Wireless hop-by-hop credit-based flow control extended to source for stable best effort traffic
Bibliographic record
Abstract
Data traffic is expected to grow faster than capacity in future wireless networks. Therefore it will become unavoidable to deal with congestion. Bottlenecks are located on the wireless links because back-haul and Internet are overprovisioned. Traffic routed towards the user terminal (UT) in down-link direction keeps coming in through a big pipe until it reaches the base station (BS). The following wireless links can only carry a limited data rate due to congestion. In a multi-hop situation buffers before the bottlenecks ramp up and become unstable, leading to packet loss. While real-time traffic is safe due to call admission control (CAC), highest static priority and over-provisioning, best effort data traffic experiences congestion and therefore packet losses. A wireless flow control based on a credit-based hop-by-hop concept can solve this problem by avoiding any buffer overflow completely. This paper proposes extending the closed flow control loops to the source, either by a genuine credit-based flow control or by TCP rate control with deep packet inspection and ACK modification. This paper analyses the queueing behavior with stochastic Petri nets models. Markov state analysis provides numeric performance results. The example scenario consists of two wireless relayed hops and a wired back-haul with different control approaches for the hop between source and bottleneck.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".