Channel Dependent Interference and Decentralized Colouring

Clifford, Peter; Leith, Douglas J.

doi:10.1007/978-3-540-72709-5_10

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…Several solutions, based mainly on graph theory, have been proposed trying to consider these constraints on communication. This solution requires decentralised algorithms for channel selection, see [16], [17], [18].…”

Section: B Channel Selection Algorithmsmentioning

confidence: 99%

On the Interactions Between Multiple Overlapping WLANs Using Channel Bonding

Bellalta

Checco

Zocca

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-Next-generation WLANs will support the use of wider channels, which is known as channel bonding, to achieve higher throughput. However, because both the channel center frequency and the channel width are autonomously selected by each WLAN, the use of wider channels may also increase the competition with other WLANs operating in the same area for the available channel resources. In this paper, we analyse the interactions between a group of neighboring WLANs that use channel bonding and evaluate the impact of those interactions on the achievable throughput. A Continuous Time Markov Network (CTMN) model that is able to capture the coupled dynamics of a group of overlapping WLANs is introduced and validated. The results show that the use of channel bonding can provide significant performance gains even in scenarios with a high density of WLANs, though it may also cause unfair situations in which some WLANs receive most of the transmission opportunities while others starve.

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Section: B Channel Selection Algorithmsmentioning

confidence: 99%

On the Interactions Between Multiple Overlapping WLANs Using Channel Bonding

Bellalta

Checco

Zocca

et al. 2016

IEEE Trans. Veh. Technol.

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“…This work builds upon the works of the early work of Clifford and Leith [16], then refined and extended by Leith et al [17], Duffy et al [1]; they present a fully decentralised CFL, proven to solve a large class of problems that include graph coloring, but without sensing restrictions.…”

Section: Related Workmentioning

confidence: 99%

“…Consider the stochastic learning algorithm, introduced by Clifford and Leith [16], described in Algorithm 1 with the only difference here of envisaging sensing restrictions. An instance of this algorithm is run in parallel for every variable.…”

Section: Solving Coloring Problems With Sensing Restrictions 51 Algomentioning

confidence: 99%

Learning-Based Constraint Satisfaction With Sensing Restrictions

Checco

Leith

2013

IEEE J. Sel. Top. Signal Process.

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In this paper we consider graph-coloring problems, an important subset of general constraint satisfaction problems that arise in wireless resource allocation. We constructively establish the existence of fully decentralized learning-based algorithms that are able to find a proper coloring even in the presence of strong sensing restrictions, in particular sensing asymmetry of the type encountered when hidden terminals are present. Our main analytic contribution is to establish sufficient conditions on the sensing behaviour to ensure that the solvers find satisfying assignments with probability one. These conditions take the form of connectivity requirements on the induced sensing graph. These requirements are mild, and we demonstrate that they are commonly satisfied in wireless allocation tasks. We argue that our results are of considerable practical importance in view of the prevalence of both communication and sensing restrictions in wireless resource allocation problems. The class of algorithms analysed here requires no message-passing whatsoever between wireless devices, and we show that they continue to perform well even when devices are only able to carry out constrained sensing of the surrounding radio environment

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“…To handle spectrum reusability, a buyer grouping step has been applied in many existing spectrum auction schemes, in which the buyer's interference conditions are modelled as conflict graph. Since channel characteristics are dependent on the frequency used, we can expect that the shape of the interference regions will be channel dependent [10], so we use channel‐dependent interference graphs. Of course, since the channels of each spectrum type show similar propagation and quality characteristics, we assume that these channels are homogeneous and have a same interference graph.…”

Section: Auction Designmentioning

confidence: 99%