The continuously booming of information technology has shed light on developing a variety of communication networks,multimedia,social networks and Internet of Things applications.However,users inevitably suffer from t...The continuously booming of information technology has shed light on developing a variety of communication networks,multimedia,social networks and Internet of Things applications.However,users inevitably suffer from the intrusion of malicious users.Some studies focus on static characteristics of malicious users,which is easy to be bypassed by camouflaged malicious users.In this paper,we present a malicious user detection method based on ensemble feature selection and adversarial training.Firstly,the feature selection alleviates the dimension disaster problem and achieves more accurate classification performance.Secondly,we embed features into the multidimensional space and aggregate it into a feature map to encode the explicit content preference and implicit interaction preference.Thirdly,we use an effective ensemble learning which could avoid over-fitting and has good noise resistance.Finally,we propose a datadriven neural network detection model with the regularization technique adversarial training to deeply analyze the characteristics.It simplifies the parameters,obtaining more robust interaction features and pattern features.We demonstrate the effectiveness of our approach with numerical simulation results for malicious user detection,where the robustness issues are notable concerns.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
In this paper, an optimal multi user detector in DS/CDMA communication systems based on the mean field annealing (MFA) neural network is proposed. It is shown that the NP complete problem of minimizing the objective...In this paper, an optimal multi user detector in DS/CDMA communication systems based on the mean field annealing (MFA) neural network is proposed. It is shown that the NP complete problem of minimizing the objective function of the optimal multi user detector can be translated into minimizing an MFA network energy function. Numerical results show that the proposed detector offers significant performance gain relative to the conventional detector and decorrelating detector while it can be implemented easily in analog hardware.展开更多
This paper describes a linear interference cancellation multi user detector for synchronous DS CDMA systems under the condition that all spread spectrum code waveforms have the constant cross correlating coefficien...This paper describes a linear interference cancellation multi user detector for synchronous DS CDMA systems under the condition that all spread spectrum code waveforms have the constant cross correlating coefficients. The basic idea is to get the estimation for total multiple access interference (MAI) of all users using a reference code waveform, then subtract the total MAI from the received signal. The structure of such a detector is nearly similar to the conventional detector. The BER expression obtained in the paper shows significant performance improvement compared to the other detectors.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
In recent years, non-orthogonal multiple access(NOMA) has attracted a lot of attention as a novel and promising power-domain us-er multiplexing scheme for Long-Term Evolution(LTE) enhancement and 5G. NOMA is able to c...In recent years, non-orthogonal multiple access(NOMA) has attracted a lot of attention as a novel and promising power-domain us-er multiplexing scheme for Long-Term Evolution(LTE) enhancement and 5G. NOMA is able to contribute to the improvement ofthe tradeoff between system capacity and user fairness(i.e., cell-edge user experience). This improvement becomes in particularemphasized in a cellular system where the channel conditions vary significantly among users due to the near-far effect. In this arti-cle, we provide an overview of the concept, design and performance of NOMA. In addition, we review the potential benefits and is-sues of NOMA over orthogonal multiple access(OMA) such as orthogonal frequency division multiple access(OFDMA) adoptedby LTE, and the status of 3GPP standardization related to NOMA.展开更多
Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity...Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers,safe operation of power grids,and sustainable development of cities.However,current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples,and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas.In this study,we use the Seasonal-Trend decomposition procedure based on Loess(STL)based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang,and analyze the relationship between spatial variation and urban functions through Geodetector.The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity,and the abnormal electricity users are mainly located in areas with highly mixed residential,commercial and entertainment functions in the city.The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.展开更多
基金supported in part by projects of National Natural Science Foundation of China under Grant 61772406 and Grant 61941105supported in part by projects of the Fundamental Research Funds for the Central Universitiesthe Innovation Fund of Xidian University under Grant 500120109215456.
文摘The continuously booming of information technology has shed light on developing a variety of communication networks,multimedia,social networks and Internet of Things applications.However,users inevitably suffer from the intrusion of malicious users.Some studies focus on static characteristics of malicious users,which is easy to be bypassed by camouflaged malicious users.In this paper,we present a malicious user detection method based on ensemble feature selection and adversarial training.Firstly,the feature selection alleviates the dimension disaster problem and achieves more accurate classification performance.Secondly,we embed features into the multidimensional space and aggregate it into a feature map to encode the explicit content preference and implicit interaction preference.Thirdly,we use an effective ensemble learning which could avoid over-fitting and has good noise resistance.Finally,we propose a datadriven neural network detection model with the regularization technique adversarial training to deeply analyze the characteristics.It simplifies the parameters,obtaining more robust interaction features and pattern features.We demonstrate the effectiveness of our approach with numerical simulation results for malicious user detection,where the robustness issues are notable concerns.
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In this paper, an optimal multi user detector in DS/CDMA communication systems based on the mean field annealing (MFA) neural network is proposed. It is shown that the NP complete problem of minimizing the objective function of the optimal multi user detector can be translated into minimizing an MFA network energy function. Numerical results show that the proposed detector offers significant performance gain relative to the conventional detector and decorrelating detector while it can be implemented easily in analog hardware.
文摘This paper describes a linear interference cancellation multi user detector for synchronous DS CDMA systems under the condition that all spread spectrum code waveforms have the constant cross correlating coefficients. The basic idea is to get the estimation for total multiple access interference (MAI) of all users using a reference code waveform, then subtract the total MAI from the received signal. The structure of such a detector is nearly similar to the conventional detector. The BER expression obtained in the paper shows significant performance improvement compared to the other detectors.
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In recent years, non-orthogonal multiple access(NOMA) has attracted a lot of attention as a novel and promising power-domain us-er multiplexing scheme for Long-Term Evolution(LTE) enhancement and 5G. NOMA is able to contribute to the improvement ofthe tradeoff between system capacity and user fairness(i.e., cell-edge user experience). This improvement becomes in particularemphasized in a cellular system where the channel conditions vary significantly among users due to the near-far effect. In this arti-cle, we provide an overview of the concept, design and performance of NOMA. In addition, we review the potential benefits and is-sues of NOMA over orthogonal multiple access(OMA) such as orthogonal frequency division multiple access(OFDMA) adoptedby LTE, and the status of 3GPP standardization related to NOMA.
基金National Natural Science Foundation of China(Nos.4180130642171466)The Scientific Research Program of the Department of Natural Resources of Hubei Province(No.ZRZY2021KJ02)。
文摘Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers,safe operation of power grids,and sustainable development of cities.However,current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples,and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas.In this study,we use the Seasonal-Trend decomposition procedure based on Loess(STL)based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang,and analyze the relationship between spatial variation and urban functions through Geodetector.The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity,and the abnormal electricity users are mainly located in areas with highly mixed residential,commercial and entertainment functions in the city.The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.
