The problem of robustness-supported user association and small cell station(SCS) switching ON/OFF strategies in 5G millimeter wave(mm-wave) networks is investigated, where the robustness of access links is ensured and...The problem of robustness-supported user association and small cell station(SCS) switching ON/OFF strategies in 5G millimeter wave(mm-wave) networks is investigated, where the robustness of access links is ensured and the number of active SCSs is minimized for the reduction of the aggregation power consumption. Firstly, the problem is formulated as an integer programming(IP) problem. Then the problem is proved as a NP-hard problem by means of the simplification into the minimum dominant set(MDS), which is a NP-hard problem and is intractable to be solved in polynomial time. Secondly, a greedy-idea-based heuristic algorithm(GIHA) is proposed under the consideration of the complexity of the original optimization problem. Finally, superiorities of GIHA are demonstrated with the extensive simulations in 60 GHz mm-wave ultra-dense network in terms of access robustness and aggregate power consumption.展开更多
Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data...Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.展开更多
基金Supported by the National Natural Science Foundations of China(No.61771392,61771390,61871322,61501373,61271279)the National High Tochnology Research and Development Program of China(No.2014AA01A707,2015AA01A704)the Science and Technology on Avionics Integration Laboratory(No.20185553035)。
文摘The problem of robustness-supported user association and small cell station(SCS) switching ON/OFF strategies in 5G millimeter wave(mm-wave) networks is investigated, where the robustness of access links is ensured and the number of active SCSs is minimized for the reduction of the aggregation power consumption. Firstly, the problem is formulated as an integer programming(IP) problem. Then the problem is proved as a NP-hard problem by means of the simplification into the minimum dominant set(MDS), which is a NP-hard problem and is intractable to be solved in polynomial time. Secondly, a greedy-idea-based heuristic algorithm(GIHA) is proposed under the consideration of the complexity of the original optimization problem. Finally, superiorities of GIHA are demonstrated with the extensive simulations in 60 GHz mm-wave ultra-dense network in terms of access robustness and aggregate power consumption.
文摘Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.
