Jamming-Aided Secure Communication in Ultra-Dense LEO Integrated Satellite-Terrestrial Networks

The ultra-dense low earth orbit(LEO)integrated satellite-terrestrial networks(UDLEO-ISTN)can bring lots of benefits in terms of wide coverage,high capacity,and strong robustness.Meanwhile,the broadcasting and open natures of satellite links also reveal many challenges for transmission security protection,especially for eavesdropping defence.How to efficiently take advantage of the LEO satellite’s density and ensure the secure communication by leveraging physical layer security with the cooperation of jammers deserves further investigation.To our knowledge,using satellites as jammers in UDLEO-ISTN is still a new problem since existing works mainly focused on this issue only from the aspect of terrestrial networks.To this end,we study in this paper the cooperative secrecy communication problem in UDLEOISTN by utilizing several satellites to send jamming signal to the eavesdroppers.An iterative scheme is proposed as our solution to maximize the system secrecy energy efficiency(SEE)via jointly optimizing transmit power allocation and user association.Extensive experiment results verify that our designed optimization scheme can significantly enhance the system SEE and achieve the optimal power allocation and user association strategies.

Optimal Energy-Efficient Transmission for Hybrid Spectrum Sharing in Cooperative Cognitive Radio Networks

In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

Cooperative Jamming for Physical Layer Security in Hybrid Satellite Terrestrial Relay Networks

To integrate the satellite communications with the LTE/5G services, the concept of Hybrid Satellite Terrestrial Relay Networks(HSTRNs) has been proposed. In this paper, we investigate the secure transmission in a HSTRN where the eavesdropper can wiretap the transmitted messages from both the satellite and the intermediate relays. To effectively protect the message from wiretapping in these two phases, we consider cooperative jamming by the relays, where the jamming signals are optimized to maximize the secrecy rate under the total power constraint of relays. In the first phase, the Maximal Ratio Transmission(MRT) scheme is used to maximize the secrecy rate, while in the second phase, by interpolating between the sub-optimal MRT scheme and the null-space projection scheme, the optimal scheme can be obtained via an efficient one-dimensional searching method. Simulation results show that when the number of cooperative relays is small, the performance of the optimal scheme significantly outperforms that of MRT and null-space projection scheme. When the number of relays increases, the performance of the null-space projection approaches that of the optimal one.

Deep Unfolding for Cooperative Rate Splitting Multiple Access in Hybrid Satellite Terrestrial Networks

Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.

A method for power suppliers’optimal cooperative bidding strategies considering network losses

The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.

Statistical QoS Provisioning Architecture for 6G Satellite-Terrestrial Integrated Networks

The emergence of massive ultra-reliable and low latency communications (mURLLC) as a category of age/time/reliability-sensitive service over 6G wireless networks has received considerable research attention, which has presented unprecedented challenges. As one of the key enablers for 6G,satellite-terrestrial integrated networks (STIN) have been developed to offer more expansive connectivity and comprehensive 3D coverage in space-aerial-terrestrial domains for supporting 6G mission-critical mURLLC applications while fulfilling diverse and rigorous quality of service (QoS) requirements. In the context of these mURLLC-driven satellite services, data freshness assumes paramount importance, as outdated data may engender unpredictable or catastrophic outcomes.To effectively measure data freshness in satellite-terrestrial integrated communications,age of information(AoI)has recently surfaced as a new dimension of QoS metric to support time-sensitive applications. It is crucial to design new analytical models that ensure stringent and diverse QoS metrics bounded by different key parameters,including AoI,delay,and reliability,over 6G satellite-terrestrial integrated networks. However,due to the complicated and dynamic nature of satellite-terrestrial integrated network environments, the research on efficiently defining new statistical QoS provisioning schemes while taking into account varying degrees of freedom has still been in their infancy. To remedy these deficiencies, in this paper we develop statistical QoS provisioning schemes over 6G satellite-terrestrial integrated networks in the finite blocklength regime. Particularly, we firstly introduce and review key technologies for supporting mURLLC.Secondly,we formulate a number of novel fundamental statistical-QoS metrics in the finite blocklength regime.Finally,we conduct a set of simulations to validate and evaluate our developed statistical QoS provisioning schemes over satellite-terrestrial integrated networks.

Coverage in Cooperative LEO Satellite Networks

Low-earth orbit(LEO)satellite networks ignite global wireless connectivity.However,signal outages and co-channel interference limit the coverage in traditional LEO satellite networks where a user is served by a single satellite.This paper explores the possibility of satellite cooperation in the downlink transmissions.Using tools from stochastic geometry,we model and analyze the downlink coverage of a typical user with satellite cooperation under Nakagami fading channels.Moreover,we derive the joint distance distribution of cooperative LEO satellites to the typical user.Our model incorporates fading channels,cooperation among several satellites,satellites'density and altitude,and co-channel interference.Extensive Monte Carlo simulations are performed to validate analytical results.Simulation and numerical results suggest that coverage with LEO satellites cooperation considerably exceeds coverage without cooperation.Moreover,there are optimal satellite density and satellite altitude that maximize the coverage probability,which gives valuable network design insights.

