Secrecy sum-rate analysis of massive MIMO systems under dual-threat attacks using normalization methods

Massive Multiple Input Multiple Output(MIMO)has been considered as an emerging technology to enhance the spectral and energy efficiency for the upcoming wireless communication systems.This paper derives a closedform approximation for the Ergodic Achievable Secrecy Sum-Rate(EASSR)by considering the joint impact of eavesdroppers and jammers.Two widely used linear precoding techniques,Zero-Forcing(ZF)and Maximum Ratio Transmission(MRT),were used in conjunction with matrix and vector normalization to analyze the secrecy performance.Closed-form expressions are used to explain how the secrecy performance is affected when using the ZF and MRT precoding in the eavesdropping and jamming attack models.We also analyze and compare the performances of different combinations of normalization method and precoding technique in various scenarios.From the analytical expressions and simulation results,we observe that the vector and matrix normalization perform better for the ZF precoding than for the MRT precoding in high Signal-to-Noise Ratio(SNR)scenarios.However,in low SNR,the MRT with matrix normalization outperforms the ZF with vector normalization regardless of the number of users in the system.Further,we observe that the MRT fails to serve more than two users in high SNR scenario.Numerical results obtained from Monte Carlo simulation are used to corroborate the accuracy of the asymptotic secrecy analysis.

Sum-Rate Maximization in Active RIS-Assisted Multi-Antenna WPCN

In this paper,we propose an active reconfigurable intelligent surface(RIS)enabled hybrid relaying scheme for a multi-antenna wireless powered communication network(WPCN),where the active RIS is employed to assist both wireless energy transfer(WET)from the power station(PS)to energyconstrained users and wireless information transmission(WIT)from users to the receiving station(RS).For further performance enhancement,we propose to employ both transmit beamforming at the PS and receive beamforming at the RS.We formulate a sumrate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT,transmit and receive beamforming vectors,and network resource allocation.To solve this non-convex problem,we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion,semidefinite relaxation(SDR)and successive convex approximation techniques.Specifically,the tightness of applying the SDR is proved.Simulation results demonstrate that our proposed scheme with 10 reflecting elements(REs)and 4 antennas can achieve 17.78%and 415.48%performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs,respectively.

Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various benefits.However,the adoption of cloud storage poses significant risks to data secrecy and integrity.This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic primitives.The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data.To preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing it.An extensive performance analysis is conducted to illustrate the efficiency of the proposed mechanism.Additionally,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running costs.The security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious entities.The proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management.However,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of secrecy.Motivated by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the RL.By integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms.This can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the network.We compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network conditions.The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence trend.For our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation dataset.This was accompanied by the lowest RMSE(0.95),indicating very precise predictions with minimal error.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

Leveraging UAV-assisted communications to improve secrecy for URLLC in 6G systems

Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G networks.In this regard,UAVs have appeared as a winning candidate to mitigate security risks.In this paper,we leverage UAVs to propose two methods.The first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices.Moreover,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,i.e.increasing secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay.Additionally,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation distance.Lastly,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

Secrecy Efficiency Maximization in Intelligent Reflective Surfaces Assisted UAV Communications

This paper focuses on the secrecy efficiency maximization in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communication.With the popularization of UAV technology,more and more communication scenarios need UAV support.We consider using IRS to improve the secrecy efficiency.Specifically,IRS and UAV trajectories work together to counter potential eavesdroppers,while balancing the secrecy rate and energy consumption.The original problem is difficult to solve due to the coupling of optimization variables.We first introduce secrecy efficiency as an auxiliary variable and propose relaxation optimization problem,and then prove the equivalence between relaxation problem and the original problem.Then an iterative algorithm is proposed by applying the block coordinate descent(BCD)method and the inner approximationmethod.The simulation results show that the proposed algorithm converges fast and is superior to the existing schemes.In addition,in order to improve the robustness of the algorithm,we also pay attention to the case of obtaining imperfect channel state information(CSI).

Secrecy Outage Probability for Two-Way Integrated Satellite Unmanned Aerial Vehicle Relay Networks with Hardware Impairments

In this paper,we investigate the secrecy outage performance for the two-way integrated satellite unmanned aerial vehicle relay networks with hardware impairments.Particularly,the closed-form expression for the secrecy outage probability is obtained.Moreover,to get more information on the secrecy outage probability in a high signalto-noise regime,the asymptotic analysis along with the secrecy diversity order and secrecy coding gain for the secrecy outage probability are also further obtained,which presents a fast method to evaluate the impact of system parameters and hardware impairments on the considered network.Finally,Monte Carlo simulation results are provided to show the efficiency of the theoretical analysis.

