# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
## 1 Introduction  
With the constant evolution of cellular mobile communication systems, traditional cellular monolayer networks have failed to satisfy the requirement of transmission speed, and are beset by such issues as imperfect indoor coverage effect and insufficient capacity for outdoor hotspot areas [1]. Hence, the heterogeneous network (HetNet) has become a key technology for the next generation of communication networks. Given that over 70% mobile data is produced indoors at present [2; 3], the femtocell can effectively solve relevant issues about indoor communication due to its flexibility of deployment. Therefore, deploying femtocells in current macro-cellular networks to form macro-femto bilayer HetNet can transfer the load of the macro base station to femtocells, thus improving system performance. However, the introduction of femtocells will bring cross-layer interference between macro base stations and femtocells. The macro users located around femtocells tend to be highly subject to the strong interference of femtocells, thereby lowering the overall performance of system, especially the throughput [4]. In order to solve the above problems within academia, there are some cases using resources allocation method. In recent years, most research concerning resource allocation algorithms have introduced game theory [5; 6]. A game of resource allocation problem in HetNets involves a set of players, strategies,and payoffs. The players can be defined as base stations and users. Strategies can be the choice of transmission power level or subcarrier allocation. The payoff is the evaluation for a player of all possible outcomes represented by the utility function. Based on these settings, the interferences in wireless networks can then be considered as a result of the frequency resources among stations and users. Due to the strong transmitting power and wide coverage scope, the macro base station plays a more important role than femtocells in the formation of cross-layer interference, so it should first satisfy the requirement of frequency spectrum of macro users. Moreover, players cannot know strategy at the current moment, which means that it is a game with incomplete information. In previous literature, games with incomplete information--known as Bayesian games--are employed to address these kinds of issues. These games often assume that the players act independently according to some complex strategies for optimizing the setting of various resources of the network. With respect to HetNet, the macro plays a centralized role and can communicate with the low power node (LPNs) through the X2 interface. Thus, the topology matches a game with the macro base station being leader and the femtocells acting as followers. With this understanding, the Stackelberg game is a strategic game in economics in which the leader firm moves first and then the follower firms move sequentially. In the Stackelberg model, the leader can make prior decisions, and followers will choose a reasonable resources allocation scheme based on these decisions. Therefore, the Stackelberg game theory is more appropriate to analyze the resources allocation of HetNet based on cross-layer interference suppression. There are already some researchers studying this area. The Stackelberg game theory was used to solve the resource allocation problem both in power and in spectrum [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. In the two-tier network scenario of single macro community, reference [8] proposed a supermodel game-based femtocell downlink power control algorithm in the double network of single community. The proposed algorithm maximized the network capacity through reducing femtocells' cross-layer interference upon neighboring macro users, yet without considering the interference of the macro base station upon femtocell users or the macro base station in game process. In the same network scenario, [10] proposed a power control algorithm with self-adapting utility based on Signal to Interference plus Noise Ratio (SINR).