Resolving the Feedback Bottleneck of Multi-Antenna Coded Caching

Multi-antenna cache-aided wireless networks have been known to suffer from a severe feedback bottleneck, where achieving the maximal Degrees-of-Freedom (DoF) performance required feedback from all served users. These costs matched the caching gains and thus scaled with the number of users. In the context of the $L$-antenna MISO broadcast channel with $K$ receivers having normalized cache size $\gamma$, we pair a fundamentally novel algorithm together with a new information-theoretic converse, and identify the optimal tradeoff between feedback costs and DoF performance, by showing that having CSIT from only $C<L$ served users implies an optimal one-shot linear DoF of $C+K\gamma$. As a side consequence of this, we also now understand that the well known DoF performance $L+K\gamma$ is in fact exactly optimal. In practice, the above means that we are now able to disentangle caching gains from feedback costs, thus achieving unbounded caching gains at the mere feedback cost of the multiplexing gain. This further solidifies the role of caching in boosting multi-antenna systems; caching now can provide unbounded DoF gains over multi-antenna downlink systems, at no additional feedback costs. The above results are extended to also include the corresponding multiple transmitter scenario with caches at both ends.Comment: 16 pages, partially presented in ISIT 2018, submitted on Transactions on Information Theor

Optimization of Heterogeneous Coded Caching

This paper aims to provide an optimization framework for coded caching that accounts for various heterogeneous aspects of practical systems. An optimization theoretic perspective on the seminal work on the fundamental limits of caching by Maddah Ali and Niesen is first developed, whereas it is proved that the coded caching scheme presented in that work is the optimal scheme among a large, non-trivial family of possible caching schemes. The optimization framework is then used to develop a coded caching scheme capable of handling simultaneous non-uniform file length, non-uniform file popularity, and non-uniform user cache size. Although the resulting full optimization problem scales exponentially with the problem size, this paper shows that tractable simplifications of the problem that scale as a polynomial function of the problem size can still perform well compared to the original problem. By considering these heterogeneities both individually and in conjunction with one another, insights into their interactions and influence on optimal cache content are obtained.Comment: To be submitted to IEEE Transactions on Information Theory. 26 pages, 6 figure