ArticleSensors (Basel, Switzerland)2021
A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-Enabled IoT.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure.Sensors (Basel, Switzerland) · 2024Article
- Towards efficient IoT communication for smart agriculture: A deep learning framework.PloS one · 2024Article
- Wireless Local Area Networks Threat Detection Using 1D-CNN.Sensors (Basel, Switzerland) · 2023Article
- Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas.International journal of environmental research and public health · 2021Article
- Neural Architecture Search for 1D CNNs-Different Approaches Tests and Measurements.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
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Abstract
The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.
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Registered trials
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