非理想CSI下IRS辅助MISO保密速率最大化方法

基于智能反射面辅助多输入、单输出的安全无线通信系统,提出一种最大化系统保密速率为目标联合主被动波束形成算法.考虑非理想信道状态信息下基站发射波束成形向量和无源智能反射面相移矩阵联合优化设计问题.为解决非凸分式规划问题,通过Charnes-Cooper变换引入两个辅助变量,将单项分式问题转化为差的形式,同时采用一种交替迭代优化联合半正定松弛方法,得到易于求解的凸问题.仿真实验结果表明,该算法相比传统算法,有效提升了系统的安全性,保密性能提升10%~30%,且在一定信道状态信息误差下保密速率下降不明显,具有较强的鲁棒性.

基于DF-NOMA辅助的PLC系统安全性能分析

为了探求影响电力通信系统数据安全传输的关键因素,构建基于解码转发(decode-and-forward,DF)中继和非正交多址接入(non-orthogonal multiple access,NOMA)技术辅助的电力线通信(power line communication,PLC)系统,并研究其安全传输性能.针对外部窃听和内部窃听两种情况,联合考虑背景噪声和脉冲噪声的影响,分析系统的可达速率、遍历安全速率和安全中断概率等性能,并利用高斯-切比雪夫求积方法获得其相应的闭合表达式.结果表明:较高的脉冲噪声会降低系统的频谱效率和安全传输性能;功率分配系数以及源用户到中继用户的距离均对系统安全传输产生显著影响.进一步地,通过蒙特卡罗仿真实验验证了理论分析的正确性.

智能反射面辅助的多入单出共生无线电鲁棒安全资源分配算法

针对信道不确定性影响、无线信息泄露和障碍物阻挡导致通信质量下降等问题,该文提出一种基于智能反射面(RIS)辅助的多输入单输出(MISO)共生无线电(SR)鲁棒安全资源分配算法。考虑主用户的安全速率约束、次用户的最小速率约束、RIS最小能量收集约束,基于有界信道不确定性,建立了一个联合主被动波束赋形优化的资源分配问题。利用半正定松弛、S-procedure和变量替换法将含参数摄动的非凸问题转化为确定性的凸优化问题,并提出一种基于半正定松弛的鲁棒资源分配算法。仿真结果表明,与现有算法相比,该文算法具有较好的收敛性和鲁棒性。

基于D2D通信系统的无人机辅助物联网保密通信频谱共享方法

保密通信作为物联网安全的一项核心技术近些年受到了广泛关注。无人机(UAV)由于其高机动和灵活部署等特性,被认为是提高物联网通信安全的有力手段。针对当前物联网中UAV辅助保密通信频谱利用率问题,研究了UAV和地面设备到设备(Device-to-Device, D2D)通信系统频谱共享的系统保密容量最大化,提出了一种基于块坐标下降(Block Coordinate Descent, BCD)法的频谱共享算法,该算法通过联合优化系统中发送方(UAV和地面设备对)的传输功率和无人机轨迹,在确保地面设备对和UAV能够共存的情况下最大化接收方的保密容量。具体来说,该算法通过差分凸规划(Difference of Two Convex Functions, DC)方法优化固定轨迹下的系统功率,利用连续凸优化方法解决给定发射功率的UAV轨迹优化问题。仿真结果表明,所提方法在多种参数配置下都能有效提高系统的保密容量,并且和其他算法相比有更低的功率消耗。

硬件损伤下IRS辅助的SWIPT系统安全波束成形设计

为了提高无线携能通信(Simultaneous Wireless nformation and Power Transfer,SWIPT)通信系统的安全性,同时克服系统收发机硬件损伤(Hardware Impairments,HIs)的影响,提出一种硬件损伤下的智能反射面(Intelligent Reflecting Surface,IRS)辅助的SWIPT系统安全波束成形设计方法.考虑能量接收设备为潜在的窃听者,在基站最大发射功率、最小接收能量和IRS相移约束下,通过联合优化基站波束赋形矢量、人工噪声矢量和IRS的相移矩阵,构建系统安全速率最大化问题.针对该优化问题是非凸的,且优化变量是耦合的,提出一种基于交替优化和半正定松弛的有效算法来次优地解决该问题.仿真结果表明,本文所提算法能够在保障能量需求的同时,提升系统的安全性和抗硬件损伤能力.

