Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC...Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.展开更多
A wireless powered communication network(WPCN)assisted by intelligent reflecting surface(IRS)is proposed in this paper,which can transfer information by non-orthogonal multiple access(NOMA)technology.In the system,in ...A wireless powered communication network(WPCN)assisted by intelligent reflecting surface(IRS)is proposed in this paper,which can transfer information by non-orthogonal multiple access(NOMA)technology.In the system,in order to ensure that the hybrid access point(H-AP)can correctly decode user information via successive interference cancellation(SIC)technology,the information transmit power of user needs to satisfy a certain threshold,so as to meet the corresponding SIC constraints.Therefore,when the number of users who transfer information simultaneously increases,the system performance will be greatly restricted.To minimize the influence of SIC constraints on system performance,users are firstly clustered,and then each cluster collects energy from H-AP and finally,users transfer information based on NOMA with the assistance of IRS.Specifically,this paper aims to maximize the sum throughput of the system by jointly optimizing the beamforming of IRS and resource allocation of the system.The semi-definite relaxation(SDR)algorithm is employed to alternately optimize the beamforming of IRS in each time slot,and the joint optimization problem about user’s transmit power and time is transformed into two optimal time allocation sub-problems.The numerical results show that the proposed optimization scheme can effectively improve the sum throughput of the system.In addition,the results in the paper further reveals the positive impact of IRS on improving the sum throughput of the system.展开更多
Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling...Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.展开更多
User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation re...User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user;2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.展开更多
AP deployment is significant for indoor WLAN system to achieve seamless coverage. The available algorithms do not take user distribution into consideration so that poor user coverage and imbalanced network load occur....AP deployment is significant for indoor WLAN system to achieve seamless coverage. The available algorithms do not take user distribution into consideration so that poor user coverage and imbalanced network load occur. Therefore, this paper proposed a novel AP placement algorithm to bridge the AP deployment with user distribution. The proposed algorithm employs statistics theory to model the user distribution as its location and probability. Then we obtain the AP location based on the fuzzy C-clustering algorithm. The proposed algorithm is practical for implementation, which means the actual signal transmission isn't required in our proposed method. The simulation results show that the proposed algorithm could automatically achieve a good AP deployment with different user distribution, and provide a good performance in the maximum users and AP load balance in WLAN.展开更多
This research is interested in the user ratings of Apps on Apple Stores. The purpose of this research is to have a better understanding of some characteristics of the good Apps on Apple Store so Apps makers can potent...This research is interested in the user ratings of Apps on Apple Stores. The purpose of this research is to have a better understanding of some characteristics of the good Apps on Apple Store so Apps makers can potentially focus on these traits to maximize their profit. The data for this research is collected from kaggle.com, and originally collected from iTunes Search API, according to the abstract of the data. Four different attributes contribute directly toward an App’s user rating: rating_count_tot, rating_count_ver, user_rating and user_rating_ver. The relationship between Apps receiving higher ratings and Apps receiving lower ratings is analyzed using Exploratory Data Analysis and Data Science technique “clustering” on their numerical attributes. Apps, which are represented as a data point, with similar characteristics in rating are classified as belonging to the same cluster, while common characteristics of all Apps in the same clusters are the determining traits of Apps for that cluster. Both techniques are achieved using Google Colab and libraries including pandas, numpy, seaborn, and matplotlib. The data reveals direct correlation from number of devices supported and languages supported to user rating and inverse correlation from size and price of the App to user rating. In conclusion, free small Apps that many different types of users are able to use are generally well rated by most users, according to the data.展开更多
With the development of micro-satellite technology,traditional monolithic satellites can be replaced by micro-satellite clusters to achieve high flexibility and dynamic reconfiguration capability.For satellite cluster...With the development of micro-satellite technology,traditional monolithic satellites can be replaced by micro-satellite clusters to achieve high flexibility and dynamic reconfiguration capability.For satellite clusters based on the frequency division-code division multiple access(FD-CDMA)communication system,the inter-satellite ranging precision is usually constrained due to the influence ofmulti-address interference(MAI).Themulti-user detection(MUD)is a solution to MAI,which can be divided into two categories:the linear detector(LD)and the non-linear detector(NLD).The general idea of the LD is aiming to make a better decision during the symbol decision