Numerous efforts in wireless power transfer have concentrated on simultaneous wireless information and power transfer (SWIPT), enabling terminal devices (TDs) to achieve permanent operation and reducing the limitation of energy on information transmission. Nevertheless, most existing studies focus on the amount of energy transferred, the estimation of the harvest states of TDs, and the optimization of energy efficiency for a system, failing to investigate the timeliness of energy replenishment for TDs through wireless energy harvesting. Additionally, due to channel fading and the influence of nonlinear circuits, serious estimation errors will occur during wireless energy transmission and state of charge (SoC) evaluation, resulting in the failure of system strategies and degraded system performance. To address this issue, a novel wireless energy access quality of service (QoS) index, age of energy harvesting (AoEH), is proposed to measure the timeliness of wireless energy transfer for TDs. Based on AoEH, this paper establishes a SWIPT access channel model and revises the time sequence of access channels. Furthermore, an energy management strategy is proposed for the permanent operation of TDs, and relevant theoretical analysis on the boundlessness of remaining energy is provided. As a typical case, this paper formulates the age of information (AoI) minimization problem for SWIPT-enabled sensor networks to find the optimal energy packet generation interval at the sink node based on AoEH expressions. Corresponding solutions are proposed for the AoI minimization problem with and without maximum transmission frequency constraints. Simulation results indicate that TD’s energy is bounded and stable under the proposed energy management strategy, and the AoI optimized by the optimal energy packet generation interval algorithm is up to 30% lower compared to existing algorithms.

Intrusion detection system plays an important role in network security, however, the problem with data imbalance limits the detection ability of intrusion detection system. In order to improve the performance of intrusion detection system, this paper proposes to use the adaptive synthetic sampling technique (ADASYN) and random under sampling technique to alleviate the problem of data imbalance in intrusion detection. Firstly, the majority class samples in the dataset are removed by undersampling technology and the minority class samples are oversampled, so the samples can reach a balanced state. Subsequently, a sparse autoencoder (SAE) extracts features from the resampled data to fit the original sample as closely as possible. Finally, LightGBM is applied on the processed dataset for the classification process. Multi-classification experiments were conducted on KDD99 and UNSWNB15 datasets. We compare six models' performance and find LightGBM is superior to other models. Furthermore, we also compare existing methods and the results show that our proposed method outperforms current methods.

With the explosive increase of the types and quantity of devices in wireless networks, it is necessary to design a fast and energy-efficient decision-making strategy for the resource allocation to maintain the efficient operation of the wireless system. However, traditional resource allocation strategies designed based on optimization methods are of high complexity and poor real-time performance, which are not conducive to online decision-making. In this paper, deep learning approaches are introduced to design the optimal beamforming for Sink nodes in the SWIPT-enabled Sensor-Cloud system. First, the energy-efficiency maximization problem is first formulated with a “return/pay” form. In order to realize the application of deep learning for problem solving, a high-dimensional solvable mathematical expression is transformed from the maximization problem, and then a SWIFT-WMMSE algorithm iteratively is designed to obtain the optimal beamforming vector. At the same time, the convergence of the SWIPT-WMMSE algorithm is proved. Second, the approachability of the neural network to approximate the SWIPT-WMMSE algorithm is discussed. Furtherly, based on the error propagation in DNN approaching process, the criteria for the DNN scale design is deduced, and the approximation to the SWIPT-WMMSE algorithm is realized through the training of DNN, which is operated as an alternative algorithm to solve the optimal beamforming vectors for Sink nodes. Finally, the simulation results verify the effectiveness of SWIPT-WMMSE algorithm and DNN model, as well as the approximation effect of DNN model and its advantages in improving the system performance, especially in the aspect of complexity reducing and time saving.

Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained systems. The system gains are usually optimized by designing resource allocation algorithm appropriately. However, few works focus on the interaction that channel's time-vary characters make the energy transfer inefficiently. To address this, we propose a novel system operation sequence for sensor-cloud system where the Sinks provide SWIPT for sensor nodes opportunistically during downlink phase and collect the data transmitted from sensor nodes in uplink phase. Then, the energy-efficiency maximization problem of the Sinks is presented by considering the time costs and energy consumption of channel detection. It is proved that the formulated problem is an optimal stopping process with optimal stopping rules. An optimal energy-efficiency (OEE) algorithm is designed to obtain the optimal stopping rules for SWIPT. Finally, the simulations are performed based on the OEE algorithm compared with the other two strategies to verify the effectiveness and gains in improving the system efficiency.

