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An Enhanced Random Access with Preamble-Assisted Short-Packet Transmissions for Cellular IoT Communications
We propose an enhanced random access (RA) with preamble-assisted short-p...
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Performance Analysis of 2-Step Random Access with CDMA in Machine-Type Communication
There is a growing interest in the transition from 4-step random access ...
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A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication
Jamming attacks target a wireless network creating an unwanted denial of...
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Detecting Compressed Cleartext Traffic from Consumer Internet of Things Devices
Data encryption is the primary method of protecting the privacy of consu...
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Short Packet Structure for Ultra-Reliable Machine-type Communication: Tradeoff between Detection and Decoding
Machine-type communication requires rethinking of the structure of short...
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Wireless Powered Asynchronous Backscatter Networks with Sporadic Short Packets: Performance Analysis and Optimization
In the fifth generation era, the pervasive applications of Internet of T...
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Transmitter Classification With Supervised Deep Learning
Hardware imperfections in RF transmitters introduce features that can be...
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Grant-Free Access: Machine Learning for Detection of Short Packets
In this paper, we explore the use of machine learning methods as an efficient alternative to correlation in performing packet detection. Targeting satellite-based massive machine type communications and internet of things scenarios, our focus is on a common channel shared among a large number of terminals via a fully asynchronous ALOHA protocol to attempt delivery of short data packets. In this setup, we test the performance of two algorithms, neural networks and random forest, which are shown to provide substantial improvements over traditional techniques. Excellent performance is demonstrated in terms of detection and false alarm probability also in the presence of collisions among user transmissions. The ability of machine learning to extract further information from incoming signals is also studied, discussing the possibility to classify detected preambles based on the level of interference they undergo.
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