Paper Info
Title
Short-Packet Communications in Multi-Hop WPINs: Performance Analysis and Deep Learning Design
Abstract
In this paper, we study short-packet communications (SPCs) in multi-hop wireless-powered Internet-of-Things networks (WPINs), where IoT devices transmit short packets to multiple destination nodes by harvesting energy from multiple power beacons. To improve system block error rate (BLER) and throughput, we propose a best relay-best user (bR-bU) selection scheme with an accumulated energy harvesting mechanism. Closed-form expressions for the BLER and throughput of the proposed scheme over Rayleigh fading channels are derived and the respective asymptotic analysis is also carried out. To support real-time settings, we design a deep neural network (DNN) framework to predict the system throughput under different channel settings. Numerical results demonstrate that the proposed bR-bU selection scheme outperforms several baseline ones in terms of the BLER and throughput, showing to be an efficient strategy for multi-hop SPCs. The resulting DNN can estimate accurately the throughput with low execution time. The effects of message size on reliability and latency are also evaluated and discussed.
Year
DOI
Venue
2021
10.1109/GLOBECOM46510.2021.9685765
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Keywords
DocType
ISSN
Block error rate, deep neural network, energy harvesting, multi-hop IoT networks, short-packet communication, relay selection, ultra-reliable low-latency communications
Conference
2334-0983
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Nguyen Van Toan13112.14
Van-Dinh Nguyen217923.75
Daniel Benevides da Costa310613.25
Beongku An418725.19