REVIEW 1 cited by
The impact of mobility, beam sweeping and smart jammers on security vulnerabilities of 5G cells
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The vulnerability of 5G networks to jamming attacks has emerged as a significant concern. This paper contributes in two primary aspects. Firstly, it investigates the effect of a multi-jammer on 5G cell metrics, specifically throughput and goodput. The investigation is conducted within the context of a mobility model for user equipment (UE), with a focus on scenarios involving connected vehicles (CVs) engaged in a mission. Secondly, the vulnerability of synchronization signal block (SSB) components is examined concerning jamming power and beam sweeping. Notably, the study reveals that increasing jamming power beyond 40 dBm in our specific scenario configuration no longer decreases network throughput due to the re-transmission of packets through the hybrid automatic repeat request (HARQ) process. Furthermore, it is observed that under the same jamming power, the physical downlink shared channel (PDSCH) is more vulnerable than the primary synchronization signal (PSS) and secondary synchronization signal (SSS). However, a smart jammer can disrupt the cell search process by injecting less power and targeting PSS-SSS or physical broadcast channel (PBCH) data compared to a barrage jammer. On the other hand, beam sweeping proves effective in mitigating the impact of a smart jammer, reducing the error vector magnitude root mean square from 51.59% to 23.36% under the same jamming power.
Forward citations
Cited by 1 Pith paper
-
Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine
A Tsetlin-machine classifier on 5G SSB features achieves 91.5% jamming-detection accuracy with much faster training and a 14x smaller memory footprint than a CNN, at the cost of a 5-point accuracy gap and slower CPU i...
Discussion (0). Sign in to comment.