107,870 research outputs found
The Balanced Unicast and Multicast Capacity Regions of Large Wireless Networks
We consider the question of determining the scaling of the -dimensional
balanced unicast and the -dimensional balanced multicast capacity
regions of a wireless network with nodes placed uniformly at random in a
square region of area and communicating over Gaussian fading channels. We
identify this scaling of both the balanced unicast and multicast capacity
regions in terms of , out of total possible, cuts. These cuts
only depend on the geometry of the locations of the source nodes and their
destination nodes and the traffic demands between them, and thus can be readily
evaluated. Our results are constructive and provide optimal (in the scaling
sense) communication schemes.Comment: 37 pages, 7 figures, to appear in IEEE Transactions on Information
Theor
Broadcast Caching Networks with Two Receivers and Multiple Correlated Sources
The correlation among the content distributed across a cache-aided broadcast
network can be exploited to reduce the delivery load on the shared wireless
link. This paper considers a two-user three-file network with correlated
content, and studies its fundamental limits for the worst-case demand. A class
of achievable schemes based on a two-step source coding approach is proposed.
Library files are first compressed using Gray-Wyner source coding, and then
cached and delivered using a combination of correlation-unaware cache-aided
coded multicast schemes. The second step is interesting in its own right and
considers a multiple-request caching problem, whose solution requires coding in
the placement phase. A lower bound on the optimal peak rate-memory trade-off is
derived, which is used to evaluate the performance of the proposed scheme. It
is shown that for symmetric sources the two-step strategy achieves the lower
bound for large cache capacities, and it is within half of the joint entropy of
two of the sources conditioned on the third source for all other cache sizes.Comment: in Proceedings of Asilomar Conference on Signals, Systems and
Computers, Pacific Grove, California, November 201
A Stochastic Resource-Sharing Network for Electric Vehicle Charging
We consider a distribution grid used to charge electric vehicles such that
voltage drops stay bounded. We model this as a class of resource-sharing
networks, known as bandwidth-sharing networks in the communication network
literature. We focus on resource-sharing networks that are driven by a class of
greedy control rules that can be implemented in a decentralized fashion. For a
large number of such control rules, we can characterize the performance of the
system by a fluid approximation. This leads to a set of dynamic equations that
take into account the stochastic behavior of EVs. We show that the invariant
point of these equations is unique and can be computed by solving a specific
ACOPF problem, which admits an exact convex relaxation. We illustrate our
findings with a case study using the SCE 47-bus network and several special
cases that allow for explicit computations.Comment: 13 pages, 8 figure
Stackelberg Network Pricing Games
We study a multi-player one-round game termed Stackelberg Network Pricing
Game, in which a leader can set prices for a subset of priceable edges in a
graph. The other edges have a fixed cost. Based on the leader's decision one or
more followers optimize a polynomial-time solvable combinatorial minimization
problem and choose a minimum cost solution satisfying their requirements based
on the fixed costs and the leader's prices. The leader receives as revenue the
total amount of prices paid by the followers for priceable edges in their
solutions, and the problem is to find revenue maximizing prices. Our model
extends several known pricing problems, including single-minded and unit-demand
pricing, as well as Stackelberg pricing for certain follower problems like
shortest path or minimum spanning tree. Our first main result is a tight
analysis of a single-price algorithm for the single follower game, which
provides a -approximation for any . This can
be extended to provide a -approximation for the
general problem and followers. The latter result is essentially best
possible, as the problem is shown to be hard to approximate within
\mathcal{O(\log^\epsilon k + \log^\epsilon m). If followers have demands, the
single-price algorithm provides a -approximation, and the
problem is hard to approximate within \mathcal{O(m^\epsilon) for some
. Our second main result is a polynomial time algorithm for
revenue maximization in the special case of Stackelberg bipartite vertex cover,
which is based on non-trivial max-flow and LP-duality techniques. Our results
can be extended to provide constant-factor approximations for any constant
number of followers
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