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Record W1566544339 · doi:10.1109/pimrc.2004.1373878

Transmitter and receiver designs for the MIMO fading broadcast channel

2005· article· en· W1566544339 on OpenAlexaff
R. Doostnejad, Teng Joon Lim, E. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceTransmitterFadingMIMOSpectral efficiencyCode division multiple accessTelecommunications linkElectronic engineeringCoding (social sciences)DetectorBit error rateDiversity gainChannel (broadcasting)Transmit diversityAlgorithmTelecommunicationsMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

We study the downlink of a MIMO multi-user system when no information about the channel is assumed at the transmitter. Instead of applying direct-sequence code-division multiple access (DS-CDMA) over the codewords of conventional space-time coding (STC) schemes, a modulation technique that can be seen as two-dimensional space-time spreading (2D-STSC) is described. It is based on well-known Walsh codes, provides full transmit diversity and high spectral efficiency, and produces groups of users that are orthogonal to each other. This last point translates into simplified detection strategies without loss of performance. The main detector structure of interest is a two-stage interference canceller which employs serial interference cancellation (SIC) in the first stage. We will demonstrate that in conjunction with an unequal power allocation scheme, this receiver is able to provide full diversity and suffers from only a small performance loss compared to the full-complexity maximum likelihood receiver. The proposed scheme compares favorably with related ones in terms of spectral efficiency, bit error probability, and complexity at the receiver.

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)0.002
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.034
GPT teacher head0.260
Teacher spread0.226 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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