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Record W2096016927 · doi:10.1109/isit.2002.1023404

An algebraic number theoretic framework for space-time coding

2003· article· en· W2096016927 on OpenAlexaff
H. El Gamal, Mohamed Oussama Damen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFadingChannel state informationDecoding methodsComputer scienceSpace–time codeTransmitterConstellation diagramAlgorithmMIMOMathematicsFull RateTheoretical computer scienceTopology (electrical circuits)Channel (broadcasting)TelecommunicationsBit error rateWirelessCombinatorics

Abstract

fetched live from OpenAlex

In this paper, we develop a novel framework for constructing full rate, full diversity, and polynomial complexity space-time codes for systems with arbitrary numbers of transmit and receive antennas. The proposed framework combines space-time layering concepts with algebraic number theoretic constellations to construct universal codes for scenarios where the channel state information (CSI) is known a-priori at the transmitter and receiver (TR-CSI), receiver only (R-CSI), and neither one of them (N-CSI). For a coherent system (i.e., R-CSI) with M transmit and N receive antennas in quasi-static fading, the proposed codes are constructed over T = M symbol periods by properly assigning algebraic number theoretic constellations to the different layers. The proposed codes are delay-optimal and achieve the maximum diversity advantage MN over quasi-static fading channels for arbitrary numbers of antennas and arbitrary transmission rates. The lattice structure of the proposed codes allows for polynomial complexity maximum likelihood decoding using the sphere decoder. The proposed framework subsumes many of the existing codes in the literature, extends naturally to time-selective and frequency-selective channels, and allows for more flexibility in the trade-off between power efficiency, bandwidth efficiency, and receiver complexity.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.581
Threshold uncertainty score0.811

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)0.0010.000

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 head0.010
GPT teacher head0.278
Teacher spread0.268 · 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 designTheoretical or conceptual
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

Citations24
Published2003
Admission routes1
Has abstractyes

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