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Record W2110776229 · doi:10.1109/glocom.2005.1577688

Spatio-ternporal schedulers in IEEE 802.16

2005· article· en· W2110776229 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGLOBECOM '05. IEEE Global Telecommunications Conference, 2005. · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceBase stationComputer networkWireless broadbandTelecommunications linkWirelessMobile broadbandScheduling (production processes)BroadbandBackhaul (telecommunications)WiMAXBroadband networksWireless networkIEEE 802Real-time computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

With the growing interest in broadband wireless access (BWA) and demand for mobile high-speed connection, there is a need to extend wireless connectivity to passengers travelling in highspeed vehicles. In this paper, we use the IEEE 802.16 standard as a backhaul communication technology for broadband wireless access to railway systems. The proposed architecture uses relay elements located in the vicinity of train track to repeat the signal between the base station and the mobile vehicle. The signal transmitted from the base station is received by repeaters and relayed to the train, and vice versa. We propose spatio-temporal scheduling as a means to increase downlink throughput. The proposed spatio-temporal scheduler distributes data traffic among the repeaters in the vicinity of the train and on the route of the train. Simulation results show that a substantial improvement can be obtained when data are scheduled in both temporal and spatial dimensions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.020
GPT teacher head0.260
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it