In ultra-dense networks(UDNs), large-scale deployment of femto base stations is an important technique for improving the network throughput and quality of service(QoS). In this paper, a multidimensional resource alloc...In ultra-dense networks(UDNs), large-scale deployment of femto base stations is an important technique for improving the network throughput and quality of service(QoS). In this paper, a multidimensional resource allocation algorithm based on noncooperation game theory is proposed to manage the resource allocation in UDNs, including transmission point association, user channels, and power. The algorithm derives a multidimensional resource optimization model and converts into a noncooperation game model according to the analysis of transmission point association user channel and power allocation. The algorithm includes two phases: transmission point association, and channel and power allocation. Then, feasible domain and discrete variables relaxation approaches are introduced to derive an approximate optimal multidimensional resource allocation solution with low complexity. Simulation results show that this method has some advantages in suppressing interference and improves the overall system throughput, while ensuring the QoS of femtocell users.展开更多
Ultra-dense networks(UDNs) is a promising solution to meet the exponential increase in mobile data traffic. But the ultra-dense deployment of cells inevitably brings complicated inter-cell interference(ICI) and existi...Ultra-dense networks(UDNs) is a promising solution to meet the exponential increase in mobile data traffic. But the ultra-dense deployment of cells inevitably brings complicated inter-cell interference(ICI) and existing interference coordination scheme cannot be directly applied. To minimize the aggregate interference of each small cells, this paper formulated the problem as a distributed noncooperation game-based interference coordination scheme in UDNs considering the real demand rate of each small cell user equipment(SUE) and proved it to be a potential game. An improved no-regret learning algorithm was introduced to coverage to the Nash equilibrium(NE) of the formulated game. Simulation results show that the proposed scheme has better performance compared with existing schemes.展开更多
基金supported by Natural Science Foundation of China (61372125)973 project (2013CB329104)+1 种基金Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA510005)the open research fund of National Mobile Communications Research Laboratory, Southeast University (2013D01, 2015D10)
文摘In ultra-dense networks(UDNs), large-scale deployment of femto base stations is an important technique for improving the network throughput and quality of service(QoS). In this paper, a multidimensional resource allocation algorithm based on noncooperation game theory is proposed to manage the resource allocation in UDNs, including transmission point association, user channels, and power. The algorithm derives a multidimensional resource optimization model and converts into a noncooperation game model according to the analysis of transmission point association user channel and power allocation. The algorithm includes two phases: transmission point association, and channel and power allocation. Then, feasible domain and discrete variables relaxation approaches are introduced to derive an approximate optimal multidimensional resource allocation solution with low complexity. Simulation results show that this method has some advantages in suppressing interference and improves the overall system throughput, while ensuring the QoS of femtocell users.
文摘Ultra-dense networks(UDNs) is a promising solution to meet the exponential increase in mobile data traffic. But the ultra-dense deployment of cells inevitably brings complicated inter-cell interference(ICI) and existing interference coordination scheme cannot be directly applied. To minimize the aggregate interference of each small cells, this paper formulated the problem as a distributed noncooperation game-based interference coordination scheme in UDNs considering the real demand rate of each small cell user equipment(SUE) and proved it to be a potential game. An improved no-regret learning algorithm was introduced to coverage to the Nash equilibrium(NE) of the formulated game. Simulation results show that the proposed scheme has better performance compared with existing schemes.
