Band Assignment in Ultra-Narrowband (UNB) Systems for Massive IoT Access

Enes Krijestorac, Ghaith Hattab, Petar Popovski, Danijela Cabric

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1 Citationer (Scopus)

Abstract

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing bands and transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. The hardware constraint is mainly due to the complexity of performing fast Fourier transform (FFT) at a very small sampling interval over the multiplexing bands in order to counter the uncertainty of IoT device frequency and synchronize onto transmissions. The objective is to find an assignment of BSs to multiplexing bands in order to maximize the packet decoding probability (PDP). We develop a learning-based algorithm based on a sub-optimal solution to PDP maximization. The simulation results show that our approach to band assignment achieves near-optimal performance in terms of PDP, while at the same time, significantly exceeding the performance of random assignment. We also develop a heuristic algorithm with no learning overhead based on the locations of the BSs that also outperforms random assignment and serves as a performance reference to our learning-based algorithm.

OriginalsprogEngelsk
TitelGLOBECOM 2020 - 2020 IEEE Global Communications Conference
Antal sider6
ForlagIEEE
Publikationsdato2020
Artikelnummer9322262
ISBN (Trykt)978-1-7281-8299-5
ISBN (Elektronisk)978-1-7281-8298-8
DOI
StatusUdgivet - 2020
BegivenhedGLOBECOM 2020 - 2020 IEEE Global Communications Conference - Taipei, Taiwan
Varighed: 7 dec. 202011 dec. 2020

Konference

KonferenceGLOBECOM 2020 - 2020 IEEE Global Communications Conference
Land/OmrådeTaiwan
ByTaipei
Periode07/12/202011/12/2020
NavnIEEE Global Communications Conference (GLOBECOM)
ISSN1930-529X

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