A Queue-Length Based Approach to Metropolized Hamiltonians for Distributed Scheduling in Wireless Networks

Rahman, Taha Ameen ur; Hassan, Mohamed S.; Ismail, Mahmoud H.

doi:10.1109/wts48268.2020.9198730

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…For practical scheduling in wireless ad-hoc networks, distributed MWIS solvers with low communication and computational complexity are usually preferred. Distributed MWIS solvers [6], [10], [19]- [21], [27], [60] construct a solution through an iterative procedure of a round of local exchanges between a vertex and its neighbors, followed by a phase of processing on each vertex. Thus, it is best to describe the complexity of distributed MWIS solvers with local (communication) complexity, defined as the number of rounds of local exchanges between each vertex and its neighbors.…”

Section: Related Workmentioning

confidence: 99%

“…In [20], a solution is constructed through O(V ) iterations of combining feasible local solutions at each vertex and exchanging the results with its neighbors. Compared with distributed solvers with linear local complexity O(V ) [6], [10], [19], [20], Ising-formulated MWIS solvers [21], [27], [60] require a fixed number of rounds (e.g. tens to hundreds) of local exchanges to emulate the cooling process of atoms with magnetic spin.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Link Scheduling using Graph Neural Networks

Zhao¹,

Verma²,

Rao³

et al. 2021

Preprint

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Efficient scheduling of transmissions is a key problem in wireless networks. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is known to be NP-hard. For practical link scheduling schemes, centralized and distributed greedy heuristics are commonly used to approximate the solution to the MWIS problem. However, these greedy schemes mostly ignore important topological information of the wireless network. To overcome this limitation, we propose fast heuristics based on graph convolutional networks (GCNs) that can be implemented in centralized and distributed manners. Our centralized MWIS solver is based on tree search guided by a trainable GCN module and 1-step rollout. In our distributed MWIS solver, a trainable GCN module learns topology-aware node embeddings that are combined with the network weights before calling a distributed greedy solver. Test results on mediumsized wireless networks show that a GCN-based centralized MWIS solver can reach a near-optimal solution quickly. Moreover, we demonstrate that a shallow GCN-based distributed MWIS scheduler can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity. The proposed scheduling solutions also exhibit good generalizability across graph and weight distributions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Link Scheduling using Graph Neural Networks

Zhao¹,

Verma²,

Rao³

et al. 2021

Preprint

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Privacy-Preserving Link Scheduling for Wireless Networks

Abbasalizadeh,

Chan,

Rayavaram

et al. 2024

IEEE Access

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Link Scheduling Using Graph Neural Networks

Zhao

Verma

Rao

et al. 2023

IEEE Trans. Wireless Commun.

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To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling overhead to the basic BP. Rather than relying on hopdistance, we introduce a new edge-weighted shortest path bias built on the scheduling duty cycle of wireless links, which can be predicted by a graph convolutional neural network based on the topology and traffic of wireless networks. Additionally, we tackle three long-standing challenges associated with SP-BP: optimal bias scaling, efficient bias maintenance, and integration of delay awareness. Our proposed solutions inherit the throughput optimality of the basic BP, as well as its practical advantages of low complexity and fully distributed implementation. Our approaches rely on common link features and introduces only a one-time constant overhead to previous SP-BP schemes, or a one-time overhead linear in the network size to the basic BP. Numerical experiments show that our solutions can effectively address the major drawbacks of slow startup, random walk, and the last packet problem in basic BP, improving the end-to-end delay of existing low-overhead BP algorithms under various settings of network traffic, interference, and mobility.

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A Queue-Length Based Approach to Metropolized Hamiltonians for Distributed Scheduling in Wireless Networks

Cited by 3 publications

References 18 publications

Link Scheduling using Graph Neural Networks

Link Scheduling using Graph Neural Networks

Privacy-Preserving Link Scheduling for Wireless Networks

Link Scheduling Using Graph Neural Networks

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