No Arabic abstract
For enhancing transmission performance, privacy level and energy manipulating efficiency of wireless networks, this paper initiates a novel simultaneous wireless information and power transfer (SWIPT) full-duplex (FD) relaying protocol, termed harvest-and-opportunistically-relay (HOR). In the proposed HOR protocol, the relay can work opportunistically in either pure energy harvesting (PEH) or the FD SWIPT mode. Due to the FD characteristics, the dynamic fluctuation of Rs residual energy is difficult to quantify and track. To solve this problem, we apply a novel discrete-state Markov Chain (MC) method in which the practical finite-capacity energy storage is considered. Furthermore, to improve the privacy level of the proposed HOR relaying system, covert transmission performance analysis is developed and investigated, where closed-form expressions of optimal detection threshold and minimum detection error probability are derived. Last but not least, with the aid of stationary distribution of the MC, closed-form expression of transmission outage probability is calculated, based on which transmission outage performance is analyzed. Numerical results have validated the correctness of analyses on transmission outage and covert communication. The impacts of key system parameters on the performance of transmission outage and covert communication are given and discussed. Based on mathematical analysis and numerical results, it is fair to say that the proposed HOR model is able to not only reliably enhance the transmission performance via smartly managing residual energy but also efficiently improve the privacy level of the legitimate transmission party via dynamically adjust the optimal detection threshold.
Ambient backscatter communications is an emerging paradigm and a key enabler for pervasive connectivity of low-powered wireless devices. It is primarily beneficial in the Internet of things (IoT) and the situations where computing and connectivity capabilities expand to sensors and miniature devices that exchange data on a low power budget. The premise of the ambient backscatter communication is to build a network of devices capable of operating in a battery-free manner by means of smart networking, radio frequency (RF) energy harvesting and power management at the granularity of individual bits and instructions. Due to this innovation in communication methods, it is essential to investigate the performance of these devices under practical constraints. To do so, this article formulates a model for wireless-powered ambient backscatter devices and derives a closed-form expression of outage probability under Rayleigh fading. Based on this expression, the article provides the power-splitting factor that balances the tradeoff between energy harvesting and achievable data rate. Our results also shed light on the complex interplay of a power-splitting factor, amount of harvested energy, and the achievable data rates.
In this study, the problem of fault zone detection of distance relaying in FACTS-based transmission lines is analyzed. Existence of FACTS devices on the transmission line, when they are included in the fault zone, from the distance relay point of view, causes different problems in determining the exact location of the fault by changing the impedance seen by the relay. The extent of these changes depends on the parameters that have been set in FACTS devices. To solve the problem associated with these compensators, two instruments for separation and analysis of three-line currents, from the relay point of view at fault instance, have been utilized. The wavelet transform was used to separate the high-frequency components of the three-line currents, and the support vector machine (using methods for multi-class usage) was used for classification of fault location into three protection regions of distance relay. Besides, to investigate the effects of TCSC location on fault zone detection of distance relay, two places, one in fifty percent of line length and the other in seventy-five percent of line length, have been considered as two scenarios for confirmation of the proposed method. Simulations indicate that this method is effective in the protection of FACTS-based transmission lines.
Since transmission lines are crucial links in the power system, one line outage event may bring about interruption or even cascading failure of the power system. If a quick and accurate line outage detection and localization can be achieved, the system operator can take necessary actions in time to mitigate the negative impact. Therefore, the objective of this paper is to study a method for line outage detection and localization via synchrophasor measurements. The density of deployed phasor measurement units (PMUs) is increasing recently, which greatly improves the visibility of the power grid. Taking advantage of the high-resolution synchrophasor data, the proposed method utilizes frequency measurement for line outage detection and power change for localization. The procedure of the proposed method is given. Compared with conventional methods, it does not require the pre-knowledge on the system. Simulation study validates the effectiveness of the proposed method.
Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance of FedTOE for a deep learning-based classification task with transmission latency constraints.
In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the framework of full channel state information (F-CSI) feedback problem and its corresponding channel dataset are provided. Then the enhancing schemes for DL-based F-CSI feedback including i) channel data analysis and preprocessing, ii) neural network design and iii) quantization enhancement are elaborated. The final competition results composed of different enhancing schemes are presented. Based on the valuable experience of 1st WAIC, we also list some challenges and potential study areas for the design of AI-based wireless communication systems.