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Record W2167074200 · doi:10.1109/vetec.1996.504062

Analysis of two-layered adaptive transmission systems

2002· article· en· W2167074200 on OpenAlexaff
Mahdi Sajadieh, Frank R. Kschischang, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceChannel (broadcasting)Channel state informationLink adaptationPhase-shift keyingTransmitterBit error rateRayleigh fadingTransmission (telecommunications)Signal-to-noise ratio (imaging)PrecodingChannel capacityFadingElectronic engineeringComputer networkTelecommunicationsWirelessEngineeringMIMO

Abstract

fetched live from OpenAlex

A major challenge for personal communication systems is to meet the growing demand for various services when the frequency resources are increasingly scarce. Given the time varying nature of the radio channel, an efficient resource utilization calls for channel-adaptive transmission schemes. This paper integrates the two concepts of channels with side information and broadcast channels and introduces a model for flat Rayleigh fading channels with perfect interleaving and a single bit channel state information at the receiver. A true-level modulation constellation is deployed to send information to a two-state channel, where the state of the channel is determined by monitoring the received signal to noise ratio. The limited adaptability of the system helps gear up to a higher data rate as channel conditions improve, without any adjustment at the transmitter. A two-stage receiver, driven by the channel state estimation device, demodulates at full rate only if the channel signal to noise ratio is above a pre-set threshold. The average mutual information of the model as well as the bit probability expressions are derived for a non-uniform 8 PSK signal set. It is shown that the achievable rate is higher than that obtained using a uniform QPSK with only a negligible loss in error rate.

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 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: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.257

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 head0.028
GPT teacher head0.251
Teacher spread0.223 · 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 teacher head, not a consensus.

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

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

Citations7
Published2002
Admission routes1
Has abstractyes

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