大规模机器类通信(massive Machine Type Communications,mMTC)旨在实现海量的物联网设备通信,被广泛应用于工业自动化、智能交通、智慧城市、智慧医疗等领域。面对数量巨大的用户设备,基于授权的随机接入技术存在接入失败概率高和信令...大规模机器类通信(massive Machine Type Communications,mMTC)旨在实现海量的物联网设备通信,被广泛应用于工业自动化、智能交通、智慧城市、智慧医疗等领域。面对数量巨大的用户设备,基于授权的随机接入技术存在接入失败概率高和信令开销大的弊端。为了解决这些弊端,学术界和工业界提出了免授权随机接入(Grant-Free Random Access,GFRA)技术,此技术能够使活跃用户设备在发送导频序列后直接传输数据而无需等待基站授权。因此,在基于免授权接入的mMTC中,基站的一个关键任务是进行用户设备活跃性检测。而在实际场景中,用户设备往往在连续时隙上呈现活跃状态,即存在时间相关性。特别地,利用时间相关性的先验信息可以降低用户设备活跃性的错误检测概率。本文从该出发点提出一种基于最大后验(Maximum A Posteriori,MAP)概率的坐标下降算法。具体而言,该算法首先从最大后验概率的角度构建目标函数,并通过Markov链模拟连续时隙中的状态转移。其次,使用坐标下降法处理接收信号的协方差得到活跃用户设备集合。最后,对当前时隙的用户设备最可能发生的情况进行决策。仿真结果表明,相比经典的活跃用户检测算法,本文算法拥有更低的错误检测概率。并且,当导频序列长度较短及活跃用户数量增加时,仍能表现出较好的检测性能。此外,随着接收天线增多,本文算法相比经典算法的性能增益更显著。展开更多
The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,th...The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.展开更多
5G无线通信网络中,大规模机器类通信(massive machine type communication,mMTC)是一个新兴的研究课题.对于mMTC,已经提出非正交多址接入(non-orthogonal multiple access,NOMA)来支持其大规模接入.由于mMTC实时接入的稀疏性,基于压缩...5G无线通信网络中,大规模机器类通信(massive machine type communication,mMTC)是一个新兴的研究课题.对于mMTC,已经提出非正交多址接入(non-orthogonal multiple access,NOMA)来支持其大规模接入.由于mMTC实时接入的稀疏性,基于压缩感知的算法可用于识别活跃用户并恢复稀疏信道状态信息(channel state information,CSI)向量.于是提出一种基于期望传播(expectation propagation,EP)的贝叶斯消息传递算法,用于NOMA中的联合活跃用户检测(active user detection,AUD)和信道估计(channel estimation,CE).该算法使用高斯分布对复杂的目标分布函数近似表达,实现线性计算复杂度,通过引入阻尼因子可以确保算法的收敛性.分析与仿真结果表明,基于EP的消息传递算法在联合活跃用户检测和信道估计中比现有算法具有更高的检测准确率和更低的漏检率及均方误差.展开更多
This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and m...This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.展开更多
The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an import...The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.展开更多
文摘大规模机器类通信(massive Machine Type Communications,mMTC)旨在实现海量的物联网设备通信,被广泛应用于工业自动化、智能交通、智慧城市、智慧医疗等领域。面对数量巨大的用户设备,基于授权的随机接入技术存在接入失败概率高和信令开销大的弊端。为了解决这些弊端,学术界和工业界提出了免授权随机接入(Grant-Free Random Access,GFRA)技术,此技术能够使活跃用户设备在发送导频序列后直接传输数据而无需等待基站授权。因此,在基于免授权接入的mMTC中,基站的一个关键任务是进行用户设备活跃性检测。而在实际场景中,用户设备往往在连续时隙上呈现活跃状态,即存在时间相关性。特别地,利用时间相关性的先验信息可以降低用户设备活跃性的错误检测概率。本文从该出发点提出一种基于最大后验(Maximum A Posteriori,MAP)概率的坐标下降算法。具体而言,该算法首先从最大后验概率的角度构建目标函数,并通过Markov链模拟连续时隙中的状态转移。其次,使用坐标下降法处理接收信号的协方差得到活跃用户设备集合。最后,对当前时隙的用户设备最可能发生的情况进行决策。仿真结果表明,相比经典的活跃用户检测算法,本文算法拥有更低的错误检测概率。并且,当导频序列长度较短及活跃用户数量增加时,仍能表现出较好的检测性能。此外,随着接收天线增多,本文算法相比经典算法的性能增益更显著。
基金supported by National Key Research and Development Program of China under Grants 2021YFB1600500,2021YFB3201502,and 2022YFB3207704Natural Science Foundation of China(NSFC)under Grants U2233216,62071044,61827901,62088101 and 62201056+1 种基金supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62supported by Beijing Nova Program,Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009.
文摘The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.
文摘5G无线通信网络中,大规模机器类通信(massive machine type communication,mMTC)是一个新兴的研究课题.对于mMTC,已经提出非正交多址接入(non-orthogonal multiple access,NOMA)来支持其大规模接入.由于mMTC实时接入的稀疏性,基于压缩感知的算法可用于识别活跃用户并恢复稀疏信道状态信息(channel state information,CSI)向量.于是提出一种基于期望传播(expectation propagation,EP)的贝叶斯消息传递算法,用于NOMA中的联合活跃用户检测(active user detection,AUD)和信道估计(channel estimation,CE).该算法使用高斯分布对复杂的目标分布函数近似表达,实现线性计算复杂度,通过引入阻尼因子可以确保算法的收敛性.分析与仿真结果表明,基于EP的消息传递算法在联合活跃用户检测和信道估计中比现有算法具有更高的检测准确率和更低的漏检率及均方误差.
基金supported by National Natural Science Foundation of China(NSFC)under Grant No.62001190The work of J.Wen was supported by NSFC(Nos.11871248,61932010,61932011)+3 种基金the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019),Guangdong Major Project of Basic and Applied Basic Research(2019B030302008)the Fundamental Research Funds for the Central Universities(No.21618329)The work of P.Fan was supported by National Key R&D Project(No.2018YFB1801104)NSFC Project(No.6202010600).
文摘This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.
文摘The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.
