Power Communication Network Research Articles

Joint Power and 3D Trajectory Optimization for UAV-Enabled Wireless Powered Communication Networks With Obstacles

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of obstacles. In this work, we consider a UAV-enabled WPCN scenario that a UAV needs to cover the ground wireless devices (WDs). During the coverage process, the UAV needs to collect data from the WDs and charge them simultaneously. To this end, we formulate a joint-UAV power and three-dimensional (3D) trajectory optimization problem (JUPTTOP) to simultaneously increase the total number of the covered WDs, increase the time efficiency, and reduce the total flying distance of UAV so as to improve the energy utilization efficiency in the network. Due to the difficulties and complexities, we decompose it into two sub optimization problems, which are the UAV power allocation optimization problem (UPAOP) and UAV 3D trajectory optimization problem (UTTOP), respectively. Then, we propose an improved non-dominated sorting genetic algorithm-II with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means initialization operator and Variable dimension mechanism (NSGA-II-KV) for solving the UPAOP. For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem. Simulation results verify the effectiveness of the proposed strategies under different scales and settings of the networks.

Robust Resource Allocation Design for Secure IRS-Aided WPCN

This paper studies the robust resource allocation design for secure intelligent reflecting surface (IRS)-aided wireless powered communication networks (WPCN). In particular, deploying an IRS can establish favorable end-to-end radio propagation environment for achieving the desired performance gain in secure wireless-powered systems. We aim to minimize the total hybrid base station (HBS) transmit power by jointly designing the active transmitting and receiving beamforming at the HBS, the passive beamforming at the IRS, and the transmit power of each wireless-powered device (WD) and jammer node (JN). We formulate a non-convex optimization problem for the robust resource allocation design taking into account the secrecy rate requirement of the WDs and the power budgets for both the WDs and the JNs. To handle this intractable problem, we propose a computationally efficient iterative suboptimal algorithm exploiting the block coordinate descent approach, the successive convex approximation, and the penalty method, which attains a Karush-Kuhn-Tucker (KKT) solution of the transformed problem. Also, we reveal that the optimal energy beamforming matrices are rank-one sharing the same spatial direction. Simulation results unveil that the proposed scheme is able to dramatically reduce the HBS transmit power over various baseline schemes adopting existing solutions. Besides, our results show the superiority of introducing IRS for secure communication in wireless-powered systems.

Resource Scheduling for Intelligent Reflecting Surface-Assisted Full-Duplex Wireless-Powered Communication Networks With Phase Errors

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless powered communication networks (FD-WPCNs). This paper investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of formulated non-convex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method, and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

Multicast Spatial Filter Beamforming with Resource Allocation Using Joint Multi-objective Optimization Approaches in Wireless Powered Communication Networks

Multicast Spatial Filter Beamforming with Resource Allocation Using Joint Multi-objective Optimization Approaches in Wireless Powered Communication Networks

Q-learning-based sequential recovery of interdependent power-communication network after cascading failures

Q-learning-based sequential recovery of interdependent power-communication network after cascading failures

Consensus control for distributed power tracking by device-level digital twin agents

Abstract: The development of electronics and software has resulted in the ability of twin agents to act as digital counterparts for the optimization, control, and monitoring of real power grids. When increasing regulation resources are connected to the power grid, is challenging for independent system operators to use a centralized controller to achieve power tracking and balance. Therefore, the present study applied device-level-based digital twins to monitor physical signals for computer-aided design for power tracking. Moreover, a consensus control-based distributed power tracking system is proposed for the physical-model simulation of the power grid. A communication network was also designed for realistic signal exchange. The combination of the proposed distributed power tracking method and communication network can accelerate computational efficiency and protect the privacy of the regulation resources. Finally, the performance of the proposed distributed power tracking method is validated in a simulation system with 10 regulation resources.

Achievable Rates and Resource Allocation for CDMA-Based Overlay Cognitive Radio With RF Energy Harvesting

This article introduced a cognitive wireless powered communication network with an uplink (UL) code-division multiple access (CDMA) scheme consisting of one hybrid access point (H-AP) and multiple energy harvesting secondary users (SUs), which are coexisted with a primary wireless network. The frame structure is coordinated through the two-phase harvest-then-transmit protocol. The energy-limited SUs first harvest wireless energy in the downlink phase and then transmit their information signals simultaneously to H-AP with the CDMA scheme in the UL phase. An overlay spectrum sharing model is also considered where H-AP has access to the primary transmitter's codebook and primary network's channel. Our goal is to maximize the sum-throughput optimization problem of SUs via jointly optimizing power and time allocation. The considered joint optimization problem is generally nonconvex due to coupled variables. As a solution, we first transform it into the equivalent convex form using proper variables and then we solve it using the convex optimization technique. The closed-form solutions for time and power allocations are given. We also confirm from various simulations’ results that our proposed CDMA scheme is superior to the conventional time-division multiple access schemes.