process by using the information of all channels.However,it is not beneficial for the signal phase tracking precision.Instead,the principle of the NLD is to rebuild the interference signal and cancel it from the original one,which can improve the ranging performance at the expense of considerable delays.In order to enable simultaneous ranging and communication and reduce multi-node ranging performance degradation,this paper proposes an NLD scheme based on a delay locked loop(DLL),which simplifies the receiver structure and introduces no delay in the decision process.This scheme utilizes the information obtained from the interference channel to reconstruct the interference signal and then cancels it from the original delayed signal.Therefore,the DLL input signal-to-interference ratio(SIR)of the desired channel can be significantly improved.The experimental results show that with the proposed scheme,the standard deviation of the tracking steady error is decreased from 5.59 cm to 3.97 cm for SIR=5 dB,and 13.53 cm to 5.77 cm for SIR=-5 dB,respectively.展开更多
A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean ...A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean clustering and iterative operations. The simulation results show that the proposed method can determined the number of PUs blindly and achieves better performance than traditional expectation-maximization (EM) algorithm.展开更多
We propose an algorithm for learning hierarchical user interest models according to the Web pages users have browsed. In this algorithm, the interests of a user are represented into a tree which is called a user inter...We propose an algorithm for learning hierarchical user interest models according to the Web pages users have browsed. In this algorithm, the interests of a user are represented into a tree which is called a user interest tree, the content and the structure of which can change simultaneously to adapt to the changes in a user's interests. This expression represents a user's specific and general interests as a continuurn. In some sense, specific interests correspond to shortterm interests, while general interests correspond to longterm interests. So this representation more really reflects the users' interests. The algorithm can automatically model a us er's multiple interest domains, dynamically generate the in terest models and prune a user interest tree when the number of the nodes in it exceeds given value. Finally, we show the experiment results in a Chinese Web Site.展开更多
The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced pr...The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.展开更多
基金This work was funded by Multimedia University under Grant Number MMUI/170084.
文摘Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.
基金supported by the Key Scientific and Technological Projects in Henan Province(202102310560)。
文摘A wireless powered communication network(WPCN)assisted by intelligent reflecting surface(IRS)is proposed in this paper,which can transfer information by non-orthogonal multiple access(NOMA)technology.In the system,in order to ensure that the hybrid access point(H-AP)can correctly decode user information via successive interference cancellation(SIC)technology,the information transmit power of user needs to satisfy a certain threshold,so as to meet the corresponding SIC constraints.Therefore,when the number of users who transfer information simultaneously increases,the system performance will be greatly restricted.To minimize the influence of SIC constraints on system performance,users are firstly clustered,and then each cluster collects energy from H-AP and finally,users transfer information based on NOMA with the assistance of IRS.Specifically,this paper aims to maximize the sum throughput of the system by jointly optimizing the beamforming of IRS and resource allocation of the system.The semi-definite relaxation(SDR)algorithm is employed to alternately optimize the beamforming of IRS in each time slot,and the joint optimization problem about user’s transmit power and time is transformed into two optimal time allocation sub-problems.The numerical results show that the proposed optimization scheme can effectively improve the sum throughput of the system.In addition,the results in the paper further reveals the positive impact of IRS on improving the sum throughput of the system.
基金supported by the State Grid Science and Technology Project (No.5442AI90009)Natural Science Foundation of China (No. 6170337)
文摘Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.
文摘User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user;2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.
基金the financial support by National Natural Science Foundation of China (61571162)Science and Technology Project of Ministry of Public Security Foundation (2015GABJC38)Major National Science and Technology Project (2015ZX03004002-004)
文摘AP deployment is significant for indoor WLAN system to achieve seamless coverage. The available algorithms do not take user distribution into consideration so that poor user coverage and imbalanced network load occur. Therefore, this paper proposed a novel AP placement algorithm to bridge the AP deployment with user distribution. The proposed algorithm employs statistics theory to model the user distribution as its location and probability. Then we obtain the AP location based on the fuzzy C-clustering algorithm. The proposed algorithm is practical for implementation, which means the actual signal transmission isn't required in our proposed method. The simulation results show that the proposed algorithm could automatically achieve a good AP deployment with different user distribution, and provide a good performance in the maximum users and AP load balance in WLAN.
