Multi-Armed Bandit-Aided Near-Optimal Over-The-Air Updates in Multi-Band V2X Systems
Bibliographic record
Abstract
As autonomous and connected vehicles continue to garner much research attention, the automotive Over-The-Air (OTA) updates recently emerged as an important research topic. OTA is crucial to disseminate critical updates for safety and stability of on-board sensing and operational systems. In beyond 5G(BSG) systems, OTA may be regarded as cached and a service provided by cellular base stations and roadside units (RSUs). However, for large-size OTA dissemination, the Electronic Control Units (ECUs) of vehicles need to download scheduled segments of the OTA payload from the serving RSU in an opportunistic manner, i.e., while stopping at the traffic signal or waiting in traffic. To maximize the downloadable payload per vehicle served by a RSU within a limited time window, we consider multi-band RSUs and ECUs as transmitting and receiving nodes, respectively. We consider legacy RF (radio frequency), mmWave (millimeter Wave), and visible light communication (VLC) bands at the RSU to provide large capacity links to the ECUs, respectively. However, the sub-channels of these frequency bands suffer from different blockage characteristics. We formulate this as a tradeoff problem in this paper in the presence of vehicular blockers, and propose a Thompson Sampling (TS)-based opportunistic band selection to alleviate the computational burden on both the communicating RSU and ECU nodes. Based on extensive computer-based simulations, we demonstrate the performance of our proposal in contrast with an optimal (centralized) baseline, as well as other comparable heuristic-based solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".