sizes in segmented data flow and reducing peak processing loads to improve overall system efficiency, particularly where a section of the image is of interest. This segment data source schematic is integrated with a 5G RF modulator schematic. The feasibility of implementing the proposed integrated design as an integrated circuit (IC) is also assessed, with particular emphasis on achieving low power consumption and optimizing the speed. The results demonstrate that the data segmentation significantly optimizes power usage and readout speed by 10%, making the system viable for high-speed, low-power video transmission applications.

This thesis’s findings contribute to the advancements of 5G-based video transmission systems, offering trade-offs between power and readout time of pixel data and IC design feasibility in high-frequency communication systems.