Accepted Papers
Advanced Drone Attack Detection Using 5g Open Ran Platform

Mohamed Jacem Guezguez1 and Olfa Besbes2, 1Cogicom, Paris, France, 2University of Monastir, Monastir, Tunisia


The fifth generation (5G) network represents the latest evolution in mobile communication technology, offering several significant advancements over its predecessors, including 4G (LTE) and 3G. These advancements include faster speeds, lower latency, and a wealth of new capabilities. In parallel, unmanned aerial vehicles (UAVs), commonly referred to as drones, are gaining increasing popularity and becoming more ubiquitous. Integrating drones with 5G networks unlocks new possibilities and applications that harness the high-speed, low-latency, and extensive connectivity features of 5G technology. However, the misuse of drones can pose various risks and concerns, including issues related to privacy invasion and safety hazards. In response to these challenges, this research paper presents an innovative 5G Open RAN platform, featuring programmable software deployed on 5G gNodeBs, enabling the collection and monitoring of radio-sensitive events in relation to drone intrusion attacks. Additionally, a radio-based detection technique is proposed to identify threats and block unauthorized drones, thus safeguarding private infrastructures. To illustrate the effectiveness of this platform, a case study is included, demonstrating its capabilities in addressing drone intrusion attacks at an airport.


Mobile Network, Drone Attacks, 5G Networks, Beamforming, Network Slicing.

How Computationally Powerful Are Transformer Language Models?

Jesse Roberts, Computer Science Department, Vanderbilt University, Nashville, TN


In this article we prove that the general transformer model undergirding modern large language models (LLMs) is Turing complete under reasonable assumptions. This is the first work to directly address the Turing completeness of the underlying technology employed in GPT-x as past work has focused on the more expressive, full auto-encoder transformer architecture. From this theoretical analysis, we show that the sparsity/compressibility of the word embedding is an important consideration for Turing completeness to hold. From our results we categorize Transformers as variants of B machines studied by Hao Wang.


Large Language Models, Transformers, Decoder-Only, Machine Learning, Transformer Theory.

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