Fundamental Limits of Coded Caching With Multiple Antennas, Shared Caches and Uncoded Prefetching

Parrinello, Emanuele; Ünsal, Ayse; Elia, Petros

doi:10.1109/tit.2019.2955384

Cited by 93 publications

(165 citation statements)

References 47 publications

Supporting

Mentioning

162

Contrasting

Order By: Relevance

“…The converse also encompasses all scenarios that involve a library of size n∈[N ] |W n | = N but where the files may be of different size. In the end, even though the designed optimal scheme will consider an individual cache size M and equal file sizes, the converse guarantees that 4 Here L is simply the vector of the cardinalities of U λ , ∀λ ∈ {1, . .…”

Section: A Shared-link Coded Caching With Shared Cachesmentioning

confidence: 99%

“…For example, L 1 = 6 states that the highest number of users served by a single cache, is 6. 5 An example of a user-to-cache assignment could have that users U 1 = (14, 15) are assigned to helper node 1, users U 2 = (1,2,3,4,5,6,7,8) are assigned to helper node 2, and users U 3 = (9, 10, 11, 12, 13) to helper node 3. This corresponds to a profile L = (8, 5, 2).…”

Section: A Shared-link Coded Caching With Shared Cachesmentioning

confidence: 99%

“…As a first step, we present the construction of the set D L . To this end, let us start by considering the demand d = (1,2,3,4,5,6,7,8,9) and one of the 6 permutations π ∈ S 3 ; for example, let us start by considering π(1) = 2, π(2) = 3, π(3) = 1. Toward reordering d to reflect L, we construct d 1 = (1, 2, 3, 4), d 2 = (5, 6, 7), d 3 = (8,9) to obtain the reordered demand vector…”

Section: A Proof Of the Converse For Corollarymentioning

confidence: 99%

“…The outer bound now involves adding the sizes of these chosen (underlined) nodes. For example, for the demand d(U) = ( (8,9), (1,2,3,4), (5,6,7)) (this corresponds to the lower center graph), the lower bound in (34) becomes…”

Section: A Proof Of the Converse For Corollarymentioning

confidence: 99%

“…The lower bounds for the remaining 5 vectors d(U) for the same d = (1,2,3,4,5,6,7,8,9), are given in a similar way, again by adding the (underlined) nodes of the corresponding acyclic subgraphs (again see Figure 3). Subsequently, the procedure is repeated for all P (N, K) = K!…”

Section: A Proof Of the Converse For Corollarymentioning

confidence: 99%

See 4 more Smart Citations

Optimal coded caching in heterogeneous networks with uncoded prefetching

Parrinello

Ünsal

Elia

2018

2018 IEEE Information Theory Workshop (ITW)

Self Cite

View full text Add to dashboard Cite

The work explores the fundamental limits of coded caching in heterogeneous networks where multiple (N0) senders/antennas, serve different users which are associated (linked) to shared caches, where each such cache helps an arbitrary number of users. Under the assumption of uncoded cache placement, the work derives the exact optimal worstcase delay and DoF, for a broad range of user-to-cache association profiles where each such profile describes how many users are helped by each cache. This is achieved by presenting an information-theoretic converse based on index coding that succinctly captures the impact of the user-to-cache association, as well as by presenting a coded caching scheme that optimally adapts to the association profile by exploiting the benefits of encoding across users that share the same cache.The work reveals a powerful interplay between shared caches and multiple senders/antennas, where we can now draw the striking conclusion that, as long as each cache serves at least N0 users, adding a single degree of cache-redundancy can yield a DoF increase equal to N0, while at the same time -irrespective of the profile -going from 1 to N0 antennas reduces the delivery time by a factor of N0. Finally some conclusions are also drawn for the related problem of coded caching with multiple file requests.

show abstract

Section: A Shared-link Coded Caching With Shared Cachesmentioning

confidence: 99%