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Record W1607378278 · doi:10.1109/ictrc.2015.7156412

On the comparison between code-index modulation and spatial modulation techniques

2015· article· en· W1607378278 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer scienceModulation (music)Code (set theory)TransmitterSpatial modulationIndex (typography)Modulation indexElectronic engineeringEnergy (signal processing)Set (abstract data type)Antenna (radio)AlgorithmTelecommunicationsMIMOMathematicsElectrical engineeringPulse-width modulationEngineeringStatistics

Abstract

fetched live from OpenAlex

Recently, two promising modulation techniques have been developed aiming to increase data rate and save energy while being simple to implement. These modulation schemes belong to two different communication methods, however, they share the common structure of using an index as an additional parameter to convey information. The first scheme known as spatial modulation (SM), is a scheme that uses multiple antennas at the transmitter side where just one antenna is activated at a time and its index is used as means to convey information. The second is known as code-index modulation (CIM), a system that uses multiple spreading codes, where a certain code is selected and its index is used as a mechanism to ferry data. In this paper, we present these two modulation techniques and we discuss the associated set of challenges for each scheme. Moreover, in order to evaluate the advantages and disadvantages of each technique, we compare the energy efficiency, the system complexity, and the bit error rate performance of the SM and CIM schemes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.289

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 head0.049
GPT teacher head0.290
Teacher spread0.240 · 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