Power Communication Network Research Articles

Performance Analysis of Cognitive Wireless Powered Communication Networks Under Unsaturated Traffic Condition

By improving the efficiency of wireless power transfer (WPT), wireless powered communication networks (WPCNs) are receiving increasing attention. WPCN provides untethered mobility and prolongs the network lifetime by eliminating the need for repetitive charging and replacement of the battery. In this paper, we consider a cognitive WPCN in which wireless powered secondary users (SUs) opportunistically exploit the spectrum licensed to the primary users (PUs). Each SU is associated with a power beacon (PB) node which is responsible for charging the corresponding SU and receiving its data over different frequency bands. SUs have unsaturated data traffic and can transmit if they are out of any guard zone which is defined around active PUs to prevent strong interference. Using tools from stochastic geometry and queueing theory, we characterize the effects of the randomness in data and energy availability of SUs on the interference among PUs and SUs. Then, we derive the service time distribution, mean waiting time, and queue stability criterion for a typical SU, as well as the outage probability of a typical PU. Finally, through extensive simulations, the analytical results are evaluated and the effects of different parameters on the network performance are studied.

Efficient, Fair, and QoS-Aware Policies for Wirelessly Powered Communication Networks

In this paper, we propose efficient wireless power transfer (WPT) policies for various practical scenarios in wirelessly powered communication networks (WPCNs). First, we consider WPT from an energy access point (E-AP) to multiple energy receivers (E-Rs). We formulate the problem of maximizing the total average received power of the E-Rs subject to power constraints of the E-AP, which is a non-convex stochastic optimization problem. Using eigenvalue decomposition techniques, we derive a closed-form expression for the optimal policy, which requires the distribution of the channel state information (CSI) in the network. We then propose a near-optimal policy that does not require this knowledge and prove that its optimality gap can be decreased at the cost of increment in its convergence time. Next, we consider fairness among the E-Rs and propose a quality of service (QoS) aware fair policy that provides fairness and guarantees the required QoS of each E-R. Finally, we study a WPCN where the E-Rs utilize their received energy to transmit information to the E-AP. We maximize a generic fair network utility under the E-Rs’ QoS constraints and the E-AP’s power constraints. Numerical results show a significant improvement of $O(\log {N})$ in the total throughput compared to the state-of-the-art baselines.

Minimum Length Scheduling for Full Duplex Time-Critical Wireless Powered Communication Networks

Radio frequency (RF) energy harvesting is key in attaining perpetual lifetime for time-critical wireless powered communication networks (WPCNs) due to full control on energy transfer, far field region, small and low-cost circuitry. In this paper, we propose a novel minimum length scheduling problem to determine the optimal power control, time allocation and schedule subject to data, energy causality and maximum transmit power constraints in a full-duplex WPCN. We first formulate the problem as a mixed integer non-linear programming problem and conjecture that the problem is NP-hard. As a solution strategy, we demonstrate that the power control and time allocation, and the scheduling problems can be solved separately in the optimal solution. For the power control and time allocation problem, we derive the optimal solution by evaluating Karush-Kuhn-Tucker conditions. For the scheduling, we introduce a penalty function allowing reformulation of the problem as a sum penalty minimization problem. Upon derivation of the optimality conditions based on the characteristics of the penalty function, we propose two polynomial-time heuristic algorithms and a reduced-complexity exact algorithm employing smart pruning techniques. Via extensive simulations, we illustrate that the proposed heuristic schemes outperform the schemes for predetermined transmission order of users and achieve close-to-optimal solutions.

Sum‐throughput maximization for overlay cognitive wireless powered network with energy harvesting capability

AbstractIn this article, a novel time division multiple access (TDMA)‐based approach for optimal resource allocation in an overlay cognitive wireless powered communication network is proposed. In this network, power in the secondary network (SN) is provided by the primary network (PN) in addition to its own wireless network. Using the TDMA and a harvest‐then‐transmit protocol, the time and power allocation for downlink wireless energy transfer (WET) and uplink wireless information transmission (WIT) are jointly optimized to maximize the sum‐throughput in SN. Quality of service in PN and power and time in SN, as the main constrains are considered for sum‐throughput maximization in this network. This is a non‐convex problem which gets converted to a convex problem using active interference‐temperature control technique. Moreover, a new closed‐form expression which results in sub‐optimal power and time‐allocation (SO‐PT‐A) is addressed. The performance of proposed algorithm is compared with the equal time allocation (ETA) and fixed TDMA‐allocation (FTDMA‐A) algorithms which impose some special constraints for optimal power and time allocation. The simulation results depict that the SO‐PT‐A algorithm outperforms the ETA and FTDMA‐A algorithms. Eventually, the performance of the SO‐PT‐A algorithm is evaluated with other well‐known algorithms considering different values of the average and maximum transmit power of the hybrid‐access point (H‐AP), the number of secondary users (SUs) and path loss exponent.