This paper studies the secure beamforming design for simultaneous wireless information and power transfer (SWIPT) in a amplify-and-forward (AF) relay networks with multiple users and eavesdroppers. Each user node adopts the power splitting (PS) scheme to decode information and harvest energy simultaneously. Our objective is to minimize the total relay transmit power under the constraints of secure transmission rate and energy harvesting requirements by jointly optimizing the beamforming matrix, artificial noise covariance matrix and PS ratios. For this non-convex problem, we propose a two-stage optimization approach to convert the original problem into two sub-problems. The first subproblem uses semidefinite relaxation (SDR) technique to transform the original problem into a convex problem, and then finds the optimal solution of the second subproblem through k-dimensional exhaustive search algorithm. Furthermore, we also propose a low complexity suboptimal solution scheme based on particle swarm optimization (PSO) algorithm. Simulation results show that the proposed scheme saves transmitting power and achieves better performance than other schemes.

应用无线携能通信(simultaneous wireless information and power transfer,SWIPT)技术实现基于无线射频信号的信息与能量传输,提出在基于无线射频网络中采用SWIPT技术的具有自能量回收的非分时全双工中继系统.该系统利用网络中多个可供电无线设备作为能量接入点(energy access point,EAP),能量受限的中继采用功率分配方案,实现信息传输、能量捕获和协作传输在1个时间块中同步进行.以最大化系统吞吐量作为优化目标,采用二次优化、半定松弛和变量消减等方法将原多变量非凸问题转换为半定规划问题,运用拉格朗日方法进行问题求解,通过联合优化中继发射功率、中继发射波束成形向量和功率分配比率,提高系统的性能增益.实验结果表明了所提出系统的吞吐量在解码转发(decode-and-forward,DF)协议下优于在放大转发(amplify-and-forward,AF)协议下;在中继从源节点捕获的能量有限时,通过增加EAP的数量来提高系统捕获的能量能有效提高系统运行速率;验证了与HD-SWIPT (half-duplex with SWIPT)和FD-no-SWIPT (full-duplex without SWIPT)中继系统相比,所提出的系统在提高系统性能方面具有更好的增益.

针对无线供电通信网络的下行阶段缺乏信息传输功能等问题,提出一种基于无线携能通信技术的具有下行链路信息传输能力的网络模型.该模型将一个传输单位分为上、下行两个阶段,在上行阶段,用户发送射频信号进行信息传输,各小区基站完成信息解码任务;在下行阶段,基站通过波束成形技术和多载波技术向用户发送射频信号,各用户通过时分切换或功率分流方式完成信息解码与能量捕获.基于该模型,提出一种最大化最小速率的方法,通过最大化最小用户上行信息传输速率求解各小区内各用户的最优上行发送功率,通过最大化最小用户下行信息传输速率求解各个小区内的基站的最优下行波束成形矢量,实现网络中各用户相对公平的性能最优.仿真结果表明,本文方法提高了用户的最小传输速率.

The wireless powered communication networks (WPCN) is a new networking paradigm which only considers energy transmission in the downlink, no the need for information transmission. In many applications of WPCN, it is necessary to consider transmit information in the downlink. How to develop a transmission strategy to weigh the fairness and maximization of each user’s uplink throughput is a research hotspot in WPCN. This paper proposed a new design scheme for joint transmission of energy and information in a strongly interfered multi-cell network. It combined simultaneous transmission of downlink wireless information and energy with WPCN to realize downlink energy transmission and two-way information transmission between base station and users. In this scheme, uplink power allocation, downlink time allocation and beamforming were used to maximize the minimum transmission rates of both uplink and downlink, so that made a tradeoff between performance and fairness of uplink and downlink information transmission for each user. Simulation results show that compared with the traditional transmission method, the proposed scheme significantly improves the minimum transmission rate of the users.

基于能量收集和双向能量协作的菱形信道,提出一种实现系统端到端的吞吐量最大化的功率分配和能量转移策略.该策略将能量收集的高斯菱形信道模型扩展为双向能量协作的菱形通信网络模型,在满足传输节点间能量和数据因果关系约束条件下,构建了系统吞吐量优化模型.通过延迟策略将问题分解为功率分配问题和各时隙的能量转移问题,利用定向注水算法求解实际消耗功率分配,原问题的最优解最终通过求解所分离的两个问题获得.仿真结果表明,与基于无能量协作和基于单向能量协作的功率分配策略相比,本文的功率分配策略显著提高了系统的吞吐量.

