The Impact of Mobility, Beam Sweeping and Smart Jammers on Security Vulnerabilities of 5G Cells
Bibliographic record
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 blackin 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".