90 research outputs found
Simplifying Wireless Social Caching
Social groups give the opportunity for a new form of caching. In this paper,
we investigate how a social group of users can jointly optimize bandwidth
usage, by each caching parts of the data demand, and then opportunistically
share these parts among themselves upon meeting. We formulate this problem as a
Linear Program (LP) with exponential complexity. Based on the optimal solution,
we propose a simple heuristic inspired by the bipartite set-cover problem that
operates in polynomial time. Furthermore, we prove a worst case gap between the
heuristic and the LP solutions. Finally, we assess the performance of our
algorithm using real-world mobility traces from the MIT Reality Mining project
dataset and two mobility traces that were synthesized using the SWIM model. Our
heuristic performs closely to the optimal in most cases, showing a better
performance with respect to alternative solutions.Comment: Parts of this work were accepted for publication in ISIT 2016. A
complete version is submitted to Transactions on Mobile Computin
The Approximate Optimality of Simple Schedules for Half-Duplex Multi-Relay Networks
In ISIT'12 Brahma, \"{O}zg\"{u}r and Fragouli conjectured that in a
half-duplex diamond relay network (a Gaussian noise network without a direct
source-destination link and with non-interfering relays) an approximately
optimal relay scheduling (achieving the cut-set upper bound to within a
constant gap uniformly over all channel gains) exists with at most active
states (only out of the possible relay listen-transmit
configurations have a strictly positive probability). Such relay scheduling
policies are said to be simple. In ITW'13 we conjectured that simple relay
policies are optimal for any half-duplex Gaussian multi-relay network, that is,
simple schedules are not a consequence of the diamond network's sparse
topology. In this paper we formally prove the conjecture beyond Gaussian
networks. In particular, for any memoryless half-duplex -relay network with
independent noises and for which independent inputs are approximately optimal
in the cut-set upper bound, an optimal schedule exists with at most
active states. The key step of our proof is to write the minimum of a
submodular function by means of its Lov\'{a}sz extension and use the greedy
algorithm for submodular polyhedra to highlight structural properties of the
optimal solution. This, together with the saddle-point property of min-max
problems and the existence of optimal basic feasible solutions in linear
programs, proves the claim.Comment: Submitted to IEEE Information Theory Workshop (ITW) 201
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
- β¦