Utility Optimal Scheduling for Coded Caching in General Topologies

We consider coded caching over the fading broadcast channel, where the users, equipped with a memory of finite size, experience asymmetric fading statistics. It is known that a naive application of coded caching over the channel at hand performs poorly especially in the regime of a large number of users due to a vanishing multicast rate. We overcome this detrimental effect by a careful design of opportunistic scheduling policies such that some utility function of the long-term average rates should be maximized while balancing fairness among users. In particular, we propose a threshold-based scheduling that requires only statistical channel state information and one-bit feedback from each user. More specifically, each user indicates via feedback whenever its SNR is above a threshold determined solely by the fading statistics and the fairness requirement. Surprisingly, we prove that this simple scheme achieves the optimal utility in the regime of a large number of users.

[1]  A. Goldsmith,et al.  Capacity of Rayleigh fading channels under different adaptive transmission and diversity-combining techniques , 1999, IEEE Transactions on Vehicular Technology.

[2]  Alexander L. Stolyar,et al.  On the Asymptotic Optimality of the Gradient Scheduling Algorithm for Multiuser Throughput Allocation , 2005, Oper. Res..

[3]  Zhi-Quan Luo,et al.  Capacity Limits of Multiple Antenna Multicast , 2006, 2006 IEEE International Symposium on Information Theory.

[4]  Jean C. Walrand,et al.  Fair end-to-end window-based congestion control , 2000, TNET.

[5]  Feng Yang,et al.  The Performance Analysis of Coded Cache in Wireless Fading Channel , 2015, ArXiv.

[6]  Urs Niesen,et al.  Decentralized coded caching attains order-optimal memory-rate tradeoff , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[7]  Giuseppe Caire,et al.  Wireless caching: technical misconceptions and business barriers , 2016, IEEE Communications Magazine.

[8]  Jaime Llorca,et al.  Speeding Up Future Video Distribution via Channel-Aware Caching-Aided Coded Multicast , 2016, IEEE Journal on Selected Areas in Communications.

[9]  Giuseppe Caire,et al.  Physical-Layer Schemes for Wireless Coded Caching , 2017, IEEE Transactions on Information Theory.

[10]  Mari Kobayashi,et al.  Alpha fair coded caching , 2017, 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

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

[12]  Seyed Pooya Shariatpanahi,et al.  Multi-Server Coded Caching , 2015, IEEE Transactions on Information Theory.

[13]  Giuseppe Caire,et al.  Multi-antenna coded caching , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[14]  Sheng Yang,et al.  Cache-aided content delivery in MIMO channels , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[15]  Urs Niesen,et al.  Fundamental limits of caching , 2012, 2013 IEEE International Symposium on Information Theory.

[16]  Michèle Wigger,et al.  Noisy Broadcast Networks With Receiver Caching , 2016, IEEE Transactions on Information Theory.

[17]  Sheng Yang,et al.  Opportunistic Content Delivery in Fading Broadcast Channels , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[18]  Sheng Yang,et al.  Scalable Content Delivery With Coded Caching in Multi-Antenna Fading Channels , 2017, IEEE Transactions on Wireless Communications.

[19]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[20]  Jingjing Zhang,et al.  Wireless coded caching: A topological perspective , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).