能量收集网络能够延长能量受限型网络的生命周期,然而目前的研究大多针对单一天线的网络系统,同时线性的能量收集模型无法准确刻画系统能量收集过程.为此,针对能量收集多用户多输入多输出认知无线供电通讯网络系统,基于非线性能量收集模型分别建立overlay和underlay场景下的系统吞吐量优化模型;由于系统模型的参数耦合和非线性约束特性导致问题为联合凹的,利用等效代换将系统模型转化为等效凸问题,使用拉格朗日对偶方法求解;继而分别给出能量与信息协方差矩阵最优解形式及最优时间分配系数的求解方法,并使用数学方法证明其最优性.最后,基于实际的风电能量收集数据,验证算法的有效性及性能.仿真结果表明,与平均功率分配算法相比,最优求解算法能够实现更好的平均系统吞吐量,同时所述算法均在有限次迭代后达到稳定收敛.

Simultaneous wireless information and power transfer (SWIPT) becomes more and more popular in cognitive radio (CR) networks, as it can increase the resource reuse rate of the system and extend the user’s lifetime. Due to the deployment of energy harvesting nodes, traditional secure beamforming designs are not suitable for SWIPT-enabled CR networks as the power control and energy allocation should be considered. To address this problem, a dedicated green edge power grid is built to realize energy sharing between the primary base stations (PBSs) and cognitive base stations (CBSs) in SWIPT-enabled mobile edge computing (MEC) systems with CR. The energy and computing resource optimal allocation problem is formulated under the constraints of security, energy harvesting, power transfer, and tolerable interference. As the problem is nonconvex with probabilistic constraints, approximations based on generalized Bernstein-type inequalities are adopted to transform the problem into solvable forms. Then, a robust and secure artificial noise- (AN-) aided beamforming algorithm is presented to minimize the total transmit power of the CBS. Simulation results demonstrate that the algorithm achieves a close-to-optimal performance. In addition, the robust and secure AN-aided CR based on SWIPT with green energy sharing is shown to require a lower transmit power compared with traditional systems.

针对节点具有能量收集能力的全双工中继窃听信道保密速率的优化问题,提出了一种基于信息和能量联合传输的人工噪声辅助的安全波束成形方法.该方法在满足节点传输功率和中继节点收集能量等约束条件下,通过联合优化波束成形矩阵、人工噪声协方差矩阵和功率分配因子等参数实现系统安全速率最大化(Secrecy Rate Maximization,SRM).由于SRM问题是一个非凸的优化问题,为了有效解决该问题,文中采用分步优化方法将原始问题转化成两个子问题.首先,设计一种双层优化算法来优化波束成形矩阵和人工噪声协方差矩阵,其中,外层优化问题使用一维搜索方法解决,内层优化问题使用半定松弛技术解决;然后,固定波束成形矩阵和人工噪声协方差矩阵,再次使用一维搜索方法求解功率分配因子.理论推导证明了内层优化问题总是存在秩为1的最优解,即使用的松弛技术是紧的.仿真结果表明,所提方法能将系统安全性能提升2～3倍.

Energy harvesting network is a promising new solution to provide energy harvesting and energy sharing capability to traditional network. In practice, the existence of malicious nodes leads to inefficiency of EH networks. In this paper, we studies the robust security beamforming design for energy harvesting cognitive radio network with SWIPT. In particular, the probabilistic CSI error model is utilized to capture the channel uncertainty. The considered robust security beamforming design is formulated as a non-convex optimization problem constrained by security, energy harvesting, energy transfer and tolerable interference. We are aiming at minimizing the total transmit power of cognitive base station. As the intractability of the problem, a one-dimension search algorithm is proposed based on Bernstein-type inequalities to obtain the optimal solution. In addition, the simulations are submitted to illustrate the system performance.