Online Energy Consumption Optimization in WPCNs With Time-Varying Energy Storage Efficiency

This work considers a wireless powered communication network (WPCN), in which wireless nodes store the energy from an energy access point in their batteries for subsequent data transmission. An online energy consumption optimization strategy is proposed for adaptively determining the beamforming vector, data routing, network operation mode and transmitted power based only on the current state of WPCN. In most existing results, the energy/data transmission of WPCNs is based on the ideal battery models and the energy storage efficiencies therein are always assumed to be non-zero constants. Since the energy storage efficiency of batteries may be affected by the ambient environment or aging in real-time, this work considers a WPCN with a time-varying energy storage efficiencies sequence and correspondingly develops an improved Lyapunov optimization strategy to offset the impact of the time-varying energy storage efficiencies. More importantly, a distributed strategy is proposed to optimize the cooperation of wireless nodes over unrestricted numbers of hops, and thus the energy access point does not require channel state information of all data links and the data backlog queues of all nodes during the solving process. Accordingly, the computational burden at the energy access point is greatly reduced due to the use of this distributed strategy. Under this strategy, the time-averaged expected energy consumption of WPCN can be within a bounded gap of the minimum energy required to maintain stability of the network. Finally, the theoretical analysis is further corroborated by simulation results.

Dense Overload Subgraph Induced by Cyber–Physical Attacks in Smart Grid

With the tight couplings between the power grid and communication networks, smart grid is vulnerable to cyber-physical attacks, which can inject imperceptive perturbations to cause cascading failures. Nevertheless, as a dynamic and evolving process, it remains challenging to precisely lock the susceptible components that initiate a cascading event. To address this issue, we formulate a bi-level optimization problem in the context of cyber-physical attacks to model the attacker-operator adversarial game and generate an optimal attack strategy for electricity vulnerability assessments. Specifically, we merge the electrical overloading, topological proximity and outage correlations to construct a dense overload subgraph, which is critical for cascading propagation, by making use of fractional program to capture the density of subgraph with maximum average degree. We then compute exact solutions by adopting multiple linearization techniques to convert this bi-level fractional program into a mixed-integer linear program. Numerical studies in IEEE different-scale power systems verify the effectiveness of our model in understanding the cascading risk for a smart grid.

Sum Rate Maximization for Self-Sustainable IRS-Assisted UAV Communications

In this letter, we propose a novel time switching (TS) transmission scheme for intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In the energy harvesting (EH) phase, the operation of IRS is switched between energy harvesting and reflecting to improve the energy transfer. In the information transmitting (IT) phase, the operation of IRS is switched between assisted and unassisted IT to avoid the deficient duration of IT phase when the IRS is energy starved. We formulate an achievable sum-rate maximization problem by jointly optimizing the resource allocation, IRS phase shift and location of the UAV. To address the non-convexity resulting from coupling of variables, we divide original problem into three sub-problems and solve them with the block coordinate descent (BCD) method. The numerical results testify that the proposed scheme can obtain the significant sum-rate gain compared to the traditional TS scheme.

Enhancement of failure independent path protection preconfigured cycles for electric power communication networks

For the purpose of improving the resources utilization efficiency and decrease network redundancy of electric power communication network (EPCN), the paper explores using failure independent path protection preconfigured cycle (FIPP p-Cycle) to provide backup paths for electric communication service and presents a protection algorithm of enhanced FIPP p-Cycle with traffic grooming (EFIPP-TG). Firstly, the multiplexing conditions of FIPP p-Cycle are analyzed specifically, and we propose a FIPP p-Cycle multiplexing algorithm to improve the utilization efficiency of backup resources. Then segment protection algorithm for working path is designed to decrease service blocking ratio as much as possible. In addition, by taking advantage of traffic grooming strategy, the routing paths of services are optimized, yielding effective decrease of network blocking probability. Simulation results from different topologies demonstrate that compared with traditional FIPP p-Cycle scheme, other p-Cycle based protection algorithms and shared backup path protection algorithm, the proposed EFIPP-TG algorithm can significantly reduce the redundancy and blocking probability of networks, shorten the length of backup path and hence improve the reliability of service and performance of network survivability.

Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces

High-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure, and the safety of the associated telecommunication network from intrusion is crucial. Whereas events related to operation and maintenance are often available and carefully documented, only some tools have been proposed to discriminate the information dealing with the heterogeneous data from intrusion detection systems and to support the network engineers. In this work, we present the use of deep learning techniques, such as Autoencoders or conventional Multiple Correspondence Analysis, to analyze and prune the events on power communication networks in terms of categorical data types often used in anomaly and intrusion detection (such as addresses or anomaly description). This analysis allows us to quantify and statistically describe high-severity events. Overall, portions of alerts around 5-10% have been prioritized in the analysis as first to handle by managers. Moreover, probability clouds of alerts have been shown to configure explicit manifolds in latent spaces. These results offer a homogeneous framework for implementing anomaly detection prioritization in power communication networks.

WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

As the scale of power networks has expanded, the demand for multi-service transmission has gradually increased. The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks. However, it still cannot cope with situations such as wireless access point (AP) failure. To solve this problem, this paper combines orthogonal frequency division multiple access (OFDMA) technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails. First, under the premise of AP layout with strong coverage over the whole area, a faulty AP determination method based on beacon frames (BF) is designed. Then, the maximum signal-to-interference ratio (SINR) is used as the principle to select AP reconnection for the affected users. Finally, this paper designs a dynamic access selection model (DASM) for service frames of power Internet of Things (IoTs) and a scheduling access optimization model (SAO-MF) based on multi-frame transmission, which enables access optimization for differentiated services. For the above mechanisms, a heuristic resource allocation algorithm is proposed in SAO-MF. Simulation results show that the method can reduce the delay by 15% and improve the throughput by 55%, ensuring high-quality communication in power wireless networks.

Reliable Scheduling Method for Sensitive Power Business Based on Deep Reinforcement Learning

The main function of the power communication business is to monitor, control and manage the power communication network to ensure normal and stable operation of the power communication network. Communication services related to dispatching data networks and the transmission of fault information or feeder automation have high requirements for delay. If processing time is prolonged, a power business cascade reaction may be triggered. In order to solve the above problems, this paper establishes an edge object-linked agent business deployment model for power communication network to unify the management of data collection, resource allocation and task scheduling within the system, realizes the virtualization of object-linked agent computing resources through Docker container technology, designs the target model of network latency and energy consumption, and introduces A3C algorithm in deep reinforcement learning, improves it according to scene characteristics, and sets corresponding optimization strategies. Minimize network delay and energy consumption; At the same time, to ensure that sensitive power business is handled in time, this paper designs the business dispatch model and task migration model, and solves the problem of server failure. Finally, the corresponding simulation program is designed to verify the feasibility and validity of this method, and to compare it with other existing mechanisms.

Implementation of Risk-Aggregated Substation Testbed Using Generative Adversarial Networks

Capturing the anomalies of a cyber system in power control networks would promote operational awareness. Correlation of such events, e.g., intrusion attempts, traffic flow, and other signatures, together with control alarm events gives operators an in-depth understanding in order to make an informed decision. This paper proposes a threat inference framework to promote real-time vulnerability assessment associated with cyber intrusions on power communication networks. Wasserstein Generative Adversarial Networks (WGAN) is proposed to estimate the performance of the adversarial model. Additionally, a machine-learning framework is introduced to model the filtering process of the security devices, i.e., firewalls, isolation, and encryption devices, and the posterior fitting method is incorporated to establish an accurate probabilistic formulation. Finally, a testbed is established to coordinate system evaluation. Verification of the intrusion model is part of the implementation to quantify system risks based on the anomalies using (1) the open-source emulator, and (2) an externally imported system analyzer to characterize resulting impacts. The effectiveness and feasibility of the generative models are verified in a comparison study where the proper parameter settings could be obtained. The proposed framework is justified with extensive studies of substation networks using real-world settings.

