Short-Packet Communications in Wireless-Powered Cognitive IoT Networks: Performance Analysis and Deep Learning Evaluation
- Authors
- Chung Duc Ho; Toan-Van Nguyen; Thien Huynh-The; Tien-Tung Nguyen; da Costa, Daniel Benevides; An, Beongku
- Issue Date
- Mar-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Relays; Internet of Things; Reliability; Cognitive radio; Signal to noise ratio; Receivers; Manufacturing automation; Deep learning; energy harvesting; relay selection; short-packet communications
- Citation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.70, no.3, pp 2894 - 2899
- Pages
- 6
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Volume
- 70
- Number
- 3
- Start Page
- 2894
- End Page
- 2899
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28288
- DOI
- 10.1109/TVT.2021.3061157
- ISSN
- 0018-9545
1939-9359
- Abstract
- In this paper, we study short-packet communications in wireless-powered cognitive Internet-of-Things (IoT) networks with multiple primary receivers (PRs). The considered system can be applied for small factory automations, where a source and multiple relays harvest energy from a multi-antenna dedicated power beacon (PB) to send short packets to a robot destination for controlling purposes under cognitive radio constraint imposed by PRs. We propose an opportunistic relay selection (ORS) scheme to maximize the end-to-end signal-to-noise ratio in cognitive IoT systems. Closed-form expressions for the average block error rate (BLER) of the proposed system are obtained, based on which the performance floor analysis, goodput, and energy efficiency (EE) are also carried out. Relying on analytical results, we develop a deep learning framework for the BLER prediction with high accuracy and short execution time. Simulation results show the BLER, goodput, and EE improvements of the ORS scheme over conventional relay selection schemes. Moreover, the developed deep learning-based evaluation model achieves the equivalent performance as the ORS scheme in terms of BLER, goodput, and EE, while remarkably reducing the execution time in cognitive IoT systems.
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