信息年龄约束下的无人机数据采集能耗优化路径规划算法

信息年龄(AoI)是评价无线传感器网络(WSN)数据时效性的重要指标,无人机辅助WSN数据采集过程中采用优化飞行轨迹、提升速度等运动策略保障卸载至基站的数据满足各节点AoI限制。然而,不合理的运动策略易导致无人机因飞行距离过长、速度过快产生非必要能耗,造成数据采集任务失败。针对该问题,该文首先提出信息年龄约束的无人机数据采集能耗优化路径规划问题并进行数学建模;其次,设计一种协同混合近端策略优化(CH-PPO)强化学习算法,同时规划无人机对传感器节点或基站的访问次序、悬停位置和飞行速度,在满足各传感器节点信息年龄约束的同时,最大限度地减少无人机能量消耗。再次,设计一种融合离散和连续策略的损失函数,增强CH-PPO算法动作的合理性,提升其训练效果。仿真实验结果显示,CH-PPO算法在无人机能量消耗以及影响该指标因素的比较中均优于对比的3种强化学习算法,并具有良好的收敛性、稳定性和鲁棒性。

Efficient Approach for Resource Allocation in WPCN Using Hybrid Optimization

The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.

基于网络编码的协作HARQ协议

该文基于网络编码技术提出了一种新的协作HARQ协议。在中继节点的协助下两个用户与基站进行通信。中继节点响应基站的重传请求,对两用户的增加冗余帧采用物理层网络编码即信号重叠调制技术进行处理,然后发送至基站。基站对接收信号采用多用户联合检测以及联合软判决译码。在平坦瑞利衰落信道下,该网络编码协作HARQ协议较传统的非协作HARQ协议可获得较大的性能改善,其性能接近没有采用网络编码的协作HARQ协议,且需要较少的硬件和带宽资源。

考虑多源-荷-储协同优化的能源局域网系统能量管理研究

提出了一种兼顾源、荷、储及网内、网外全时段协同优化的建模与优化方法。考虑能源局域网网内、网外各可调度资源的实际运况及彼此之间的交互关系,根据可再生能源利用最大化和系统运行费用最小化原则构建了适用于分时电价和实时电价的网内/网外一体化能量管理与优化模型:在网内,引入需求响应机制和储能单元进行经济调度,以提高能源局域网网内各可调度资源间的自协调、自平衡能力;在网外,考虑网内自协调后仍存在供需失衡情况,对网外各可调度资源设置差异性交易电价和不同的使用权限并据此进行电量交易。最后,提出了3种不同的优化方案,并综合采用拉格朗日乘子法和协同演化算法对不同算例进行求解、分析,验证了本文所提方法的信度和效度。

多智能体系统的交直流混合微网群一致性协同控制

针对交直流混合微网群组成结构的复杂性、源-网-荷高度电力电子化趋势等新特点,提出一种基于离散一致性算法的智能多级控制策略,对交、直流微网群的组网形式及群级协调控制进行了详细研究。在用户需求发生变化时,基于多智能体系统构建稀疏通信网络,对微电网群内各分布式设备实现就地分布式控制,邻近智能体间进行通信,节省通信时间;分析了通信延时对一致性算法收敛稳定性的影响。仿真结果表明,当构建的随机矩阵D的对角元素不为0时,通信延时不会对一致性算法的收敛性产生影响,但会影响收敛的形式及时间;另外,多智能体系统可以实现＂即插即用＂,维持交直流混合微网的稳定性。

MIMO时分蜂窝系统混合协作上行性能研究

下一代无线通信系统中,基站联合解码和移动终端协作传输是两种提高系统性能的重要技术。该文在线性时分蜂窝系统中,通过研究MIMO信道条件下混合协作可获得的单个小区上行可达传输速率,得出两种协作方式的相互作用和系统传输最小Eb/N0,并给出了小区联合解码与终端DF(解码前向)协作混合模式下的可达速率计算方法和最小Eb/N0的闭式解。最后,文中给出了MIMO衰减信道条件下的仿真结果,得出了多小区联合解码(宏分集)与终端协作性能的线性叠加关系,强临近小区干扰将会严重削弱用户与小区联合协作时终端协作作用的结论,验证了混合协作模式会极大提高系统传输性能。

