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Non-Orthogonal Multiple Access with Index Modulated Non-Orthogonal Frequency Division Multiplexing

2025· article· en· W4414648453 on OpenAlexaff
Md. Shahriar Kamal, Muhammad Sajid Sarwar, Soo Young Shin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersInstitute for Information and Communications Technology PromotionMinistry of Science and ICT, South Korea
KeywordsSubcarrierOrthogonal frequency-division multiplexingFrequency-division multiplexingMultiplexingSpectral efficiencyContext (archaeology)Base stationChannel access methodTime-division multiplexing

Abstract

fetched live from OpenAlex

This work proposes spectral efficient frequency division multiplexing (SEFDM) based non-orthogonal multiple access (NOMA) with subcarrier index modulation (SIM). SEFDM exploits the frequency division multiplexing in a non-orthogonal manner to acquire a spectral efficiency (SE) better than conventional orthogonal frequency division multiplexing (OFDM) at the cost of increased inter-carrier interference. In SEFDM-SIM, the subcarriers are activated as per the incoming bitstream to convey supplemental bits of information virtually through the active subcarrier indices, offering a balance between SE and error performance. Additionally, NOMA allocates varying levels of power for particular users depending on the distance between the users and the base station (BS), which allows to provide service for multiple users on the very resource and increase SE further. The performance of NOMA-SEFDM-SIM is investigated in the context of of bit-error rate and SE.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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
Published2025
Admission routes1
Has abstractyes

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