文摘This research is interested in the user ratings of Apps on Apple Stores. The purpose of this research is to have a better understanding of some characteristics of the good Apps on Apple Store so Apps makers can potentially focus on these traits to maximize their profit. The data for this research is collected from kaggle.com, and originally collected from iTunes Search API, according to the abstract of the data. Four different attributes contribute directly toward an App’s user rating: rating_count_tot, rating_count_ver, user_rating and user_rating_ver. The relationship between Apps receiving higher ratings and Apps receiving lower ratings is analyzed using Exploratory Data Analysis and Data Science technique “clustering” on their numerical attributes. Apps, which are represented as a data point, with similar characteristics in rating are classified as belonging to the same cluster, while common characteristics of all Apps in the same clusters are the determining traits of Apps for that cluster. Both techniques are achieved using Google Colab and libraries including pandas, numpy, seaborn, and matplotlib. The data reveals direct correlation from number of devices supported and languages supported to user rating and inverse correlation from size and price of the App to user rating. In conclusion, free small Apps that many different types of users are able to use are generally well rated by most users, according to the data.
基金supported by the China National Funds of Distributed Young Scientists(61525403)the Fundamental Research Funds for the Central Universities(2018QNA4053)
文摘With the development of micro-satellite technology,traditional monolithic satellites can be replaced by micro-satellite clusters to achieve high flexibility and dynamic reconfiguration capability.For satellite clusters based on the frequency division-code division multiple access(FD-CDMA)communication system,the inter-satellite ranging precision is usually constrained due to the influence ofmulti-address interference(MAI).Themulti-user detection(MUD)is a solution to MAI,which can be divided into two categories:the linear detector(LD)and the non-linear detector(NLD).The general idea of the LD is aiming to make a better decision during the symbol decision process by using the information of all channels.However,it is not beneficial for the signal phase tracking precision.Instead,the principle of the NLD is to rebuild the interference signal and cancel it from the original one,which can improve the ranging performance at the expense of considerable delays.In order to enable simultaneous ranging and communication and reduce multi-node ranging performance degradation,this paper proposes an NLD scheme based on a delay locked loop(DLL),which simplifies the receiver structure and introduces no delay in the decision process.This scheme utilizes the information obtained from the interference channel to reconstruct the interference signal and then cancels it from the original delayed signal.Therefore,the DLL input signal-to-interference ratio(SIR)of the desired channel can be significantly improved.The experimental results show that with the proposed scheme,the standard deviation of the tracking steady error is decreased from 5.59 cm to 3.97 cm for SIR=5 dB,and 13.53 cm to 5.77 cm for SIR=-5 dB,respectively.
基金Sponsored by the Scientific Research Program of Beijing Municipal Commission of Education ( Grant No. KZ2010100009009)Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality ( Grant No. PHR201008186) Scientific Research Fund of Heilongjiang Provincial Education Department ( Grant No. 11541083)
文摘A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean clustering and iterative operations. The simulation results show that the proposed method can determined the number of PUs blindly and achieves better performance than traditional expectation-maximization (EM) algorithm.
基金Supported by the National Natural Science Funda-tion of China (69973012 ,60273080)
文摘We propose an algorithm for learning hierarchical user interest models according to the Web pages users have browsed. In this algorithm, the interests of a user are represented into a tree which is called a user interest tree, the content and the structure of which can change simultaneously to adapt to the changes in a user's interests. This expression represents a user's specific and general interests as a continuurn. In some sense, specific interests correspond to shortterm interests, while general interests correspond to longterm interests. So this representation more really reflects the users' interests. The algorithm can automatically model a us er's multiple interest domains, dynamically generate the in terest models and prune a user interest tree when the number of the nodes in it exceeds given value. Finally, we show the experiment results in a Chinese Web Site.
文摘The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.