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Record W2142743095 · doi:10.1109/glocom.2006.711

WLC17-3: Performance of IEEE802.16 Random Access Protocol - Transient Queueing Analysis

2006· article· en· W2142743095 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 · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkRetransmissionRandom accessExponential backoffQueueing theoryOrthogonal frequency-division multiple accessCode division multiple accessQueueReal-time computingOrthogonal frequency-division multiplexingThroughputNetwork packetWirelessTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, we consider the transient queueing response of IEEE802.16 random access protocol with piggyback and automatic repeat request. The random access protocol of IEEE 802.16 is physically based on orthogonal frequency- division-multiple-access (OFDMA)-code-division-multiple-access (CDMA) with time division duplexing (TDD) mode. In medium access control (MAC) layer, the protocol is a type of demand- assigned multiple access (DAMA) with piggyback, in which a bandwidth request can be allowed either before transmitting data or at the end of data transmission. The model of our interest is an M/G/l type queue with set-up times and exhaustive service, which is solved by a generating function approach. In the analysis, we obtain the time dependent mean queue length, the time dependent empty probability of the queue and the time dependent busy period. The random access success probability is derived from the system equilibrium. Retransmission probability is also derived by including a binary exponential backoff algorithm.

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 categoriesnone
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 score0.740
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.237
Teacher spread0.231 · 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