2,708 research outputs found
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Robust And Optimal Opportunistic Scheduling For Downlink 2-Flow Network Coding With Varying Channel Quality and Rate Adaptation
This paper considers the downlink traffic from a base station to two
different clients. When assuming infinite backlog, it is known that
inter-session network coding (INC) can significantly increase the throughput of
each flow. However, the corresponding scheduling solution (when assuming
dynamic arrivals instead and requiring bounded delay) is still nascent.
For the 2-flow downlink scenario, we propose the first opportunistic INC +
scheduling solution that is provably optimal for time-varying channels, i.e.,
the corresponding stability region matches the optimal Shannon capacity.
Specifically, we first introduce a new binary INC operation, which is
distinctly different from the traditional wisdom of XORing two overheard
packets. We then develop a queue-length-based scheduling scheme, which, with
the help of the new INC operation, can robustly and optimally adapt to
time-varying channel quality. We then show that the proposed algorithm can be
easily extended for rate adaptation and it again robustly achieves the optimal
throughput. A byproduct of our results is a scheduling scheme for stochastic
processing networks (SPNs) with random departure, which relaxes the assumption
of deterministic departure in the existing results. The new SPN scheduler could
thus further broaden the applications of SPN scheduling to other real-world
scenarios
Comparative Evaluation of Packet Classification Algorithms for Implementation on Resource Constrained Systems
This paper provides a comparative evaluation of a number of known classification algorithms that have been considered for both software and hardware implementation. Differently from other sources, the comparison has been carried out on implementations based on the same principles and design choices. Performance measurements are obtained by feeding the implemented classifiers with various traffic traces in the same test scenario. The comparison also takes into account implementation feasibility of the considered algorithms in resource constrained systems (e.g. embedded processors on special purpose network platforms). In particular, the comparison focuses on achieving a good compromise between performance, memory usage, flexibility and code portability to different target platforms
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