In recent years, due to the rapid development of wireless communication and edge computing as well as artificial intelligence, wireless Adhoc networks have been significantly upgraded and widely applied in many areas, such as smart cities and industrial internet of things. To support the development of Adhoc networks, many new technologies have been proposed, among which reconfigurable backscatter is one of the most promising technologies. Backscatter communication enables passive backscatter devices to transmit information by modulating incident sinusoidal carriers or ambient radio-frequency carriers without using any power-hungry or complex RF transmitters, and thus is an energy- and cost-efficient communication technology for wireless devices that typically have limited battery energy and strict cost constraint. Currently, the research of deep reconfigurable backscatter communication has become a hot topic from both the academy and industry.

Several critical challenges have appeared in the study of deep reconfigurable backscatter communication for wireless Adhoc networks. One critical challenge is the complicated channel estimation, where some deep learning-based channel estimation methods as well as the deep feedback mechanism should be developed for the backscatter communication in wireless Adhoc networks. One more challenge is the complicated performance evaluation, due to the intelligent integration of backscatter into other communication technologies, such as MIMO, OFDM, UAV, caching, and mobile edge computing. Another challenge is the complicated resource management, where some deep learning-based resource management schemes should be devised to enhance the system performance of wireless Adhoc networks.

The aim of this Special Issue is to collate original research articles, as well as review articles, investigating the significance of deep learning in reconfigurable backscatter communication to preserve the wireless Adhoc networks. With this Special Issue, we hope to discover the promises of backscatter communication using machine learning in wireless Adhoc networks. In addition, we also wish to come across innovative solutions with useful insights, and results.

Potential topics include but are not limited to the following:

• Deep backscatter-based transmission schemes
• Intelligent reception schemes for backscatter communication
• Deep channel estimation of reconfigurable backscatter communication
• Performance evaluation of backscatter communication
• Advanced resource management of backscatter communication
• Intelligent backscatter with MIMO
• Backscatter communication with UAV
• Intelligent backscatter with OFDM
• Backscatter communication with Caching
• Security of backscatter communication
• Industrial internet of things
• Backscatter with edge computing