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Record W2140102464 · doi:10.1109/wpmc.2002.1088153

An adaptive downlink spread spectrum OFDM packet data system with two-dimensional radio resource allocation: performance in low-mobility cellular environments

2003· article· en· W2140102464 on OpenAlexaff
R. Novak, Witold A. Krzymień

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRetransmissionComputer scienceOrthogonal frequency-division multiplexingThroughputComputer networkTelecommunications linkFadingNetwork packetTime diversityMultipath propagationChannel (broadcasting)Resource allocationReal-time computingTelecommunicationsWireless

Abstract

fetched live from OpenAlex

In multipath mobile radio channels, fading is both frequency selective and time varying The SS-OFDM-F/TA system allocates time and frequency resources adaptively to provide high bit rate services on the downlink to low-mobility users. Multiuser diversity is employed in this system so that users receive data packets only when conditions are favorable. It is shown that fast best sector selection (1 Hz) provides significant throughput gains in severely shadowed environments. Further, asynchronous retransmission of packets increases the data throughput of the system in comparison to synchronous m-transmissions with only a small increase in packet delay. In a highly frequency selective channel, the allocation of single sub-bands is compared with the allocation of groups of narrower disjoint sub-bands.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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