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Record W3215601134 · doi:10.1109/5gwf52925.2021.00021

Multi-Dimensional Modulation for Data Rate Maximization in Non-Orthogonal Spatial-Time-Frequency Domains

2021· article· en· W3215601134 on OpenAlexaff
Thakshanth Uthayakumar, Jie Mei, Xianbin Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceOrthogonalitySpatial multiplexingMIMOOrthogonal frequency-division multiplexingInterference (communication)Electronic engineeringModulation (music)Frequency domainSpatial correlationTelecommunicationsChannel (broadcasting)MathematicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Loss of orthogonality among radio resource blocks in different domains introduces additional interference such as inter carrier interference (ICI) in frequency domain, and inter symbol interference (ISI) in time domain, and inter antenna correlation (IAC) in spatial domain. Such interference due to imperfect time/frequency synchronization and spatial separation are usually tolerated in LTE, and 5G communication systems. However, with the ongoing wireless evolution towards beyond 5G and 6G networks that operate in mmWave bands with significantly higher data rates, these interference will become more severe, and deteriorate the system performance significantly. Based on these observations, this paper is motivated to maximize the communication data rate under non-orthogonality conditions that occur in spatial, time and frequency domains. We propose a novel multi-dimensional modulation (MDM) scheme to achieve our objective. The simulation results demonstrated that our proposed MDM scheme achieves maximized data rate in spatial-time-frequency domains and it outperforms the state-of-art spatially multiplexed MIMO-OFDM system. We also show that the proposed MDM scheme is highly advantageous for mmWave and massive MIMO applications.

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 score0.554
Threshold uncertainty score0.549

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 head0.029
GPT teacher head0.268
Teacher spread0.239 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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