Adaptive channel-superframe allocation (ACSA) for 20 GHz wireless networks
Bibliographic record
Abstract
Millimeter-wave (MMW) systems are high frequency wireless systems with a center frequency of around 60 GHz. This thesis deals with adaptive channel-superframe allocation (ACSA) for such system. An adaptive bandwidth or channel allocation algorithm is utilized in the piconet controller (PNC) and a new superframe structure is designed in order to distribute bandwidth among real-time (RT) and non-real-time (NRT) flows. We propose to serve RT and NRT flows separately in different channels instead of serving them in different times. We also propose to 'change the sliced superframe of IEEE 802.15.3 to an adaptive unsliced superframe in order to decrease the TCP round-trip time. We simulated a MMW system with appropriate parameters using 802.15.3 MAC as well as ACSA MAC. We meaSured three performance metrics (throughput, delay and fairness), which we aimed to improve in our superframe design. The simulation results show that the adaptive superframe structure . could provide' throughput improvements not only for NRT flows, but also for RT flows. The control algorithm in PNC could manage the bandwidth allocation in superframe and improve the throughput of RT flows. The channel access delay is improved by providing an unsliced superframe, which eliminated an imposed delay on TCP connections. Finally, the better distribution of bandwidth in ACSA MAC improves the fairness of the system. As a brief, the simulation results support the analysis of the proposed adaptive channelsuperframe allocation algorithm, which could generally improve the quality of service for MMW 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".