Channel Scheduling for IoT Access with Spatial Correlation
Raghuwanshi, Prasoon; López, Onel Luis Alcaraz; Popovski, Petar; Latva-aho, Matti (2024-02-23)
IEEE
23.02.2024
P. Raghuwanshi, O. L. A. López, P. Popovski and M. Latva-Aho, "Channel Scheduling for IoT Access With Spatial Correlation," in IEEE Communications Letters, vol. 28, no. 5, pp. 1014-1018, May 2024, doi: 10.1109/LCOMM.2024.3369480.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403252414
https://urn.fi/URN:NBN:fi:oulu-202403252414
Tiivistelmä
Abstract
Spatially correlated device activation is a typical feature of the Internet of Things (IoT). This motivates the development of channel scheduling (CS) methods that mitigate device collisions efficiently in such scenarios, which constitutes the scope of this work. Specifically, we present a quadratic program (QP) formulation for the CS problem considering the joint activation probabilities among devices. This formulation allows the devices to stochastically select the transmit channels, thus, leading to a soft-clustering approach. We prove that the optimal QP solution can only be attained when it is transformed into a hard-clustering problem, leading to a pure integer QP, which we transform into a pure integer linear program (PILP). We leverage the branch-and-cut (B&C) algorithm to solve PILP optimally. Due to the high computational cost of B&C, we resort to a sub-optimal clustering method with low computational costs to tackle the clustering problem in CS. Our findings demonstrate that the CS strategy, sourced from B&C, significantly outperforms the one derived from the sub-optimal clustering method, even amidst increased device correlation.
Spatially correlated device activation is a typical feature of the Internet of Things (IoT). This motivates the development of channel scheduling (CS) methods that mitigate device collisions efficiently in such scenarios, which constitutes the scope of this work. Specifically, we present a quadratic program (QP) formulation for the CS problem considering the joint activation probabilities among devices. This formulation allows the devices to stochastically select the transmit channels, thus, leading to a soft-clustering approach. We prove that the optimal QP solution can only be attained when it is transformed into a hard-clustering problem, leading to a pure integer QP, which we transform into a pure integer linear program (PILP). We leverage the branch-and-cut (B&C) algorithm to solve PILP optimally. Due to the high computational cost of B&C, we resort to a sub-optimal clustering method with low computational costs to tackle the clustering problem in CS. Our findings demonstrate that the CS strategy, sourced from B&C, significantly outperforms the one derived from the sub-optimal clustering method, even amidst increased device correlation.
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