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An improved resource allocation algorithm based on Stackelberg game and gradient theory

With the development of the mobile communication technology and the widespread popularity of mobile terminals, the mobile data service, especially the indoor data service, is growing exponentially. Due to the limitations in spectrum efficiency and network coverage, the traditional cellular networks cannot meet the communication requirements. In order to solve this problem, femtocells are deployed in the traditional macro cells to form heterogeneous networks (HetNet), which can ensure the quality of indoor communication, enhance the coverage of the system network and improve the system capacity. However, the deployment of low-power nodes has introduced cross-layer interference between the macro base station layer and the layer of the femtocell. In particular, the macro users around the femtocells are vulnerable to the strong interference of the femtocells, which reduces the overall performance of the system. In order to solve the problem of interference in HetNet, the resource allocation method, as an effective solution of increasing interference has been research hot issue. In this paper, the Stackelberg game model is used to analyze the resource allocation in a two-tier HetNet. Different from the traditional system model which only focuses on the performance improvement of macro base stations, the profit function is established considering the overall performance and fairness of the system. The utility function defined as the average throughput, which does not adopt the monetary price to avoid excessive signaling overhead. Furthermore, we consider the process of solving the optimal strategy. In practical applications, the game participants are independent to determine the frequency of the shared spectrum, which cannot be known at the current moment of other participants' strategy. Considering this problem, we use iterative method to solve the problem. For better iterative properties, the paper proposes an exponential gradient iterative algorithm for the spectrum sharing strategy of the model. The algorithm uses the shared spectrum strategy of each user at the previous time to solve the current time spectrum strategy. The simulation results show that the proposed algorithm can effectively obtain the shared spectrum strategy with the greatest benefit to the authorized users, and has a better convergence performance than the linear gradient iterative algorithm.