Achieving optimum throughput for LTE and WiFi coexistence
Bibliographic record
Abstract
In this paper, we consider how to achieve optimum throughput when LTE with listen before talk (LBT) shares the unlicensed spectrum with WiFi. Basically, we aim to address two problems: 1) How can WiFi access point (AP) and WiFi station (STN) configure the access parameters to maximize the throughput of their own networks when they have knowledge of the access parameter of LTE? 2) How to jointly tune the access parameters of both networks towards the maximum throughput of the whole network? It is found that even when the WiFi network optimally configures the access parameters, the throughput gain is marginal when LTE accesses the unlicensed spectrum with a fixed backoff window size, which necessitates a joint tuning of the access parameters of both parties. With joint tuning, nevertheless, the throughput of the whole network is maximized when only the LTE uses the unlicensed spectrum. To provide a fair coexistence to the WiFi network, the throughput optimization problem is considered under the constraint that the WiFi network share a given portion of the throughput of the whole network. Explicit expressions of optimal access parameters are obtained for both LTE and WiFi networks, which provide direct guidance for harmonious coexistence of the two systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".