多群落双向驱动协作搜索算法

针对复杂优化问题中数据混杂多变的特点,提出一种能够根据环境变化不断优化种群适应度的多群落双向驱动协作搜索算法。该算法在分析微粒群落特性的基础上,基于无向加权图建立了多群落协作网演化模型,该模型依据群落适应值的优劣程度对群落类型进行划分,并根据不同群落间的协作权重和群落节点响应度评估群落节点强度,由节点强度最大的群落引导整个协作网进化,改进传统群集智能算法面对复杂优化问题中环境变化的自适应性能缺陷;构建了一种多群落双向驱动的进化新模式,给出了多群落协作的异步并行搜索算法,实现了不同环境下群落内部与群落之间的并行进化,降低了数据分析中巨大的计算时空开销。实验结果表明,该方法面向混杂多变数据不断优化种群适应度,能够较快地适应环境变化,并在可接受的时间内得到精确解,为复杂优化问题的求解提供了有效手段。

虚拟检测维修系统的设计与实现

结合实际装备维修工作的需求,开发了一套基于虚拟现实技术的具有一定通用性的虚拟维修训练系统。系统由界面层、应用层、对象层和技术支持层组成,运用OSG渲染创建逼真的场景,基于均匀网格和OBB树的混合碰撞检测算法解决碰撞问题,通过协同网络通信实现多人协作维修/检测操作。通过对某型装备手控台的虚拟维修训练系统的构建,证明系统能够快速开发相应的虚拟训练系统。训练过程中操作与场景变化实时同步,结果表明该系统能够有效提高操作人员的维修维护能力。

无线传感器网络自适应多中继协作差错控制策略

提出一种基于能效和通信距离的适用于无线传感器网络的多中继自适应协作差错控制策略,在提供高可靠性数据传输同时保持高吞吐率和高能效.根据传统ARQ、Chase合并的混合ARQ的能效规律和数据包在链路层分割为若干个数据帧的规律建立了一种马尔科夫链模型,并给出了饱和吞吐率、数据包丢弃概率、时延和能效分析方法,在此基础上提出了自适应多中继协作差错控制策略.针对线性移动无线传感器网络模型,对传统ARQ、Chase合并的混合ARQ和自适应多中继协作差错控制的通信性能以及移动性的影响进行了分析.结果表明所提策略在饱和吞吐率、可靠性和能效方面,表现出了比传统ARQ和Chase合并的混合ARQ更优越的性能.

判决反馈RBF信道均衡器的微粒群算法进化设计

本文在分析判决反馈RBF网络信道均衡器(DFRBFNE)的结构特点基础上,定义一个布尔向量L作为网络的结构参数,与原来DFRBFNE的隐节点参数集一起构成了新的DFRBFNE隐节点参数集{c,σ,L},并给出了一个新的DFRBFNE网络输入输出关系表达式;采用一种混合协同微粒群算法同时对DFRBFNE网络拓扑结构和隐层节点参数进行优化设计,并将输出线性参数集分离后采用最小二乘法进行优化设计,简化了优化空间,加速了算法的收敛速度。

RBF神经网络的混合微粒群学习算法

在分析RBF神经网络的结构特点基础上,定义一个布尔向量L作为网络的结构参数,与原来RBF神经网络的隐节点参数集一起构成了新的RBF网络隐节点参数集{c,,σL},并给出了一个新的RBF网络输入输出关系表达式;采用一种混合协同微粒群算法同时对RBF网络拓扑结构和隐层节点参数进行优化设计,并将输出线性参数集分离后采用最小二乘法进行优化设计,简化了优化空间,加速了算法的收敛速度。

无线协同通信协议与应用前景综述

无线协同通信具有自组织组网、快速部署、无需基础设施支持、高可靠、高抗毁、网络自恢复、动态灵活等特点,在未来具有广阔的应用前景。按照控制方式和应用模式的不同,无线协同通信组网方式可以分为集中式、分布式和混合式。对以上3种组网方式的特点进行了分析和比较,概述了现有无线通信系统中集中式、分布式单跳及多跳组网协议,最后对无线协同通信在军事与应急通信、5G超密集组网、物联网、星载组网以及轨道交通领域的应用前景进行展望。

星-地混合通信网络前向链路安全传输方案研究

针对星-地混合通信网络前向链路在部分非理想信道状态信息(CSI)及非理想CSI下卫星窃听链路的安全问题,提出一种将地面基站信号作为协同干扰的安全传输方案。优化目标为地面基站节点传输速率最大,确保卫星合法节点接收信号质量,同时利用地面基站发送信号阻塞卫星潜在窃听节点对卫星信号的侦听。通过半定松弛原理将其转化为可解的凸优化问题,分别设计分层迭代、高斯随机化和二分搜索算法对问题进行优化求解。仿真校验结果表明,所提方案能够降低网络对CSI误差的敏感性,提高网络的可靠性和保密性。