This paper studies an interference coordination method by means of spectrum allocation in Long-Term Evolution (LTE) multi-cell scenario that comprises of macrocells and femtocells. The purpose is to maximize the total...This paper studies an interference coordination method by means of spectrum allocation in Long-Term Evolution (LTE) multi-cell scenario that comprises of macrocells and femtocells. The purpose is to maximize the total throughput of femtocells while ensuring the Signal-to-Interference plus Noise Ratio (SINR) of the edge macro mobile stations (mMSs) and the edge femtocell Mobile Stations (fMSs). A new spectrum allocation algorithm based on graph theory is proposed to reduce the interference. Firstly, the ratio of Resource Blocks (RBs) that mMSs occupy is obtained by genetic algorithm. Then, after considering the impact of the macro Base Stations (mBSs) and small scale fading to the fMS on different RBs, multi-interference graphs are established and the spectrum is allocated dynamically. The simulation results show that the proposed algorithm can meet the Quality of Service (QoS) requirements of the mMSs. It can strike a balance between the edge fMSs' throughput and the whole fMSs' throughput.展开更多
In orthogonal frequency division multiple access(OFDMA) based femtocell networks,the co-tier interference among femto base stations(FBS) becomes important in multiuser and densely deployed environment.In order to miti...In orthogonal frequency division multiple access(OFDMA) based femtocell networks,the co-tier interference among femto base stations(FBS) becomes important in multiuser and densely deployed environment.In order to mitigate the co-tier interference and enhance the system total throughput,this paper proposed a best effort spectrum allocation scheme based on the extension of graph theory.In the scheme,a controller was proposed to collect the channel state information(CSI)of all femtocell user equipments(FUEs) in a certain range.Then,the controller evaluated the signal-to-interference Ratio(SIR) of each FUE and determined the set of its interference neighbors.By calculating the received power matrix(RPM) among FUEs and building interference graph matrix(IGM),different spectrum resource blocks(RBs) were assigned to the users with interference relation,while users without interference relation shared the same RBs,which could increase the spectrum efficiency.Simulation results show that the proposed algorithm can significantly improve the RB usage efficiency compared with the basic graph coloring theory,and more than 80% improvement can be acquired in dense deployment scenario.Besides,the throughput of both cell edge macro user equipments(MUEs) and cell edge FUEs is guaranteed on the premise of low interference.展开更多
The allocation of resources in English teaching can improve the ability of resource sharing, in order to optimize the allocation of resources, so as to improve the performance of English teaching, and promote the cons...The allocation of resources in English teaching can improve the ability of resource sharing, in order to optimize the allocation of resources, so as to improve the performance of English teaching, and promote the construction of English teaching resources database, a method of optimizing the allocation of English teaching resources is proposed based on network cloud platform. Text semantic key words conceptual decision tree model is constructed for massive English teaching resources allocation, semantic information conversion method is used to compute key semantic features of English Teaching resources, the concept convergence point of English Teaching resource allocation is formed in semantic model. According to the set between the upper and lower relationship, a decision tree model of English Teaching semantic subject words is constructed, semantic conversion and information extraction are realized. English teaching resources optimization allocation simulation is taken in the cloud platform, simulation results show that the scheduling performance of English teaching resources is better, and the adaptive allocation ability of English teaching resources is stronger, and the resource utilization rate is higher.展开更多
基金Supported by National Natural Science Foundation of China (61171094, 61071092)National Science & Technology Key Project (2011ZX03001-006-02, 2011ZX03005-004-03)Key Project of Jiangsu Provincial Natural Science Foundation (BK2011027)
文摘This paper studies an interference coordination method by means of spectrum allocation in Long-Term Evolution (LTE) multi-cell scenario that comprises of macrocells and femtocells. The purpose is to maximize the total throughput of femtocells while ensuring the Signal-to-Interference plus Noise Ratio (SINR) of the edge macro mobile stations (mMSs) and the edge femtocell Mobile Stations (fMSs). A new spectrum allocation algorithm based on graph theory is proposed to reduce the interference. Firstly, the ratio of Resource Blocks (RBs) that mMSs occupy is obtained by genetic algorithm. Then, after considering the impact of the macro Base Stations (mBSs) and small scale fading to the fMS on different RBs, multi-interference graphs are established and the spectrum is allocated dynamically. The simulation results show that the proposed algorithm can meet the Quality of Service (QoS) requirements of the mMSs. It can strike a balance between the edge fMSs' throughput and the whole fMSs' throughput.
基金supported by the National Key Technology R&D Program of China(2012ZX03001031-004)the Fundamental Research Funds for the Central Universities (BUPT 2013RC0111)
文摘In orthogonal frequency division multiple access(OFDMA) based femtocell networks,the co-tier interference among femto base stations(FBS) becomes important in multiuser and densely deployed environment.In order to mitigate the co-tier interference and enhance the system total throughput,this paper proposed a best effort spectrum allocation scheme based on the extension of graph theory.In the scheme,a controller was proposed to collect the channel state information(CSI)of all femtocell user equipments(FUEs) in a certain range.Then,the controller evaluated the signal-to-interference Ratio(SIR) of each FUE and determined the set of its interference neighbors.By calculating the received power matrix(RPM) among FUEs and building interference graph matrix(IGM),different spectrum resource blocks(RBs) were assigned to the users with interference relation,while users without interference relation shared the same RBs,which could increase the spectrum efficiency.Simulation results show that the proposed algorithm can significantly improve the RB usage efficiency compared with the basic graph coloring theory,and more than 80% improvement can be acquired in dense deployment scenario.Besides,the throughput of both cell edge macro user equipments(MUEs) and cell edge FUEs is guaranteed on the premise of low interference.
文摘The allocation of resources in English teaching can improve the ability of resource sharing, in order to optimize the allocation of resources, so as to improve the performance of English teaching, and promote the construction of English teaching resources database, a method of optimizing the allocation of English teaching resources is proposed based on network cloud platform. Text semantic key words conceptual decision tree model is constructed for massive English teaching resources allocation, semantic information conversion method is used to compute key semantic features of English Teaching resources, the concept convergence point of English Teaching resource allocation is formed in semantic model. According to the set between the upper and lower relationship, a decision tree model of English Teaching semantic subject words is constructed, semantic conversion and information extraction are realized. English teaching resources optimization allocation simulation is taken in the cloud platform, simulation results show that the scheduling performance of English teaching resources is better, and the adaptive allocation ability of English teaching resources is stronger, and the resource utilization rate is higher.
