Reconfigurable Intelligent Sensing Surface aided Wireless Powered Communication Networks: A Sensing-Then-Reflecting Approach

This paper presents a reconfigurable intelligent sensing surface (RISS) that combines passive and active elements to achieve simultaneous reflection and direction of arrival (DOA) estimation tasks. By utilizing DOA information from the RISS instead of conventional channel estimation, the pilot overhead is reduced and the RISS becomes independent of the hybrid access point (HAP), enabling efficient operation. Specifically, the RISS autonomously estimates the DOA of uplink signals from single-antenna users and reflects them using the HAP’s slowly varying DOA information. During downlink transmission, it updates the HAP’s DOA information and designs the reflection phase of energy signals based on the latest user DOA information. The paper includes a comprehensive performance analysis, covering system design, protocol details, receiving performance, and RISS deployment suggestions. We derive a closed-form expression to analyze system performance under DOA errors, and calculate the statistical distribution of user received energy using the moment-matching technique. We provide a recommended transmit power to meet a specified outage probability and energy threshold. Numerical results demonstrate that the proposed system outperforms the conventional counterpart by 2.3 dB and 4.7 dB for Rician factors κ h = κ G = 1 and κ h = κ G = 10, respectively

Secure Wireless Communications Based on Compressive Sensing: A Survey

IEEE Compressive sensing (CS) has become a popular signal processing technique and has extensive applications in numerous fields such as wireless communications, image processing, magnetic resonance imaging, remote sensing imaging, and anology to information conversion, since it can realize simultaneous sampling and compression. In the information security field, secure CS has received much attention due to the fact that CS can be regarded as a cryptosystem to attain simultaneous sampling, compression and encryption when maintaining the secret measurement matrix. Considering that there are increasing works focusing on secure wireless communications based on CS in recent years, we produce a detailed review for the state-of-the-art in this paper. To be specific, the survey proceeds with two phases. The first phase reviews the security aspects of CS according to different types of random measurement matrices such as Gaussian matrix, circulant matrix, and other special random matrices, which establishes theoretical foundations for applications in secure wireless communications. The second phase reviews the applications of secure CS depending on communication scenarios such as wireless wiretap channel, wireless sensor network, internet of things, crowdsensing, smart grid, and wireless body area networks. Finally, some concluding remarks are given

Advances in Deep Learning Algorithms for Agricultural Monitoring and Management

This study examines the transformative role of deep learning algorithms in agricultural monitoring and management. Deep learning has shown remarkable progress in predicting crop yields based on historical weather, soil, and crop data, thereby enabling optimized planting and harvesting strategies. In disease and pest detection, image recognition technologies such as Convolutional Neural Networks (CNNs) can analyze high-resolution images of crops to identify early signs of diseases or pest infestations, allowing for swift and effective interventions. In the context of precision agriculture, these advanced techniques offer resource efficiency by enabling targeted treatments within specific field areas, significantly reducing waste. The paper also sheds light on the application of deep learning in analyzing vast amounts of remote sensing and satellite imagery data, aiding in real-time monitoring of crop growth, soil moisture, and other critical environmental factors. In the face of climate change, advanced algorithms provide valuable insights into its potential impact on agriculture, thereby aiding the formulation of effective adaptation strategies. Automated harvesting and sorting, facilitated by robotics powered by deep learning, are also investigated, as they promise increased efficiency and reduced labor costs. Moreover, machine learning models have shown potential in optimizing the entire agricultural supply chain, ensuring minimal waste and optimum product quality. Lastly, the study highlights the power of deep learning in integrating multi-source data, from weather stations to satellites, to form comprehensive monitoring systems that allow real-time decision-making

Nonorthogonal Multiple Access for 5G and Beyond

This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N029720/1 and Grant EP/N029720/2. The work of L. Hanzo was supported by the ERC Advanced Fellow Grant Beam-me-up