A link addition method based on uniformity of node degree in interdependent power grids and communication networks

In order to enhance the robustness of interdependent power grids and communication networks, a link addition strategy is proposed, which obeys the partial one-to-one correspondence between power nodes and communication nodes. In this method, the target value of node degree is set and then the number of addition links is determined according to its degree for each node. Our strategy aims to homogenize degree distribution of nodes. Case study on IEEE test systems based on the time-step failure model of nodes shows that the proposed method can obtain better robustness and synchronization of interdependent networks in the case of random and intentional attacks. The proposed strategy can provide guidance on current topic to improve robustness of interdependent networks.

Energy-Delay Tradeoff Scheme for NOMA-Based D2D Groups With WPCNs

An integration of nonorthogonal multiple access (NOMA) in device-to-device (D2D) mobile groups (DMGs) generates a large amount of energy and delay due to sharing the same subcarrier. In this article, we investigate a distributed energy-efficient and delay tradeoff scheme for NOMA-based DMGs in wireless powered communication networks. The stochastic optimization problem is formulated to maximize the energy efficiency and minimize the delay. Many-to-one matching-based subcarrier allocation, weighted sum method, and Lyapunov optimization scheme are used to investigate the possible solution. Further, to obtain the closed-form expression of power and to optimize the time for wireless powered communication networks, we use Lagrangian function. Simulation results show that the proposed scheme achieves 2.6% and 15.78% superior performance than the existing NOMA and orthogonal frequency division multiple access (OFDMA) schemes under the condition of constant circuit power consumption.

Throughput maximization in discrete rate based full duplex wireless powered communication networks†

In this study, we consider a discrete rate full‐duplex wireless powered communication network. We characterize a novel optimization framework for sum throughput maximization to determine the rate adaptation and transmission schedule subject to energy causality and user transmit power. We first formulate the problem as a mixed integer nonlinear programming problem, which is hard to solve for a global optimum in polynomial‐time. Then, we investigate the characteristics of the solution and propose a polynomial time heuristic algorithm for rate adaptation and scheduling problem. Through numerical analysis, we illustrate that the proposed scheduling algorithm outperforms the conventional schemes such as equal time allocation half‐duplex and on‐off transmission schemes for different initial battery levels, hybrid access point transmit power and network densities.

HYBRID-CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN-Based Smart Grid

Software-Defined Network (SDN) can improve the performance of the power communication network and better meet the control demand of the Smart Grid for its centralized management. Unfortunately, the SDN controller is vulnerable to many potential network attacks. The accurate detection of abnormal flow is especially important for the security and reliability of the Smart Grid. Prior works were designed based on traditional machine learning methods, such as Support Vector Machine and Naive Bayes. They are simple and shallow feature learning, with low accuracy for large and high-dimensional network flow. Recently, there have been several related works designed based on Long Short-Term Memory (LSTM), and they show excellent ability on network flow analysis. However, these methods cannot get the deep features from network flow, resulting in low accuracy. To address the above problems, we propose a Hybrid Convolutional Neural Network (HYBRID-CNN) method. Specifically, the HYBRID-CNN utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional (2D) data. Finally, the proposed method is evaluated by experiments on the datasets of UNSW_NB15 and KDDCup 99. The experimental results show that the HYBRID-CNN significantly outperforms existing methods in terms of accuracy and False Positive Rate (FPR), which successfully demonstrates that it can effectively detect abnormal flow in the SDN-based Smart Grid.

MLPRA: An MCDS and Link-Priority-Based Network Repair Algorithm for Smart Grid

The power system is an infrastructure for industrial manufacturing, and its availability is relevant to industrial systems. The smart grid combines communication systems with sensing devices to provide intelligent management tools for fault monitoring and processing of the power grid. Regional failures caused by natural disasters can have a large impact on the power system. To reduce the impact of disasters, an efficient network repair strategy is needed. This article focuses on the emergency repair strategy of a power communication network under disaster conditions and seeks to ensure the operation of the power system with fewer repairs. Combined with the analysis of cascading failures and regional failures, a communication network fast repair algorithm is proposed. The coupled network is constructed in the simulation part to verify the algorithm. The results show that with a limited number of node repairs, the algorithm can ensure the highest percentage of workable nodes.