To solve the end-to-end throughput maximization problem of dual-relay channels based on harvested energy and bi-directional energy cooperation, a power distribution strategy for bi-directional energy cooperation diamond channel with nodes of harvested energy is proposed. The strategy extends Gaussian diamond channel model of energy harvesting to the diamond communication network model of bi-directional energy cooperation, and applies the delay policies to decompose the problem into the energy distribution problem and energy transmission problem of each time slot. The two-way water injection algorithm to solve the practical energy consumption distribution, and then the optimization schem of the original problem is obtained by solving solutions of the two separated problems. Simulation results proved that proposed power distribution strategy has obviously improved the system throughput with the power split strategy based on uni-directional energy collaboration and bi-directional energy collaboration when the energy collection of source and relay nodes are very different.

能量收集网络(Energy harvesting network,EHN)融合了新能源的优势,为能量受限型网络提供了解决方案,同时实现传统网络的节能减排,为网络的自治与永久运行提供发展前景.针对能量收集网络这一新的研究领域,本文分别从信息论视角、能量收集模型、离线与在线能量规划、无线能量与信息联合传输4个方面综述近年的研究现状与相关结论,旨在为能量收集网络的研究发展提供参考.

无线信息与能量同步传输技术为通信系统中能量受限型节点提供了解决方案,可延长节点生命周期.为研究时间分配对系统中断性能的影响,针对采用解码转发策略的SWIPT协作中继系统,研究构建系统信道容量模型,在满足系统最低传输速率的约束下,提出一种中继动态时间分配策略来优化传输.该策略首先对系统目的端节点的中断概率进行分析,推导得出系统中断概率与时间分配系数之间关系的表达式,然后以最小化中断概率为目的构建优化求解模型,利用迭代算法得到问题的解.仿真结果表明,与现有的传输策略相比,动态时间分配策略可以显著减少系统中断概率,能较好地改善系统传输性能,保障系统的可靠运行.

In this paper, we study secure beamforming for simultaneous wireless information and power transfer (SWIPT) in wireless-powered full-duplex (FD) relay networks. The relay assists transmission of confidential information, while simultaneously harvesting the energy with power switching scheme by the radio-frequency (RF) signals. We propose a joint amplify and -forward beamforming (AB) and energy signal (AB-ES) with the receiver power splitting strategy (PS) scheme to maximize the secrecy rate under harvesting energy constraints. Since these constraints make the optimization problems become non-convex and hard to tackle. We propose a two-level optimization problem to solve this problem. The out part can be solved via the one-Dimensional search, and the inner part can be solved by semidefinite relaxation (SDR) technique. Simulation results show that the proposed scheme outperforms other schemes.

为了优化能量收集无线网络传输性能,延长网络中能量受限型节点的生命周期,提出了一种基于解码转发策略的机会协作中继系统动态时间分配策略.在满足系统最低传输速率的约束下,推导出中断概率与时间分配系数之间的关系表达式;通过减小系统中断概率来优化时间分配系数,得出系统吞吐量和能量效率的表达式;设计了一种基于次优迭代的时间分配算法,实现问题的求解.仿真实验结果表明,在非理想信道状况下,所提出的策略有较高的系统吞吐量和能量效率,可优化无线网络的传输性能,提高可靠运行度.

In this paper, we develop optimal energy scheduling algorithms for optical Poisson channels with energy harvesting devices. The objective is to maximize the channel sum-rate, assuming that the side information of energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O(K) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver is exponential in the number of constraints. This paper gives an efficient energy scheduling algorithm that has a computational complexity of O(K-2). The proposed algorithm is also shown to be optimal. The proposed energy schedule is piece-wise constant, which changes when the battery overflows or depletes. Numerical results depict significant improvement of the optimal strategy over benchmark strategies.

能量收集网络(Energy Harvesting Networks)是一种新型的计算机网络形式,它通过搜寻各类环境能源,将其转化成可用的电能,然后将这些电能作为主要或辅助的电源方式供给电子设备进行网络通讯.但是,现有的能量收集网络大多采用解析概率分布函数刻画能量获取过程,无法准确模拟实际情况,缺乏真实性.为此,提出一种基于场景生成的能量收集网络模拟技术.首先,该方法基于历史能量获取数据,无需预设概率分布函数,使用最优消减技术生成单时段代表场景;然后,利用时齐模拟退火算法生成日场景序列,以便能够准确模拟能量收集网络中能量获取的随机特性.以实际的风电数据为例,通过与真实数据的对比,验证该方法的准确性和稳定性;然后以网络吞吐量的优化为例,验证了该方法在能量收集网络系统规划运行中的可行性和有效性.