715 research outputs found
Coded Caching for a Large Number Of Users
Information theoretic analysis of a coded caching system is considered, in
which a server with a database of N equal-size files, each F bits long, serves
K users. Each user is assumed to have a local cache that can store M files,
i.e., capacity of MF bits. Proactive caching to user terminals is considered,
in which the caches are filled by the server in advance during the placement
phase, without knowing the user requests. Each user requests a single file, and
all the requests are satisfied simultaneously through a shared error-free link
during the delivery phase.
First, centralized coded caching is studied assuming both the number and the
identity of the active users in the delivery phase are known by the server
during the placement phase. A novel group-based centralized coded caching (GBC)
scheme is proposed for a cache capacity of M = N/K. It is shown that this
scheme achieves a smaller delivery rate than all the known schemes in the
literature. The improvement is then extended to a wider range of cache
capacities through memory-sharing between the proposed scheme and other known
schemes in the literature. Next, the proposed centralized coded caching idea is
exploited in the decentralized setting, in which the identities of the users
that participate in the delivery phase are assumed to be unknown during the
placement phase. It is shown that the proposed decentralized caching scheme
also achieves a delivery rate smaller than the state-of-the-art. Numerical
simulations are also presented to corroborate our theoretical results
Decentralized Coded Caching Attains Order-Optimal Memory-Rate Tradeoff
Replicating or caching popular content in memories distributed across the
network is a technique to reduce peak network loads. Conventionally, the main
performance gain of this caching was thought to result from making part of the
requested data available closer to end users. Instead, we recently showed that
a much more significant gain can be achieved by using caches to create
coded-multicasting opportunities, even for users with different demands,
through coding across data streams. These coded-multicasting opportunities are
enabled by careful content overlap at the various caches in the network,
created by a central coordinating server.
In many scenarios, such a central coordinating server may not be available,
raising the question if this multicasting gain can still be achieved in a more
decentralized setting. In this paper, we propose an efficient caching scheme,
in which the content placement is performed in a decentralized manner. In other
words, no coordination is required for the content placement. Despite this lack
of coordination, the proposed scheme is nevertheless able to create
coded-multicasting opportunities and achieves a rate close to the optimal
centralized scheme.Comment: To appear in IEEE/ACM Transactions on Networkin
Adaptive Delivery in Caching Networks
The problem of content delivery in caching networks is investigated for
scenarios where multiple users request identical files. Redundant user demands
are likely when the file popularity distribution is highly non-uniform or the
user demands are positively correlated. An adaptive method is proposed for the
delivery of redundant demands in caching networks. Based on the redundancy
pattern in the current demand vector, the proposed method decides between the
transmission of uncoded messages or the coded messages of [1] for delivery.
Moreover, a lower bound on the delivery rate of redundant requests is derived
based on a cutset bound argument. The performance of the adaptive method is
investigated through numerical examples of the delivery rate of several
specific demand vectors as well as the average delivery rate of a caching
network with correlated requests. The adaptive method is shown to considerably
reduce the gap between the non-adaptive delivery rate and the lower bound. In
some specific cases, using the adaptive method, this gap shrinks by almost 50%
for the average rate.Comment: 8 pages,8 figures. Submitted to IEEE transaction on Communications in
2015. A short version of this article was published as an IEEE Communications
Letter with DOI: 10.1109/LCOMM.2016.255814
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