GridAttackSim: A Cyber Attack Simulation Framework for Smart Grids

The smart grid system is one of the key infrastructures required to sustain our future society. It is a complex system that comprises two independent parts: power grids and communication networks. There have been several cyber attacks on smart grid systems in recent years that have caused significant consequences. Therefore, cybersecurity training specific to the smart grid system is essential in order to handle these security issues adequately. Unfortunately, concepts related to automation, ICT, smart grids, and other physical sectors are typically not covered by conventional training and education methods. These cybersecurity experiences can be achieved by conducting training using a smart grid co-simulation, which is the integration of at least two simulation models. However, there has been little effort to research attack simulation tools for smart grids. In this research, we first review the existing research in the field, and then propose a smart grid attack co-simulation framework called GridAttackSim based on the combination of GridLAB-D, ns-3, and FNCS. The proposed architecture allows us to simulate smart grid infrastructure features with various cybersecurity attacks and then visualize their consequences automatically. Furthermore, the simulator not only features a set of built-in attack profiles but also enables scientists and electric utilities interested in improving smart grid security to design new ones. Case studies were conducted to validate the key functionalities of the proposed framework. The simulation results are supported by relevant works in the field, and the system can potentially be deployed for cybersecurity training and research.

Time Allocation and Optimization in Time-Reversal Wireless Powered Communication Networks

This paper studies optimal resource allocation in the wireless powered communication networks (WPCN) combined with time reversal (TR) in which one hybrid access point (H-AP) broadcasts constant wireless energy to a set of distributed users in the downlink (DL) and receives information from the users via space division multiple access (SDMA) in the uplink (UL). Inevitable interferences will occur when users transmit information in the UL simultaneously and the special space-time focusing of TR is used to suppress the interferences. An efficient protocol is proposed to support wireless energy transfer (WET) and TR in the DL and wireless information transmission in the UL for the proposed TR-WPCN. We optimize the time allocations to the H-AP for DL WET, DL TR, and UL WIT to maximize the sum throughput. Due to the nonconvexity of the studied optimization problem, we optimize variables successively, where the nonconvex optimization problem is transformed into the convex optimization problem. The approximate convex optimization problem can then be solved iteratively combined with the dichotomy method. Simulation results show that the proposed scheme can effectively suppress interferences and improve system performance.

Exploiting a Deep Neural Network for Efficient Transmit Power Minimization in a Wireless Powered Communication Network

In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.

Common Throughput Maximization in Wireless Powered Communication Networks With Non-Orthogonal Multiple Access

This paper considers the wireless powered communication networks (WPCNs) where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested radio-frequency energy only, and aims to maximize the common throughput (CT) of nodes. Specifically, we investigate the WPCN using non-orthogonal multiple access, where all nodes transmit information simultaneously to H-sink and H-sink performs successive interference cancellation to alleviate the noise from other nodes. Energy transfer duration, data transmit powers of nodes and the decoding order of nodes’ signals are jointly optimized to maximize the CT. We first formulate the CT maximization (CTM) as a non-convex optimization problem, then separate the CTM problem into a number of subproblems to solve, and finally recover the optimal solution of the original problem from the optimal solutions of subproblems. Our CTM scheme significantly outperforms the available scheme using up all harvested energy. Furthermore, we propose an efficient algorithm to maximize the sum throughput while guaranteeing that the maximum CT is achieved. This scheme further greatly increases the sum throughput.

Two-Way Dual-Hop WPCN With A Practical Energy Harvesting Model

In this paper, we study a dual-hop wireless powered communication network (DH-WPCN), where the two-way communication between the users and the access point (AP) is assisted by a relay. Assuming a piece-wise linear energy harvesting (EH) model for the users and the relay, we investigate the problem of total throughput maximization for both amplify-and-forward (AF) and decode-and-forward (DF) relaying modes. To deal with the non-convexity of the throughput maximization problem, we apply the block coordinate descent (BCD) technique and alternately optimize different blocks of optimization variables until convergence is achieved. Numerical results demonstrate the substantial performance gain of the proposed scheme with piece-wise linear EH model as compared to the case with oversimplified linear EH model. This is due to the fact that our proposed scheme takes into consideration the saturation effect of practical EH circuits in optimizing the resource allocation.

