A centralized TDMA-based scheme for fair bandwidth allocation in wireless IP networks
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Bibliographic record
Abstract
A unified time-division multiple access (TDMA) based centralized wireless access scheme is proposed for performing the statistical multiplexing of bursty data sources in a wireless packet data network. This scheme combines dynamic bandwidth allocation with admission control and packet conditioning (at the mobile stations) to provide fair bandwidth distribution among bursty data flows with different profile rates (or subscription levels) in an error-prone environment. The dynamic bandwidth allocation policy is credit-based and both the burst-level and the packet-level bandwidth allocations are considered. The performance of the scheme is evaluated using computer simulations for different total subscription levels, for different compositions of flows with different profile rates, and for different channel quality with different channel-error correlation patterns. The simulation results show that the throughput variability among flows with the same level of subscription is considerably small except for long range dependent flows with very high traffic burstiness. The relative throughput fairness among flows with different profile rates can also be achieved. The post facto loss and delay values (i.e., observed average packet delay and average packet loss values) for the flows depend on the corresponding delay tolerance limits of the data bursts, TDMA frame-length, and the wireless link utilization level. The energy efficiency of the wireless access scheme is evaluated in terms of the average transmitter usage time and the average receiver usage time in the mobile stations for both the burst-level and the packet-level bandwidth allocation. The proposed scheme can be used in an adaptive quality-of-service (QoS) framework for dynamically adjusting the QoS for flows in order to accommodate wireless channel errors and user mobility.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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