1,171 research outputs found
Splitting Algorithms for Fast Relay Selection: Generalizations, Analysis, and a Unified View
Relay selection for cooperative communications promises significant
performance improvements, and is, therefore, attracting considerable attention.
While several criteria have been proposed for selecting one or more relays,
distributed mechanisms that perform the selection have received relatively less
attention. In this paper, we develop a novel, yet simple, asymptotic analysis
of a splitting-based multiple access selection algorithm to find the single
best relay. The analysis leads to simpler and alternate expressions for the
average number of slots required to find the best user. By introducing a new
`contention load' parameter, the analysis shows that the parameter settings
used in the existing literature can be improved upon. New and simple bounds are
also derived. Furthermore, we propose a new algorithm that addresses the
general problem of selecting the best relays, and analyze and
optimize it. Even for a large number of relays, the algorithm selects the best
two relays within 4.406 slots and the best three within 6.491 slots, on
average. We also propose a new and simple scheme for the practically relevant
case of discrete metrics. Altogether, our results develop a unifying
perspective about the general problem of distributed selection in cooperative
systems and several other multi-node systems.Comment: 20 pages, 7 figures, 1 table, Accepted for publication in IEEE
Transactions on Wireless Communication
Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity
In bipartite matching problems, vertices on one side of a bipartite graph are
paired with those on the other. In its online variant, one side of the graph is
available offline, while the vertices on the other side arrive online. When a
vertex arrives, an irrevocable and immediate decision should be made by the
algorithm; either match it to an available vertex or drop it. Examples of such
problems include matching workers to firms, advertisers to keywords, organs to
patients, and so on. Much of the literature focuses on maximizing the total
relevance---modeled via total weight---of the matching. However, in many
real-world problems, it is also important to consider contributions of
diversity: hiring a diverse pool of candidates, displaying a relevant but
diverse set of ads, and so on. In this paper, we propose the Online Submodular
Bipartite Matching (\osbm) problem, where the goal is to maximize a submodular
function over the set of matched edges. This objective is general enough to
capture the notion of both diversity (\emph{e.g.,} a weighted coverage
function) and relevance (\emph{e.g.,} the traditional linear function)---as
well as many other natural objective functions occurring in practice
(\emph{e.g.,} limited total budget in advertising settings). We propose novel
algorithms that have provable guarantees and are essentially optimal when
restricted to various special cases. We also run experiments on real-world and
synthetic datasets to validate our algorithms.Comment: To appear in AAAI 201
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