Neural optimal self-constrained computing in smart grid considering fine-tuning island constraints with visual information

With the expansion of the power grid and the acceleration of power information construction, the information communication network covers all aspects and collects accurate information in time to provide continuous and reliable operation for users. The modern power system will gradually enter the era of interconnected power grids. It is more environmentally friendly and efficient than traditional power systems, management is more information and lean, and its operation is safer and more stable. As the infrastructure for carrying smart grid and future energy information interaction, the power communication network has higher and higher requirements for reliability. The coupling between the communication network and the power grid is more and more closely related. The real-time acquisition and the reliable transmission of control information such as the power system require the support of the power communication network. This paper uses machine learning algorithms to learn effective features or patterns from these data and apply them to new data. In this paper, we present a neural optimal self-constrained computing model based on fine-tuning island constraints with visual information. The experimental results show that the proposed algorithm has higher processing efficiency and accuracy.

Throughput Maximization in Backbone-Assisted Wireless Powered Communication Networks With Successive Interference Cancellation

In this mymargin letter, we investigate a backbone-assisted wireless powered communication network (WPCN) under the protocol of “harvest-then-transmit” protocol, where user nodes (UNs) harvest energy from the sinks in the downlink (DL), then they use the energy to transmit signals to the sinks by a non-orthogonal multiple access in the uplink (UL). We consider joint successive interference cancellation and backbone-assisted sink cooperation to reduce the impact of co-channel interference on the throughput performance. We maximize the system throughput by jointly optimizing user scheduling and the time allocation for DL energy harvesting and UL transmission. For solving the maximum throughput problem efficiently, we propose a column generation method based on a greedy algorithm. Numerical results show that our proposed energy and information transfer design is superior to other reference methods in WPCNs.

An Optimization Scheme with Weighted Sum-Rate Maximization for Multi-User Wireless Powered Communication Networks

An Optimization Scheme with Weighted Sum-Rate Maximization for Multi-User Wireless Powered Communication Networks

Clustered-NOMA Based Resource Allocation in Wireless Powered Communication Networks

Wireless Powered Communication (WPC) provides an effective solution to the contradiction between information transmission and energy consumption for Internet of Things (IoT) terminals. In this paper, a clustered-Non-Orthogonal Multiple Access (C-NOMA) based WPC model is proposed. Base Station (BS) transfers Radio Frequency (RF) energy to all terminals in the downlink, while terminals transmit information to BS in the uplink by utilizing the harvested energy. A terminal clustering scheme based on maximizing the sum of channel condition difference between terminals is adopted. Specifically, Orthogonal Frequency Division Multiple Access (OFDMA) is used for inter-cluster terminals, while NOMA is used for intra-cluster terminals. In addition, a time-sharing mechanism is adopted to decode information. The sum rate in the uplink is maximized by jointly optimizing transmit power of BS, uplink and downlink subslot. Simulation results demonstrate that, the proposed C-NOMA based WPC model can not only maximize the sum rate in the uplink, but also enhance the fairness between terminals.

Energy efficiency optimization in MIMO heterogeneous wireless powered communication networks

This paper studies the energy efficiency (EE) optimization problem in multiple-user multiple-input–multiple-output heterogeneous wireless powered communication network (HWPCN) in which the users in small cells can harvest energy from the small cell base stations in downlink and use harvested energy for uplink transmission. We consider the joint design problem of wireless energy transmission and wireless information transmission precoding matrices subject to the transmit power constraints at each BS and each user. Since the optimization problem under consideration is highly nonlinear non-convex fractional programming in design variables, it is mathematically challenging to obtain the optimal solutions. To tackle with this difficulty, we employ the difference of convex programming to obtain the concave lower bound of the achievable sum rates. Then, we apply the Dinkelbach approach to develop an efficient iterative algorithm in which the convex optimization problems are solved. Simulation results are provided to investigate the EE of the proposed algorithm in HWPCNs.

Slotted ALOHA wireless networks with RF energy harvesting in Nakagami-m fading

In this paper, we propose two resource allocation schemes for slotted ALOHA wireless powered communication networks (WPCNs) with channel aware energy harvesting users (EHUs) in Nakagami-m fading channels. Both schemes guarantee proportionally fair (PF) resource sharing among the EHUs. The resource allocation schemes determine the duration of the energy harvesting phase, the access probability, the transmit power of the base station, and the power allocation policy at the EHUs. The EHUs in both schemes transmit only when the channel fading conditions are favorable. When the first scheme is employed, the EHUs use truncated channel inversion power allocation at fixed rate transmission. In the second scheme, the EHUs use optimal power allocation policy with rates adjusted according to the channel fading states. Using numerical analysis we compare the performance of the proposed schemes in terms of network sum throughput and fairness to resource allocation schemes that are channel aware but don’t optimize the access probability and to resource allocation scheme where the EHUs exploit only statistical channel state information.