A power-controlled multiple access scheme for differentiated service and energy efficiency in mobile ad hoc networks and wireless LANs
Bibliographic record
Abstract
In this paper, we present multiple access with lag time (MALT) for medium access control (MAC) with strong quality-of-service (QoS) supports and throughput-efficient power control in mobile ad hoc networks. On lop of conventional priority-based techniques such as different interframe spaces and backoff algorithms, MAPS supports effective differentiated service employing the distributed differentiated scheduling (DDS) to allow packets with higher priority or urgent deadline to schedule for a packet slot that is farther into the future. In this way, these higher-priority packets can schedule for their transmissions without competition from lower priority packets since the latter are not allowed to make reservation during slots with conflicting schedules yet. We demonstrate through simulations that MALT is considerably stronger than IEEE 802.11e and a power-controlled dual-channel variant of IEEE 802.11e in terms of its differentiation capability for delays, discarding/blocking ratios, and throughput. Our simulation results also show that MAPS can achieve higher throughput as compared to EDCF of IEEE 802.11e due to its supports for power-controlled variable-radius transmissions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".