Power Communication Network Research Articles

Design of an 8X8 Optical Matrix Protection Device based on Electric Power Communication Network

In order to realize the fast establishment of power fiber circuitous channels, the reliability of power communication optical cable network is guaranteed. An optical matrix 8X8 protection device is designed by using all-optical switcher. The test results show that at the end of each input light path, any 8 paths can be switched, it can achieve 1:8 protection switch, realized the arbitrary switching of three modes and has the function of remote fiber route scheduling.

Capacity Analysis of Power Beacon-Assisted Energy Harvesting MIMO System Over $\kappa -\mu$ Shadowed Fading Channels

In this paper, novel ergodic capacity (EC) performance evaluation results of a power beacon (PB)-assisted multi-input multi-output (MIMO) wireless powered communication network are presented. In the considered system, the energy harvesting node harvests energy from the radio-frequency signals sent by the dedicated PB and uses this energy to communicate with the destination node. To accurately model the combined effect of multi-path fading and shadowing, it is assumed that the energy transfer link is subject to $\kappa$ - $\mu$ shadowed fading. Performance evaluation results are presented for two cases, depending upon the availability of channel state information (CSI) at the PB, namely, $no$ CSI and $full$ CSI. In the former case, equal power allocation is assumed, whereas, in the later case, energy beamforming is employed to increase energy transfer efficiency. For the performance evaluation of EC under $full$ CSI, a closed-form approximation for the probability density function of the maximum eigenvalue of a $\kappa$ - $\mu$ shadowed distributed random matrix is derived. For both $no$ CSI and $full$ CSI cases, lower and upper bounds on the achievable EC are derived in closed-form. Moreover, in order to obtain further insights on the impact of key parameters on the system performance, asymptotic EC expressions which become very tight at low- and high-signal-to-noise ratio regimes, are obtained. Using the proposed EC lower bound as well as these asymptotic results, simple closed-form expressions for the optimal time split that maximize the achievable EC are derived. Numerically evaluated results accompanied with Monte-Carlo simulations are further presented to corroborate the theoretical analysis.

Fuzzy Based Cluster Greedy Optimization and Convolutional Neural Networks Based Scheme for Internet of Medical Things Based Healthcare Resource Allocation in Cognitive Wireless Powered Communication Network

A cognitive wireless powered communication network (CWPCN) for spectrum distribution in IoMT based healthcare systems is employed with a principal network, which in turn deals with security issues from various attacks like Denial of Service (DoS), Man-In-the-Middle, or phishing attacks. In this, a new protocol is proposed for wireless powered SU (secondary users) so as to cooperate with PU (primary user) of the healthcare network. At the time of wireless power transfer (WPT) in a IoMT based healthcare network, the first harvest energy of SUs was carried from power signals broadcasted by the cognitive hybrid access point. Then the harvested energy is employed while gaining transmission opportunities simultaneously all through the phase of Wireless Information Transfer (WIT) of healthcare system. Furthermore, Fuzzy based cluster greedy algorithm is introduced for reducing the interruption of PU secrecy prospect and to offer the best optimal values in the healthcare data. In this approach, the injection impact and reactive jamming attacks on wireless transmission are analyzed. These can be recognized through a Convolutional Neural Network (CNN) to detect different attack types and classify them. Finally, the results were compared with the existing method.

Backscatter-Assisted Wireless Powered Communication Networks Empowered by Intelligent Reflecting Surface

Intelligent reflecting surface (IRS) has recently been emerged as an effective way for improving the performance of wireless networks by reconfiguring the propagation environment through a large number of passive reflecting elements. This game-changing technology is especially important for stepping into the Internet of Everything (IoE) era, where high performance is demanded with very limited available resources. In this paper, we study a backscatter-assisted wireless powered communication network (BS-WPCN), in which a number of energy-constrained users, powered by a power station (PS), transmit information to an access point (AP) via backscatter and active wireless information transfer, with their communication being aided by an IRS. Using a practical energy harvesting (EH) model which is able to capture the characteristics of realistic energy harvesters, we investigate the maximization of total network throughput. Specifically, IRS reflection coefficients, PS transmit and AP receive beamforming vectors, power and time allocation are designed through a two-stage algorithm, assuming minimum mean square error (MMSE) receiver at the AP. The effectiveness of the proposed algorithm is confirmed via extensive numerical simulations. We also show that our proposed scheme is readily applicable to practical IRS-aided networks with discrete phase shift values.

