DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things

We propose a new energy harvesting strategy that uses a dedicated energy source (ES) to optimally replenish energy for radio frequency (RF) energy harvesting powered Internet of Things. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that minimizes the energy consumption on both the energy harvesting device and the ES. Besides the causality and capacity constraints that are investigated in the existing approaches, DTER also takes into account the overhead issue and the nonlinear charge characteristics of an energy storage component to make the proposed strategy practical. Both offline and online scenarios are considered in the second step of DTER. To solve the nonlinear optimization problem of the offline scenario, we convert the design of offline optimal energy requesting problem into a classic shortest path problem and thus a global optimal solution can be obtained through dynamic programming (DP) algorithms. The online suboptimal transmission strategy is developed as well. Simulation study verifies that the online strategy can achieve almost the same energy efficiency as the global optimal solution in the long term

One-Shot Pruning for Fast-adapting Pre-trained Models on Devices

Large-scale pre-trained models have been remarkably successful in resolving downstream tasks. Nonetheless, deploying these models on low-capability devices still requires an effective approach, such as model pruning. However, pruning the model from scratch can pose a practical challenge given the limited resources of each downstream task or device. To tackle this issue, we present a scalable one-shot pruning method that leverages pruned knowledge of similar tasks to extract a sub-network from the pre-trained model for a new task. Specifically, we create a score mask using the pruned models of similar tasks to identify task-specific filters/nodes in the pre-trained model for the new task. Based on this mask, we conduct a single round of pruning to extract a suitably-sized sub-network that can quickly adapt to the new task with only a few training iterations. Our experimental analysis demonstrates the effectiveness of the proposed method on the convolutional neural networks (CNNs) and vision transformers (ViT) with various datasets. The proposed method consistently outperforms popular pruning baseline methods in terms of accuracy and efficiency when dealing with diverse downstream tasks with different memory constraints

Dynamic Wireless QoS Analysis for Real-Time Control in URLLC

One of the major goals of ultra-reliable and low-latency communication (URLLC) is to enable real-time wireless control systems. However, it is challenging to use URLLC throughout the control process since a huge amount of wireless resource is needed to maintain the rigorous quality-of-service (QoS) in URLLC, i.e, ultra reliability and low latency. In this paper, our goal is to discuss that whether the extreme high QoS in URLLC leads to better control performance than low QoS during the control process. This is expected to provide a guideline on the usage of the URLLC throughout the control process dynamically. Specifically, we first investigate the relationship between the URLLC QoS and control performance. Then, we discuss the effect of different communication QoS on the control performance. Our results show that the rigorous QoS in URLLC and a low QoS can be used dynamically throughout the control process with high system performance

Advances in diagnosis and non-surgical treatment of Bell's palsy

AbstractBell's palsy is a commonly seen cranial nerve disease and can result in compromised facial appearance and functions. Its etiology, prognosis and treatment are still being debated. This paper is a review of recent development in the understanding of etiology, diagnosis and non-surgical treatment of Bell's palsy

Internet of Things and Sensors Networks in 5G Wireless Communications

The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic

Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary channel gain is estimated in the primary system and thus unavailable at the CT. To deal with this issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signals, a ML estimator is first developed. After demonstrating the high computational complexity of the ML estimator, a median based (MB) estimator with proved low complexity is then proposed. Furthermore, the estimation accuracy of the MB estimation is theoretically characterized. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Numerical results show that the estimation errors of the ML estimator and the MB estimator can be as small as $0.6$ dB and $0.7$ dB, respectively.Comment: Submitted to IEEE Transactions on Communication