Joint Recommendation and Pricing for Cache-Aided RAN with Malicious Users: A Game Theoretic Method

As mobile data traffic has explosively grown during the past decades, pushing popular contents to small cells has been proposed to deal with the growing data demands. To improve the cache hit ratio, the recommender system is employed to recommend cached contents when the requests are not hit by the cache. However, how to persuade users to accept recommended files remains an open problem. We conceive a method that the network operators can give a discount on the traffic cost of the recommended contents. However, some users may maliciously request unpopular contents to get the discount, which will reduce the profit of the virtual network operator (VNO). In order to punish the malicious behaviour, we the VNOcan reduce the recommendation probability to these users. Meanwhile, these malicious users will reduce the malicious probability to increase the revenue. To study the interactions between the profit of the VNO and the revenue of the users, we formulate a non-cooperative game to find the Nash equilibrium (NE) of the recommendation probability of the VNO and the malicious probability of the users. Simulation results indicate that the VNO's profit and the user's revenue can be significantly increased with the proposed system compared with the system without joint recommendation and pricing schemes.