Energy Minimization for IRS-Aided WPCNs With Non-Linear Energy Harvesting Model

This letter considers an intelligent reflecting surface(IRS)-aided wireless powered communication network (WPCN), where devices first harvest energy from a power station (PS) in the downlink (DL) and then transmit information using non-orthogonal multiple access (NOMA) to a data sink in the uplink (UL). However, most existing works on WPCNs adopted the simplified linear energy-harvesting model and also cannot guarantee strict user quality-of-service requirements. To address these issues, we aim to minimize the total transmit energy consumption at the PS by jointly optimizing the resource allocation and IRS phase shifts over time, subject to the minimum throughput requirements of all devices. The formulated problem is decomposed into two subproblems, and solved iteratively in an alternative manner by employing difference of convex functions programming, successive convex approximation, and penalty-based algorithm. Numerical results demonstrate the significant performance gains achieved by the proposed algorithm over benchmark schemes and reveal the benefits of integrating IRS into WPCNs. In particular, employing different IRS phase shifts over UL and DL outperforms the case with static IRS beamforming.

Deep learning-based optimal placement of a mobile HAP for common throughput maximization in wireless powered communication networks

Hybrid access point (HAP) is a node in wireless powered communication networks (WPCN) that can distribute energy to each wireless device and also can receive information from these devices. Recently, mobile HAPs have emerged for efficient network use, and the throughput of the network depends on their location. There are two kinds of metrics for throughput, that is, sum throughput and common throughput; each is the sum and minimum value of throughput between a HAP and each wireless device, respectively. Likewise, two types of throughput maximization problems can be considered, sum throughput maximization and common throughput maximization. In this paper, we focus on the latter to propose a deep learning-based methodology for common throughput maximization by optimally placing a mobile HAP for WPCN. Our study implies that deep learning can be applied to optimize a complex function of common throughput maximization, which is a convex function or a combination of a few convex functions. The experimental results show that our approach provides better performance than mathematical methods for smaller maps.

Minimum Length Scheduling for Multi-Cell Full Duplex Wireless Powered Communication Networks.

Wireless powered communication networks (WPCNs) will be a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. These MTC networks will be deployed by using low-power transceivers and a very limited set of transmission configurations. We investigate a novel minimum length scheduling problem for multi-cell full-duplex wireless powered communication networks to determine the optimal power control and scheduling for constant rate transmission model. The formulated optimization problem is combinatorial in nature and, thus, difficult to solve for the global optimum. As a solution strategy, first, we decompose the problem into the power control problem (PCP) and scheduling problem. For the PCP, we propose the optimal polynomial time algorithm based on the evaluation of Perron–Frobenius conditions. For the scheduling problem, we propose a heuristic algorithm that aims to maximize the number of concurrently transmitting users by maximizing the allowable interference on each user without violating the signal-to-noise-ratio (SNR) requirements. Through extensive simulations, we demonstrate a 50% reduction in the schedule length by using the proposed algorithm in comparison to unscheduled concurrent transmissions.

Sum-Rate Maximization in IRS-Assisted Wireless Power Communication Networks

Wireless-powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this article, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station, and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

Energy Efficiency Maximization for IRS-Aided WPCNs

Intelligent reflecting surface (IRS) is a revolutionary technology for achieving spectral- and energy-efficient wireless networks. By adjusting the phase shifts at the IRS, a high passive beamforming gain can be achieved, which is particularly appealing for improving the spectral efficiency of wireless powered communication networks (WPCNs). Inspired by this, we study IRS-aided WPCNs, where the IRS is leveraged to help the energy transmitter (ET) broadcast the energy signals to the distributed devices in the downlink (DL), and meanwhile help the distributed devices forward the information signals to the information receiver (IR) in the uplink (UL). By taking account into the system energy consumption and achievable system throughput, we aim to maximize the energy efficiency (EE) of WPCNs by jointly optimizing phase shifts at the IRS, the beamformer at the ET, time allocation, and the transmit power of devices while guaranteeing the minimum achievable rate requirements for the devices. Since the formulated optimization problem is a nonlinear fractional programming problem, which is non-convex. To tackle this difficulty, an alternating optimization (AO) algorithm is proposed. In particular, a Dinkelbach-based algorithm is proposed to optimize the resource allocation, and a penalty-based algorithm based on the difference of convex (DC) programming and successive convex approximation (SCA) techniques is proposed to solve the phase shift optimization. Simulation results unveil that the IRS is capable of improving the system EE compared to the benchmarks.

