# Traffic Offloading in Unlicensed Spectrum for 5G Cellular Network: A Two-Layer Game Approach
## 1 Introduction
In addition, collisions in overlapping areas could lead to deterioration of the whole network's throughput [21], as well as the additional energy consumption [22], especially for those APs which have too many stations. Conversely, few stations in one AP would mean the waste of AP resource, which cannot maximize the WiFi network performance. To address the issues mentioned above, we intend to make a balance on stations through associating the optimal station number in a WiFi AP and improve the channel resource utilization in a game way in this paper. Game theory has an important position in wireless resource management and there are also some game theory applications in LAA field [17; 18; 19; 23]. In [17], a spectrum sharing scheme based on the decision tree was built through the channel condition, load condition, etc., and then parts of data were offloaded from the operator which asked for a favor to the operator with few stations based on a repeated game. However, LTE and WiFi operators belong to different communication protocols and it is not practical to predict the state of the other operator and use the dynamic game with complete information in fact. A second-price reverse auction game was built in [18]: LAA network choses to work in a competition mode or in a cooperation mode according to their announced reserve rate. If they work in a competition mode, LAA randomly accesses the unlicensed channel and competes with WiFi based on a typical coexistence mechanism such as CSAT or LBT. For the cooperation mode, the LAA would carry the traffic of WiFi users in exchange for the exclusive access of the corresponding channel. Ref. [19] proposed a transaction from LTE-LAA to WiFi using CSAT way together with offloading, and then formulated a coalition game to improve resource utilization. In addition, in [23], a LAA-APs grand coalition was built and LAA could offload some data to WiFi networks when LAA traffic load is high within the grand coalition. However, the transmission efficiency of WiFi is much worse than LTE and the spectrum utilization of the WiFi network is bound to suffer from potential packet collisions due to its contention-based access protocol, especially when the number of competing WiFi users grows large, which would have bad effects on the whole network performance. However, existing works on spectrum sharing or traffic offloading mostly concentrate on the performance optimization in LAA, but ignore the throughput improvement in WiFi. In addition, many existing works use a simple single-layer game between two systems, which cannot effectively solve the problems of both simultaneously optimizing the performance of two systems and the load balancing issue in WiFi system. In this paper, we propose a two-layer game, namely a Coalition-Auction Game-based Transaction (CAGT) game in the whole network in 5 GHz unlicensed bands. A coalition game is used among WiFi APs in the first layer to balance the traffic and reduce collisions. The key idea of the proposed coalition forming is to merge the light-loaded APs with heavy-loaded APs into a coalition, consequently, the data of the heavy-loaded APs can be transferred to the light-loaded APs such that the maximum WiFi throughput can be achieved. In the second layer, an auction game between the LAA base station and AP coalitions is performed to better utilize the spectrum resources and implement the friendly coexistence between two systems. In this game, LAA BS is the auctioneer and AP coalitions are bidders; parts of WiFi data can be offloaded to LAA network in compensation for allocating more unlicensed resources to the LTE system. The main contributions of this paper can be summarized as follows. * To avoid the collision at the start of \"on\" period [13] in the CSAT mechanism, a modified LBT-based CSAT structure is designed, which could effectively increase the throughput of both LAA and WiFi. * To balance the stations among APs, the optimal station number that could maximize the average WiFi system throughput is obtained.