End-to-End Fair Bandwidth Allocation in Multi-Hop Wireless Ad Hoc 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
The shared-medium multi-hop nature of wireless ad hoc networks poses fundamental challenges to the design of an effective resource allocation algorithm to maximize spatial reuse of spectrum, whilemaintaining basic fairness among multiple flows. When previously proposed scheduling algorithms have been shown to perform well in providing fair shares of bandwidth among single-hop wireless flows, they do not consider multi-hop flows with an end-to-end perspective when maximizing spatial reuse of spectrum. Instead, previous work attempts to break each multi-hop end-to-end flow into multiple single-hop flows for scheduling purposes. While this may be sufficient for maintaining basic fairness properties among single-hop subflows with respect to bandwidth, we show that, due to the intra-flow correlation between upstream and downstream hops, it may not be appropriate for maximizing spatial reuse of bandwidth. In this paper, we analyze the issue of increasing such spatial reuse of bandwidth from an end-to-end perspective of multihop flows. Through analysis and simulation results, we show that our proposed algorithm is able to appropriately distribute resources amongmulti-hop flows, so that end-to-end throughput may be maximized in wireless ad hoc networks, while still maintaining basic fairness across the multi-hop flows.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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