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
Designing scheduling algorithms that work in synergy with TCP is a challenging problem in wireless networks. Extensive research on scheduling algorithms has focused on inelastic traffic, where there is no correlation between traffic dynamics and scheduling decisions. In this work, we study the performance of several scheduling algorithms in LTE networks, where the scheduling decisions are intertwined with wireless channel fluctuations to improve the system throughput. We use ns-3 simulations to study the performance of several scheduling algorithms with a specific focus on Max Weight (MW) schedulers with both UDP and TCP traffic, while considering the detailed behavior of OFDMA-based resource allocation in LTE networks. We show that, contrary to its performance with inelastic traffic, MW schedulers may not perform well in LTE networks in the presence of TCP traffic, as they are agnostic to the TCP congestion control mechanism. We then design a new scheduler called “Queue MW” (Q-MW) which is tailored specifically to TCP dynamics by giving higher priority to TCP flows whose queue at the base station is very small in order to encourage them to send more data at a faster rate. We have implemented Q-MW in ns-3 and studied its performance in a wide range of network scenarios in terms of queue size at the base station and round-trip delay. Our simulation results show that Q-MW achieves peak and average throughput gains of 37% and 10% compared to MW schedulers if tuned properly.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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