A Novel Resource Allocation Method in Ultra-Dense Network Based on Noncooperation Game Theory

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.

Game-based distributed noncooperation interference coordination scheme in ultra-dense networks

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.

Classification of Selfish and Regular Nodes Based on Reputation Values in MANET Using Adaptive Decision Boundary

A MANET is a cooperative network in which each node has dual responsibilities of forwarding and routing thus node strength is a major factor because a lesser number of nodes reduces network performance. The existing reputation based methods have limitation due to their stricter punishment strategy because they isolate nodes from network participation having lesser reputation value and thus reduce the total strength of nodes in a network. In this paper we have proposed a mathematical model for the classification of nodes in MANETs using adaptive decision boundary. This model classifies nodes in two classes: selfish and regular node as well as it assigns the grade to individual nodes. The grade is computed by counting how many passes are required to classify a node and it is used to define the punishment strategy as well as enhances the reputation definition of traditional reputation based mechanisms. Our work provides the extent of noncooperation that a network can allow depending on the current strength of nodes for the given scenario and thus includes selfish nodes in network participation with warning messages. We have taken a leader node for reputation calculation and classification which saves energy of other nodes as energy is a major challenge of MANET. The leader node finally sends the warning message to low grade nodes and broadcasts the classification list in the MANET that is considered in the routing activity.