Online Channel-state Clustering And Multiuser Capacity Learning For Wireless Scheduling

Tariq, Isfar; Sen, Rajat; Veciana, Gustavo de; Shakkottai, Sanjay

doi:10.1109/infocom.2019.8737425

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…In the process of research on traditional wireless sensor networks, the LEACH protocol stands out because of its energy efficiency and load balance. It was first proposed in the wireless sensor network and is also the one of the most classic clustering algorithms [16][17][18]. Many subsequent sensor network routing protocols are established on the basis of LEACH.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids

Yang

2021

Processes

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Aiming at the problem of uneven clustering and the unreasonable energy consumption of LEACH protocol in the perception layer of IoT-based microgrids of static nodes; in this paper, we propose a stationary-node energy-based routing protocol (SERP). First, we select a dynamic cluster radius for clustering to meet the actual needs of the network during clustering. Then, to solve the problem that the number of cluster heads is difficult to determine, a dynamic optimal cluster head ratio is adopted. The dynamic optimal cluster head ratio can be obtained by minimizing the total energy consumption of cluster formation and the stable transmission phase, which can improve the efficiency of network transmission. Finally, by setting the residual energy factor and distance factor to improve the calculation of the cluster head election threshold, the energy load of the network is more uniform, and the location of the cluster head is more reasonable. Compared with the LEACH protocol and the HEED protocol, the simulation results show that the SERP protocol can effectively prolong the lifetime of the whole network.

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Section: Introductionmentioning

confidence: 99%

Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids

Yang

2021

Processes

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“…Besides RF fingerprinting, some other aspects of wireless communication where machine learning is applicable are channel estimation and symbol detection [4], frequency estimation [2], spectrum sensing (cognitive radio) [8], resource allocation using multi-armed bandit [10], energy modelling [1], and end-to-end communication systems [5].…”

Section: Introductionmentioning

confidence: 99%

End-to-End Radio Fingerprinting with Neural Networks

Dreifuerst,

Graff,

Kumar

et al. 2020

Preprint

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This paper presents a novel method for classifying radio frequency (RF) devices from their transmission signals. Given a collection of signals from identical devices, we accurately classify both the distance of the transmission and the specific device identity. We develop three models, each building off the previous, that lead to a multiple classifier system that accurately discriminates between channels and classifies devices using normalized in-phase and quadrature (IQ) samples from the data. The first two are a device-classifier and distanceclassifier, with the third combining the two for an end-to-end ensemble classifier. Our residual network (ResNet) based architecture reaches 88.33% accuracy classifying 16 unique devices over 11 different distances and two different runs, on a task that was previously unlearnable. Furthermore, we demonstrate the efficacy for pre-training neural networks that need to identify subtle features from massive data domains.

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Auto-Tuning for Cellular Scheduling Through Bandit-Learning and Low-Dimensional Clustering

Tariq

Sen

Novlan

et al. 2021

IEEE/ACM Trans. Networking

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Online Channel-state Clustering And Multiuser Capacity Learning For Wireless Scheduling

Cited by 5 publications

References 11 publications

Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids

Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids

End-to-End Radio Fingerprinting with Neural Networks

Auto-Tuning for Cellular Scheduling Through Bandit-Learning and Low-Dimensional Clustering

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