67 research outputs found
Making recommendations bandwidth aware
This paper asks how much we can gain in terms of bandwidth and user
satisfaction, if recommender systems became bandwidth aware and took into
account not only the user preferences, but also the fact that they may need to
serve these users under bandwidth constraints, as is the case over wireless
networks. We formulate this as a new problem in the context of index coding: we
relax the index coding requirements to capture scenarios where each client has
preferences associated with messages. The client is satisfied to receive any
message she does not already have, with a satisfaction proportional to her
preference for that message. We consistently find, over a number of scenarios
we sample, that although the optimization problems are in general NP-hard,
significant bandwidth savings are possible even when restricted to polynomial
time algorithms
Dynamic Edge Caching with Popularity Drifting
Caching at the network edge devices such as wireless caching stations (WCS)
is a key technology in the 5G network. The spatial-temporal diversity of
content popularity requires different content to be cached in different WCSs
and periodically updated to adapt to temporal changes. In this paper, we study
how the popularity drifting speed affects the number of required broadcast
transmissions by the MBS and then design coded transmission schemes by
leveraging the broadcast advantage under the index coding framework. The key
idea is that files already cached in WCSs, which although may be currently
unpopular, can serve as side information to facilitate coded broadcast
transmission for cache updating. Our algorithm extends existing index
coding-based schemes from a single-request scenario to a multiple-request
scenario via a "dynamic coloring" approach. Simulation results indicate that a
significant bandwidth saving can be achieved by adopting our scheme
Privacy in Index Coding: Improved Bounds and Coding Schemes
It was recently observed in [1], that in index coding, learning the coding
matrix used by the server can pose privacy concerns: curious clients can
extract information about the requests and side information of other clients.
One approach to mitigate such concerns is the use of -limited-access schemes
[1], that restrict each client to learn only part of the index coding matrix,
and in particular, at most rows. These schemes transform a linear index
coding matrix of rank to an alternate one, such that each client needs to
learn at most of the coding matrix rows to decode its requested message.
This paper analyzes -limited-access schemes. First, a worst-case scenario,
where the total number of clients is is studied. For this case, a
novel construction of the coding matrix is provided and shown to be
order-optimal in the number of transmissions. Then, the case of a general
is considered and two different schemes are designed and analytically and
numerically assessed in their performance. It is shown that these schemes
perform better than the one designed for the case
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