A Deep Learning-Based Detection System of Multi-class Crops and Orchards Using a UAV

Khan, Shahbaz; Tufail, Muhammad; Khan, Muhammad Tahir; Khan, Zulfiqar Ahmad

doi:10.1007/978-981-16-9991-7_3

Cited by 2 publications

(1 citation statement)

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“…To speed up the training process in passive RIS‐aided MIMO and MISO systems, deep learning and hierarchical searching algorithms have been developed for channel estimation [164–167].…”

Section: Channel Estimationmentioning

confidence: 99%

A survey on reconfigurable intelligent surfaces: Wireless communication perspective

et al. 2023

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Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS‐assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS‐assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi‐users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed.

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“…To speed up the training process in passive RIS‐aided MIMO and MISO systems, deep learning and hierarchical searching algorithms have been developed for channel estimation [164–167].…”

Section: Channel Estimationmentioning

confidence: 99%