智能反射面增强抗干扰与反窃听的安全传输策略

针对无线通信中存在窃听者和干扰者的潜在合作关系问题,本文提出了智能反射面增强抗干扰与反窃听的安全传输策略。该策略考虑信道状态信息完美和非完美两种情况,联合优化基站发射波束成形矢量和智能反射面相移矩阵来实现通信系统保密率最大化。在完美信道状态信息下,提出交替优化算法和罚函数法来求解系统保密率优化问题;在非完美信道状态信息下,利用松弛变量、S-引理、拉格朗日乘子法和交替优化等方法将系统保密率的非凸二次约束二次规划问题转化为确定性凸优化问题并求解。并进一步地分析了基站传输功率、IRS反射单元数量、信道不确定性等参数对系统保密率的影响。数值结果表明,本文所提策略经过3至6次的迭代均能达到收敛。并且与现有的最大比发射策略和随机相移策略相比,本文所提策略能有效提升系统保密率约20%。

基于发射天线选择的修改转发协作D2D网络的物理层安全

为了进一步提升与蜂窝网络共享频谱资源的解码转发(decode-and-forward,DF)协作终端直通(device-to-device,D2D)网络的物理层安全(physical layer security,PLS)性能,提出了一种D2D发送端采用发射天线选择(transmit antenna selection,TAS)的修改转发(modify-and-forward,MF)协作D2D-蜂窝网络PLS模型。配备多根发射天线的D2D发送端采用TAS技术,选择使D2D发送端至MF中继节点链路信干噪比最大的天线发送信号;中继采用MF协议将解码得到的合法信息进行修改后再转发,以避免被动窃听者能够解码出此阶段发送的合法信息,从而改善了D2D网络的PLS性能。推导了MF协作D2D网络的安全中断概率、非零安全容量概率和渐近安全中断概率的解析表达式,并与DF协作D2D网络的PLS性能进行对比分析。数值计算与仿真结果表明,MF协作D2D网络的PLS性能优于DF协作D2D网络,并且增大D2D发送端的发射天线数量,可以增强D2D网络的PLS性能。

硬件损伤下多窃听器NOMA系统平均保密容量分析

研究了非正交多址(Non‑orthogonal‑multiple‑access,NOMA)接入通信系统的物理层安全性能。当基站采用下行NOMA方案发送信息时,由于信道的开放性,信息容易被窃听,而当存在多个随机分布的窃听者时,安全性能会进一步降低。为了增强存在硬件损伤系统的物理层安全性能,本文考虑保护区的方法,针对地面用户与基站之间存在直连链路的情况,采用莱斯衰落来建模小尺度衰落。本文还假设多个窃听者的位置遵循齐次泊松点过程(Homogeneous Poisson point process,HPPP),借助高斯切比雪夫积分公式,推导了平均保密容量的闭式表达式,并给出了在高信噪比情况下的渐近表达式来获得进一步见解。仿真结果验证了保护区方法在增强安全性能方面的有效性,并说明了不同参数对系统保密性能的影响。

中继选择NOMA无线系统的物理层安全

针对解码转发协作非正交多址接入(non-orthogonal multiple access,NOMA)系统物理层安全(physical layer security,PLS)性能较差,以及在多窃听者场景下PLS性能恶化的问题,提出了一种采用修改转发(modify-and-forward,MF)协议的多中继选择NOMA无线系统PLS模型。利用高斯-切比雪夫正交定理推导了非合谋或合谋窃听场景下多中继选择MF-NOMA无线系统的安全中断概率和渐近安全中断概率的近似表达式。仿真结果验证了多中继选择MF-NOMA无线系统PLS性能分析的准确性。结果同时也表明中继数目越多或窃听者数目越少,多中继选择MF-NOMA无线系统的PLS性能越好;合谋窃听比非合谋窃听更不利于多中继MF-NOMA无线系统的安全传输。

从创设型机制到支撑型机制:数字时代信息财产法律保护的范式转型

与有体财产不同,信息财产的法律保护机制遵循着秘密—公开信息的二分,仅秘密信息可通过保护对客体的事实控制来保障利益实现,即支撑型机制;公开信息则主要依靠法律创设排他性权利加以保护,即创设型机制。受限于权利法定原则,创设型机制需因应技术进步不断作出调整。伴随着数据成为新生产要素,国内主流观点主张在数据之上设立新的排他性权利,美国、日本等国的数据保护实践却选择对支撑型机制进行扩张,基于企业对数据的事实控制建构保护制度,这为信息财产保护提供了新范式。在数字环境下,信息与其含义、主体及物理载体逐渐分离,企业对信息的私力控制范围进一步拓宽,支撑型机制适用范围扩张,且相对于创设型机制更具效率优势,传统秘密—公开信息的二分方式已不再适用。为弥补技术变革环境下法定知识产权僵化和滞后的缺陷,应构建更具包容性和现代性的支撑型机制,在商业秘密制度基础上建立合法控制信息保护制度,对向有限主体提供、采取相应控制措施并因此具有商业价值的信息,禁止他人未经许可的获取、利用、公开行为。