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Record W2140193878 · doi:10.1109/wcnc.2003.1200365

Performance enhancement of joint adaptive modulation, coding and power control using cochannel-interferer assistance and channel reallocation

2004· article· en· W2140193878 on OpenAlexaff
Mohamed H. Ahmed, Halim Yanıkömeroğlu, D.D. Falconer, Soliman A. Mahmoud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceLink adaptationPower controlTime division multiple accessTransmission (telecommunications)AlgorithmChannel (broadcasting)Coding (social sciences)WirelessElectronic engineeringControl theory (sociology)Real-time computingPower (physics)Computer networkFadingEngineeringTelecommunicationsMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a joint adaptive rate selection and power control algorithm for broadband TDMA/TDM wireless networks. The proposed algorithm is a modified, enhanced, and more robust version of the selective power control with active link protection (SPC-ALP) algorithm proposed for adaptive transmission rate and power control in CDMA networks [R.Jantti and S. Kim, 2000]. Unlike SPC-ALP, the proposed algorithm uses adaptive coding and modulation (instead of variable spreading gain) for transmission rate control. In addition, the proposed algorithm is different in two aspects; the first one is the inclusion of the cochannel interferer assistance mode, while the second one is the use of the signal quality as a criterion for user removal. Results show that the proposed algorithm outperforms SPC-ALP in terms of the net throughput and signal quality measures such as the outage probability and frame error rate. Channel reallocation is also studied and found to be very effective in enhancing the system performance particularly at low to medium loading.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.236
Teacher spread0.215 · 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
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

Citations11
Published2004
Admission routes1
Has abstractyes

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