960 research outputs found
Analog network coding in general SNR regime: Performance of a greedy scheme
The problem of maximum rate achievable with analog network coding for a
unicast communication over a layered relay network with directed links is
considered. A relay node performing analog network coding scales and forwards
the signals received at its input. Recently this problem has been considered
under certain assumptions on per node scaling factor and received SNR.
Previously, we established a result that allows us to characterize the optimal
performance of analog network coding in network scenarios beyond those that can
be analyzed using the approaches based on such assumptions.
The key contribution of this work is a scheme to greedily compute a lower
bound to the optimal rate achievable with analog network coding in the general
layered networks. This scheme allows for exact computation of the optimal
achievable rates in a wider class of layered networks than those that can be
addressed using existing approaches. For the specific case of Gaussian N-relay
diamond network, to the best of our knowledge, the proposed scheme provides the
first exact characterization of the optimal rate achievable with analog network
coding. Further, for general layered networks, our scheme allows us to compute
optimal rates within a constant gap from the cut-set upper bound asymptotically
in the source power.Comment: 11 pages, 5 figures. Fixed an issue with the notation in the
statement and proof of Lemma 1. arXiv admin note: substantial text overlap
with arXiv:1204.2150 and arXiv:1202.037
Network Utility Maximization under Maximum Delay Constraints and Throughput Requirements
We consider the problem of maximizing aggregate user utilities over a
multi-hop network, subject to link capacity constraints, maximum end-to-end
delay constraints, and user throughput requirements. A user's utility is a
concave function of the achieved throughput or the experienced maximum delay.
The problem is important for supporting real-time multimedia traffic, and is
uniquely challenging due to the need of simultaneously considering maximum
delay constraints and throughput requirements. We first show that it is
NP-complete either (i) to construct a feasible solution strictly meeting all
constraints, or (ii) to obtain an optimal solution after we relax maximum delay
constraints or throughput requirements up to constant ratios. We then develop a
polynomial-time approximation algorithm named PASS. The design of PASS
leverages a novel understanding between non-convex maximum-delay-aware problems
and their convex average-delay-aware counterparts, which can be of independent
interest and suggest a new avenue for solving maximum-delay-aware network
optimization problems. Under realistic conditions, PASS achieves constant or
problem-dependent approximation ratios, at the cost of violating maximum delay
constraints or throughput requirements by up to constant or problem-dependent
ratios. PASS is practically useful since the conditions for PASS are satisfied
in many popular application scenarios. We empirically evaluate PASS using
extensive simulations of supporting video-conferencing traffic across Amazon
EC2 datacenters. Compared to existing algorithms and a conceivable baseline,
PASS obtains up to improvement of utilities, by meeting the throughput
requirements but relaxing the maximum delay constraints that are acceptable for
practical video conferencing applications
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