Femtocaching in wireless video networks: Distributed framework based on exact potential game

Femtocaching is a distributed caching system designed to assist the macro base station by handling the popular content downloading. Usually, femto base stations (FBSs) have weak backhaul links but large storage capacity. When the requested files are cached, FBSs serve mobile users (MUs) through high-rate wireless link and avoid the backhaul bottleneck. However, the distributed caching problem is NP-complete. Previous works solve this problem with greedy algorithms in a centralized way. In this paper, we formulate this problem as an exact potential game (EPG), where each FBS acts as a game player with the constraint of cache size. The best Nash equilibrium (NE) is the global optimum solution of the distributed caching problem. We design an iterative algorithm in a decentralized way, where only local information exchange is needed. Theoretical analysis shows the best NE can be achieved and simulation results illustrate that the proposed algorithm performs better than the simple popular cache system and the traditional methods.

[1]  Yang Song,et al.  Optimal gateway selection in multi-domain wireless networks: a potential game perspective , 2011, MobiCom.

[2]  Alexandros G. Dimakis,et al.  Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution , 2012, IEEE Communications Magazine.

[3]  Alagan Anpalagan,et al.  Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games , 2012, IEEE Journal of Selected Topics in Signal Processing.

[4]  Alexandros G. Dimakis,et al.  FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers , 2013, IEEE Transactions on Information Theory.

[5]  Alexandros G. Dimakis,et al.  FemtoCaching: Wireless video content delivery through distributed caching helpers , 2011, 2012 Proceedings IEEE INFOCOM.

[6]  Jeffrey G. Andrews,et al.  Femtocell networks: a survey , 2008, IEEE Communications Magazine.

[7]  Jeffrey G. Andrews,et al.  Heterogeneous cellular networks: From theory to practice , 2012, IEEE Communications Magazine.

[8]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[9]  L. Shapley,et al.  REGULAR ARTICLEPotential Games , 1996 .

[10]  Xianfu Chen,et al.  Optimal Base Station Sleeping in Green Cellular Networks: A Distributed Cooperative Framework Based on Game Theory , 2015, IEEE Transactions on Wireless Communications.

[11]  L. Shapley,et al.  Potential Games , 1994 .

[12]  Pablo Rodriguez,et al.  I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system , 2007, IMC '07.

[13]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[14]  Christian Bettstetter,et al.  Self-organization in communication networks: principles and design paradigms , 2005, IEEE Communications Magazine.

[15]  Jeffrey G. Andrews,et al.  Seven ways that HetNets are a cellular paradigm shift , 2013, IEEE Communications Magazine.

[16]  Walid Saad,et al.  Game Theory for Networks: A tutorial on game-theoretic tools for emerging signal processing applications , 2016, IEEE Signal Processing Magazine.

[17]  Yueming Cai,et al.  Optimal Power Allocation and User Scheduling in Multicell Networks: Base Station Cooperation Using a Game-Theoretic Approach , 2014, IEEE Transactions on Wireless Communications.