Delay-Guaranteed Path Selection and Scheduling in IAB Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integrated access and backhaul (IAB) is a promising solution to improve coverage at low deployment costs. In IAB networks, due to wireless channel variations, guaranteeing delay for delay-sensitive applications is a major challenge. Given the random traffic arrivals and channel variations, using a central controller for packet-level delay management becomes infeasible due to the added delay from the central controller. In this paper, we propose a distributed cross-layer method to provide delay-guaranteed path selection and scheduling for the IAB network where priority queues and weighted round robin are adopted to deliver differentiated services. Our goal is to determine the optimal path and scheduling decisions to maximize the total utility of the IAB network. We deploy an iterative approach in a distributed manner to solve the maximization problem at each IAB-node. Through simulation, we show that our proposed solution guarantees the delay at the packet-level while achieving considerable gains in terms of delay and packet delivery ratio compared to the state-of-the-art.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it