IntSHU: A Security-Enabled Intelligent Soft Handover Approach for UAV-Aided 5G and Beyond
Bibliographic record
Abstract
Uncrewed aerial vehicles (UAVs) play a significant role in serving as gNodeBs (gNBs), or user devices (UDs). However, considering the high data rates and low latency required by fifth generation (5G) and beyond, it is essential to maintain a reliable connection at UAVs, particularly due to frequent handovers. To minimize handover failures, the hysteresis margin and time-to-trigger (TTT) should be dynamically adjusted. Furthermore, to prevent vulnerabilities to threats, UAV and gNB authentication and secure communication should be addressed during handovers. However, available literature uses a learning-based approach to handover management in UAV-aided 5G networks, thereby neglecting security, or offers a handover authentication mechanism, but lacks a novel handover strategy. To fill this gap in the literature, in this study, we design an Intelligent Soft Handover for UAV-enabled cellular networks (IntSHU), a generative adversarial network (GAN)-based scheme designed for intelligent soft handovers in UAV-enabled 5G and beyond. IntSHU uses adapted hysteresis margins and TTT values, dynamically generated through an$\epsilon $-greedy policy. In addition, we implement a lightweight, physically unclonable function (PUF)-based message encryption and authentication scheme via blockchain for streamlined access control during handovers. The results of our simulation indicate that IntSHU significantly enhances network performance by facilitating reliable soft handovers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".