In ultra-dense networks (UDNs), large-scale deployment of femtocells base stations is an important technique for improving the network throughput and quality of service (QoS). However, traditional resource allocation algorithms are concerned with the improvement of the overall performance of the network. In this paper, a new resource allocation algorithm based on game theory is proposed to manage the resource allocation in UDNs. The quality of service (QoS) and energy consumption of each femtocell are considered. Firstly, a modified clustering algorithm is performed. Then we transform this resource allocation problem to a Stackelberg game. In sub-channel resource allocation, we aim to maximize the throughput of the whole system by cluster heads (CHs). The power allocation takes account of the balance between QoS requirement and transmit power consumption. Simulation results show that this method has some advantages in improving the overall system throughput, while obtaining a performance improvement compared with other algorithms.

In ultra-dense networks (UDNs), large-scale deployment of femtocells base stations is an important technique for improving the network throughput and quality of service (QoS). However, traditional resource allocation algorithms are concerned with the improvement of the overall performance of the network. In this paper, a new resource allocation algorithm based on game theory is proposed to manage the resource allocation in UDNs. The quality of service (QoS) and energy consumption of each femtocell are considered. Firstly, a modified clustering algorithm is performed. Then we transform this resource allocation problem to a Stackelberg game. In sub-channel resource allocation, we aim to maximize the throughput of the whole system by cluster heads (CHs). The power allocation takes account of the balance between QoS requirement and transmit power consumption. Simulation results show that this method has some advantages in improving the overall system throughput, while obtaining a performance improvement compared with other algorithms.