5 research outputs found
Iterative Merging Algorithm for Cooperative Data Exchange
We consider the problem of finding the minimum sum-rate strategy in
cooperative data exchange systems that do not allow packet-splitting (NPS-CDE).
In an NPS-CDE system, there are a number of geographically close cooperative
clients who send packets to help the others recover a packet set. A minimum
sum-rate strategy is the strategy that achieves universal recovery (the
situation when all the clients recover the whole packet set) with the the
minimal sum-rate (the total number of transmissions). We propose an iterative
merging (IM) algorithm that recursively merges client sets based on a lower
estimate of the minimum sum-rate and updates to the value of the minimum
sum-rate. We also show that a minimum sum-rate strategy can be learned by
allocating rates for the local recovery in each merged client set in the IM
algorithm. We run an experiment to show that the complexity of the IM algorithm
is lower than that of the existing deterministic algorithm when the number of
clients is lower than .Comment: 9 pages, 3 figure
A Practical Approach for Successive Omniscience
The system that we study in this paper contains a set of users that observe a
discrete memoryless multiple source and communicate via noise-free channels
with the aim of attaining omniscience, the state that all users recover the
entire multiple source. We adopt the concept of successive omniscience (SO),
i.e., letting the local omniscience in some user subset be attained before the
global omniscience in the entire system, and consider the problem of how to
efficiently attain omniscience in a successive manner. Based on the existing
results on SO, we propose a CompSetSO algorithm for determining a complimentary
set, a user subset in which the local omniscience can be attained first without
increasing the sum-rate, the total number of communications, for the global
omniscience. We also derive a sufficient condition for a user subset to be
complimentary so that running the CompSetSO algorithm only requires a lower
bound, instead of the exact value, of the minimum sum-rate for attaining global
omniscience. The CompSetSO algorithm returns a complimentary user subset in
polynomial time. We show by example how to recursively apply the CompSetSO
algorithm so that the global omniscience can be attained by multi-stages of SO