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Record W2138519663 · doi:10.1109/glocom.2005.1578495

MMSE based turbo equalization for chip space-time block coded downlink CDMA

2005· article· en· W2138519663 on OpenAlex
K. C. B. Wavegedara, V.K. Bhargava

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 Communication Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceTurboTurbo codeTelecommunications linkEqualization (audio)Electronic engineeringFadingBlock codeDecoding methodsTurbo equalizerCode division multiple accessMultipath propagationAlgorithmComputer networkChannel (broadcasting)Concatenated error correction codeEngineering

Abstract

fetched live from OpenAlex

Space-time (ST) block coding based transmit diversity is included in the 3G mobile telecommunication specification for CDMA downlink transmission. On the other hand, turbo (iterative) equalization can be used in channel coded broadband wireless systems to enhance the performance. Hence, in this paper, we develop a linear MMSE-based turbo equalization scheme for downlink ST block coded CDMA to achieve high capacity gains over multipath fading channels. The proposed unified CDMA system model considers both conventional and block spreading. However, block spreading is more desirable as confirmed by the simulation results. Both ST encoding and ST decoding are performed at the chip-block level. Turbo equalization is performed after ST decoding. Furthermore, we also consider computationally-efficient implementations of the proposed turbo equalization scheme. Simulation results demonstrate that high performance can be achieved using the proposed iterative equalizer

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: Methods · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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