Information acquisition technology for small hydropower stations in remote areas based on high- and low-orbit satellites

With a lack of coverage in private and public power communication networks, especially for collection of information from hydropower stations in remote areas, communication coverage is a significant issue. Satellite communication provides a large coverage area suitable for a variety of services and is less affected by geographical factors; moreover, the costs are independent of the communication distance. This study investigates information acquisition technology for small hydropower stations in remote areas using high- and low-orbit satellites. The information collection needs of small hydropower stations in remote areas are analyzed, and an information acquisition system is designed using high- and low-orbit satellites. For network security protection, network anomaly detection technology based on a support vector machine algorithm is proposed. The effectiveness of information collection was evaluated and verified for small hydropower plants in remote areas. The system provides technical support for “full coverage, full collection, and full monitoring” of the measurement automation information acquisition system.

Optimal SIC Decoding Order and WPT Time Length for WPCN With Imperfect SIC

Wireless Power Transfer (WPT) and Non-Orthogonal Multiple Access (NOMA) are two emerging technologies towards next generation wireless networks. In this letter we assume the integration of those two technologies in Wireless Powered Communication Network (WPCN). We evaluate the impact of WPT time length and Successive Interference Cancellation (SIC) decoding order for an arbitrary number of terminals by assuming imperfect SIC. The optimal solution can be obtained by optimization solvers whereas a suboptimal solution is conceived based on analytical insights. Numerical results show that the decoding order is relevant for spectral efficiency maximization when imperfect SIC is assumed. Also, the proposed low-complexity solution shows a quasi-optimal performance.

A novel network flow prediction method based on UWB in power communication networks

A novel network flow prediction method based on UWB in power communication networks

Age-Aware Utility Maximization in Relay-Assisted Wireless Powered Communication Networks.

This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP–sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP–sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP–sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.

Design of Remote Monitoring System for Substation DC Power Supply under the Background of Big Data

The DC power supply(DPS) system is an important part of the substation. In order to adapt to the current unmanned operation and maintenance mode of the substation and the new requirements of state maintenance and management, it must develop in the general direction of digitalization, programming and intelligence. To ensure the safety and stability of the new operation and maintenance mode of the power system. Through the design and application research of the DPS remote monitoring system of the substation, the real-time data collection of the domestic DPS device is realized, the collected data information is summarized, and the cooperation of the remote control center and related operation and maintenance management teams is requested. The management software effectively analyzes the collected equipment operating parameters and real-time data, extracts fault information and hidden dangerous data required for operation and maintenance, which is helpful for the scientific management and risk prediction of DC power equipment in substations. The operation and maintenance personnel and the DPS system of the substation ensure the operation in a high-quality and healthy state and the safe and stable operation of the entire power network. This paper studies the design principle, structure and function of the DPS remote monitoring system used in the substation. On this basis, in-depth study of battery voltage equalization technology, internal resistance measurement technology and other core technologies related to DPS remote monitoring system design. In order to realize the online monitoring, analysis and diagnosis of the operation information data of DPS devices such as remote charging and discharging operation safety management strategy, DC power information collection and communication network design, the theoretical and technical foundations have been formulated.

Optimization of Backbone Optical Network for Power Communication Based on Critical Link

The existing optimization algorithms for power communication backbone optical networks do not comprehensively consider the real-time performance of power communication services and the difference of substation voltage levels, and the optimization results are difficult to meet the actual needs of power communication networks. Firstly, this paper uses the link critical comprehensive index to express the characteristics of critical links, and designs a critical link identification algorithm; then, on the basis of link critical ranking, the network performance is optimized by using link redundancy and link expansion methods; finally, the feasibility and correctness of the method are verified by an example of provincial power backbone optical network. The research results have reference value for the planning, design and operation management of power communication network.

