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Record W2145171840 · doi:10.1109/icassp.2004.1326929

Design of optimal orthogonal linear codes in MIMO systems for MMSE receiver

2004· article· en· W2145171840 on OpenAlexaff
Jing Liu, Jian-Kang hang, K.M. Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMIMOTransmitterMinimum mean square errorUpper and lower boundsFadingComputer scienceInterference (communication)AlgorithmBit error rateMean squared errorSignal-to-noise ratio (imaging)Block codeMathematicsChannel (broadcasting)Control theory (sociology)TelecommunicationsStatisticsDecoding methods

Abstract

fetched live from OpenAlex

In this paper, we present a design of linear codes which improves the bit error rate (BER) performance for multiple-input multiple-output (MIMO) communication systems, under a flat-fading environment, in which the channel information is unknown for the transmitter but known at the receiver. Recently, space-time codes have been designed to achieve capacity and/or minimize the overall mean square error. Our codes here simultaneously achieve the lower bound of BER, the upper bound of the overall signal to interference plus noise ratio (SINR) and the lower bound of substream minimum mean square error. Our code structure is orthogonal in both overall and individual senses.

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 score0.607
Threshold uncertainty score0.328

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 head0.030
GPT teacher head0.273
Teacher spread0.243 · 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

Citations12
Published2004
Admission routes1
Has abstractyes

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