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Record W2949259901 · doi:10.82308/27613

Capacity and information rates for multiple antenna wireless systems with multi-dimensional modulation

2008· article· en· W2949259901 on OpenAlexfundno aff
Marthe Kassouf

Bibliographic record

VenueeScholarship@McGill (McGill) · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransmitterMIMOTransmitter power outputTransmission (telecommunications)Computer scienceChannel (broadcasting)Multi-user MIMOTelecommunicationsPrecodingWirelessElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

Multiple-Input Multiple-Output (MIMO) systems have shown a great potential for increasing the data transmission rates in wireless communications. These systems are characterized by a high design flexibility which allows a wide variety of efficient signaling techniques and resource allocation methods. In this thesis, we consider the capacity and transmission rates for MIMO systems using various multi-dimensional space-time modulation formats. The latters refer to various allocations of orthogonal signal dimensions (or modulating waveforms) to transmit antennas. For a single-user system with a fixed transmit power, we investigate the most efficient allocations of power and signal dimensions to transmit antennas so that transmission rates can be maximized under different cases of channel state information at the transmitter (CSIT). In many situations, we show that using all the signal dimensions on all the transmit antennas yields the largest rates. An analysis of the capacity and transmission rates is also provided at low and high SNR, where the effect of the spatial channel parameters is pointed out. For a broadcast system, we consider the power and signal dimensions allocations not only for transmit antennas, but also among users. [...]

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.003
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 head0.018
GPT teacher head0.202
Teacher spread0.184 · 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.

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

Citations0
Published2008
Admission routes1
Has abstractyes

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