Data-Integrity Aware Stochastic Model for Cascading Failures in Power Grids

The reliable operation of power grids during cascading failures is heavily dependent on the interdependencies between the power grid components and the supporting communications and control networks. Moreover, the system operators’ expertise in dealing with cascading failures can play a pivotal role during contingencies. In this paper, a dynamical probabilistic model is developed based on Markov-chains, which captures the dynamics of cascading failures in the power grid. Specifically, a previously developed Markov-chain based model is extended to capture the trade-off between the benefits of having a robust communication infrastructure and its vulnerability from data integrity (e.g., cyber-attacks). State-space reduction of the complex interactions between power grids, communication networks and system operators is achieved by judiciously specifying the state variables of the Markov chain. The impact of system operators’ probability of error during a cascade-mitigation action is incorporated into the model as a function of the state variables of the Markov chain. A point of diminishing returns is observed beyond which the effect of information infidelity outweighs the benefits of having more information. For a given level of cyber threat, an optimal size of a communication network is observed that minimizes the expected number of transmission-line failures before the cascade stops.

Critical node identification in network cascading failure based on load percolation

&lt;abstract&gt; &lt;p&gt;Identification of network vulnerability is one of the important means of cyberspace operation, management and security. As a typical case of network vulnerability, network cascading failures are often found in infrastructure networks such as the power grid system, communication network and road traffic, where the failure of a few nodes may cause devastating disasters to the whole complex system. Therefore, it is very important to identify the critical nodes in the network cascading failure and understand the internal laws of cascading failure in complex systems so as to fully grasp the vulnerability of complex systems and develop a network management strategy. The existing models for cascading failure analysis mainly evaluate the criticality of nodes by quantifying their importance in the network structure. However, they ignore the important load, node capacity and other attributes in the cascading failure model. In order to address those limitations, this paper proposes a novel critical node identification method in the load network from the perspective of a network adversarial attack. On the basis of obtaining a relatively complete topology, first, the network attack can be modeled as a cascading failure problem for the load network. Then, the concept of load percolation is proposed according to the percolation theory, which is used to construct the load percolation model in the cascading failure problem. After that, the identification method of critical nodes is developed based on the load percolation, which accurately identifies the vulnerable nodes. The experimental results show that the load percolation parameter can discover the affected nodes more accurately, and the final effect is better than those of the existing methods.&lt;/p&gt; &lt;/abstract&gt;

Limiting the Failure Impact of Interdependent Power-Communication Networks via Optimal Partitioning

The mutual dependency between the power grid and communication network increases the risk of large-scale cascading failure, making the mitigation of outages a difficult task. In this paper, we use the concept of joint partitioning of the power and communication subsystems by identifying the most vulnerable typed-graphlets. This results in higher-order partitions, which prevent cascading failures from propagating globally in the system. For this purpose, we first formulate the problem of partitioning the interdependent system as a large-scale optimization problem with the objective of minimizing the overall load shedding subject to: i) power flow convergence, ii) power stability, iii) partition connectivity, iv) communication congestion/delay control, and v) a high partition quality. Then, using Benders’ method, we solve the problem by decomposing it into a relaxed master problem that consists of constrained spectral clustering, and a linear subproblem that consists of minimizing the load shedding subject to power flow convergence, stability, and congestion/delay control. Simulations are conducted on an IEEE 118-bus supported by a 118-node communication network. Our investigations reveal an average of 62% decrease in damage when the system is optimally partitioned.

Joint Energy and Information Precoding for NOMA-Based WPCNs Aided by Reconfigurable Intelligent Surface

In a wireless powered communication network (WPCN), wireless energy acquisition is weak due to the fading characteristic of wireless link, which makes it hard to supply the energy. Reconfigurable intelligent surface (RIS) is a promising performance enhancement technology for WPCNs, which can improve the energy and spectral efficiency, expand the network coverage, and thus improve the throughput. This article proposes a new framework for energy harvesting and information transmission of non-orthogonal multiple access (NOMA)-based WPCNs aided by RIS. The total transmission capacity is maximized by joint optimization of energy precoding, information precoding, and the phase shift of RIS, which turns out to be a non-convex problem. Under the multi user scenario, we propose a joint optimization algorithm combing alternating optimization (AO), semidefinite relaxation (SDR), and successive convex approximation (SCA) methods, which is called ASS algorithm for short. Numerical results prove that compared with the non-RIS system, the ASS can increase the throughput of the system under multiuser scenario by 502.49%. For the system under single scenario, the energy precoding and information precoding are determined respectively by using the beamforming and the traditional maximal ratio combining (MRC) algorithm. The optimization problem is transformed into a complex higher order form optimization problem. Finally, a closed-form expression of the channel capacity is derived.

Markov-Chain Based Performance Analysis and Evaluation of Harvest-Then-Access Scheduling for Wireless Powered Communication Network

Markov-Chain Based Performance Analysis and Evaluation of Harvest-Then-Access Scheduling for Wireless Powered Communication Network