Bibliographic record
Abstract
Compound TCP will play a central role in future home WiFi networks supporting Internet of Things (IoT) applications. Compound TCP was designed to be fair but can manifest throughput unfairness in infrastructure-based IEEE 802.11 networks when devices at different locations experience different wireless channel quality. In this paper, we develop a comprehensive analytical model for compound TCP over WiFi. Our model captures the flow and congestion control dynamics of multiple competing long-lived compound TCP connections as well as the medium access control layer dynamics (i.e., contention, collisions, and retransmissions) that arise from different signal-to-noise ratios (SNRs) perceived by the devices. Our model provides accurate estimates for TCP packet loss probabilities and steady-state throughputs for IoT devices with different SNRs. More importantly, we propose a simple adaptive control algorithm to achieve better fairness without compromising the aggregate throughput of the system. The proposed real-time algorithm monitors the access point queue, drives the system dynamics to the desired operating point which mitigates the adverse impacts of SNR differences, and accommodates the sporadically transmitting IoT sensors in the system.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".