Multi-Dimensional Modulation for Data Rate Maximization in Non-Orthogonal Spatial-Time-Frequency Domains
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".