Secure communication in wireless powered communication networks with energy accumulation

Secure communication in wireless powered communication networks with energy accumulation

Throughput Maximization for IRS-Assisted Wireless Powered Hybrid NOMA and TDMA

The high reflect beamforming gain of the intelligent reflecting surface (IRS) makes it appealing not only for wireless information transmission but also for wireless power transfer. In this letter, we consider an IRS-assisted wireless powered communication network, where a base station (BS) transmits energy to multiple users grouped into multiple clusters in the downlink, and the clustered users transmit information to the BS in the manner of hybrid non-orthogonal multiple access and time division multiple access in the uplink. We investigate optimizing the reflect beamforming of the IRS and the time allocation among the BS’s power transfer and different user clusters’ information transmission to maximize the throughput of the network, and we propose an efficient algorithm based on the block coordinate ascent, semidefinite relaxation, and sequential rank-one constraint relaxation techniques to solve the resultant problem. Simulation results have verified the effectiveness of the proposed algorithm and have shown the impact of user clustering setup on the throughput performance of the network.

NOMA for Wireless-Powered Communication Networks With Buffered Sources

To increase the feasibility of wireless powered communication networks (WPCNs), the synergy of efficient multiple access protocols and diverse types of resources, e.g., multiple antennas, memories, and energy storage devices is of paramount importance. To this end, in this paper, we introduce the use of buffered sources in WPCNs with uplink non-orthogonal multiple access, in which a multi-antenna hybrid access point can both transmit energy and receive information. The proposed scheme aims at maximizing the long-term utility function, which is determined by the achieved average data rate and fairness among sources. The formulated long-term time-average optimization problem is converted into instantaneous ones by exploiting the Lyapunov optimization framework. The derived analytical solutions reveal the impact of data buffers’ length on the decoding order of sources’ messages. Finally, it is shown that the proposed scheme achieves superior long-term utility compared to the considered baseline schemes with non-buffered sources and buffered sources with orthogonal multiple access. Additionally, it is demonstrated that our proposed scheme can ensure fairness without reducing the average data rate.

A NOMA-Combined Hybrid Approach for SDMA Improvement in Wireless Powered Communication Networks

Wireless Powered Communication Network (WPCN) consists of Hybrid Access Point (HAP) that performs power transmission and data collection at the same time, and multiple nodes that can transmit data. In WPCN, depending on the wireless communication environment, the nodes cannot be able to transmit data because they can fail to receive power. Hence, increasing the transmission rate under a given resource is one of the very important issues. In ordinary mobile communications, a cell is divided into several sectors and the data is collected through multiple antennas to increase the transmission rate using SDMA. As a result, if the number of nodes in the one sector increases, the interference between nodes increases, and the transmission rate may decrease. Accordingly, in order to maximize performance, the number of nodes that can exist in a sector must be limited. The transmission rate between nodes according to the distance difference may not be fair because the nodes far from the HAP charge a small amount of power by attenuation of the signal, and the nodes close to the HAP charge a relatively large amount of power. Therefore, we propose Hybrid SDMA and Non-Orthogonal Multiple Access (NOMA) as a way to maximize the performance in term of both Sum-Throughput and Fairness. Also, we prove that there is a tradeoff between Sum-Throughput and Fairness according to the number of sectors. The simulation results show that the Hybrid SDMA and NOMA improves the performance substantially compared to the conventional SDMA.

Resources optimization for secure transmission in wireless powered communication networks

This paper studies the secrecy communication on wireless powered communication network (WPCN) in an orthogonal frequency division multiplexing (OFDM) system, where an energy harvest (EH) user sends confidential information to the hybrid access point (HAP) in the presence of a potential eavesdropper. We introduce a wireless powered cooperative jammer to improve the secrecy performance of this system. For the user and the jammer, we both consider the linear energy harvesting model and the nonlinear energy harvesting model. More specifically, the half-duplex WPCN is considered first. We study secrecy rate maximization problem by jointly optimizing the time allocation and the transmit power allocation for the HAP, the jammer and the user over sub-carriers. The nested form algorithm is proposed to solve the original non-convex problem, where one-dimension search method is used at the outer layer and a convex approximation algorithm by leveraging DC-based optimization is adopted at the inner layer. Then, the full-duplex WPCN is considered. We formulated the secrecy rate maximization problem and optimized the transmit power by using the DC algorithm and convex optimization. Simulation results demonstrate that the proposed scheme dramatically enhances the secrecy rate compared to the benchmark schemes.