A Distributed Transmission Scheduling Algorithm for Wireless Networks Based on the Ising Model

Li, Xi; Tolmachev, Pavel; Pauley, Michael; Manton, Jonathan H.

doi:10.1109/ssp.2018.8450715

Cited by 10 publications

(12 citation statements)

References 11 publications

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“…After manually setting the optimal power-levels, the authors show that the problem is equivalent to a maximum weight clique problem that can be efficiently solved using their proposed heuristic. Likewise, reference [188] uses the clique formulation to design a distributed transmission scheduling algorithm for wireless networks using the Ising model.…”

Section: Applications Of the Maximum Weight Clique Problem In Commmentioning

confidence: 99%

A Tutorial on Clique Problems in Communications and Signal Processing

et al. 2020

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Since its first use by Euler on the seven bridges of Königsberg problem, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems notably in communications and signal processing. The study of the structure of integer programs reveals equivalence with graph theory problems, which makes 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 problems that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial formulates and suggests various optimal and heuristic solutions for the clique problem and its variants. The tutorial, further, illustrates the use of the clique formulation and its variants through numerous contemporary examples in communications and signal processing, mainly in maximum access problem for non-orthogonal multiple access networks, throughput maximization using index coding and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. The tutorial finally sheds light on the recent advances of such applications and discusses future research directions on the topics.

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Section: Applications Of the Maximum Weight Clique Problem In Commmentioning

confidence: 99%

A Tutorial on Clique Problems in Communications and Signal Processing

et al. 2020

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“…the probability that the link transmits in a given time slot, in a distributed manner. Another such work can be found in [29] uses the Ising formulations in [30] to introduce a simple energy function, which is optimized using simulated annealing [31] to obtain a feasible schedule for transmission. However, the authors do not explicitly account for the queue lengths in the network and they only study the volume of data transmission and information delay without taking any fairness measures into consideration.…”

Section: A Related Workmentioning

confidence: 99%

“…It is easy to implement in a distributed manner, requiring only local communication and channel sensing capabilities for the links in the network. It also requires much lesser local information exchange between links compared to the annealing process in [29], making it practical for implementation in ad hoc networks. Our simulations show that this framework performs better than commonly used algorithms over a variety of network topologies and traffic arrival patterns.…”

Section: B Contributionsmentioning

confidence: 99%

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A Novel Medium Access Control Algorithm for Ad Hoc Networks Based on Ising Model

2020

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Medium Access Control (MAC) scheduling in ad hoc networks is a challenging task due to the trade-offs between fairness, delay and throughput. In this work, we propose a distributed link scheduling algorithm based on the Ising Model from statistical mechanics, by associating a novel Hamiltonian measure with the network such that its optimization yields a feasible schedule. This work overcomes the shortcomings of previous Ising Model based algorithms by incorporating queuing and servicing dynamics as well as fairness measures for improving aggregate throughput and network latency. Our simulations show considerable improvement in performance compared to existing benchmarks over a variety of traffic arrival patterns and network topologies.

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“…CSMA-CA) due to its increased spectrum utilization efficiency [5]. This benefit, however, comes with an associated challenge, namely that optimal scheduling involves solving a maximum weighted independent set (MWIS) problem [3,4,[6][7][8][9][10][11][12][13][14], which is NP-hard [14]. In this context, researchers and practitioners have resorted to efficient approximation heuristics, such as message passing [9][10][11][12][13] and greedy solvers [3,7,8].…”

Section: Introductionmentioning

confidence: 99%

Distributed Scheduling Using Graph Neural Networks

Zhao

Verma²,

Rao³

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A fundamental problem in the design of wireless networks is to efficiently schedule transmission in a distributed manner. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is NP-hard. For practical link scheduling schemes, distributed greedy approaches are commonly used to approximate the solution of the MWIS problem. However, these greedy schemes mostly ignore important topological information of the wireless networks. To overcome this limitation, we propose a distributed MWIS solver based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module learns topology-aware node embeddings that are combined with the network weights before calling a greedy solver. In small-to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.

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A Distributed Transmission Scheduling Algorithm for Wireless Networks Based on the Ising Model

Cited by 10 publications

References 11 publications

A Tutorial on Clique Problems in Communications and Signal Processing

A Tutorial on Clique Problems in Communications and Signal Processing

A Novel Medium Access Control Algorithm for Ad Hoc Networks Based on Ising Model

Distributed Scheduling Using Graph Neural Networks

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