User-Centric Multi-Dimensional Modulation for Receiver Operational Cost Minimization in Non-Orthogonal Domains
Bibliographic record
Abstract
The perpetual efforts in supporting ever-growing QoS requirements have brought new challenges in 5G advanced and 6G networks such as increased spatial channel correlation, large delaydoppler spread, higher carrier frequency offset, and more complex multi-path signals. As a result, the orthogonality among spatial, time-frequency, and delay-doppler domain radio resources is often destroyed leading to non-orthogonal radio resources a common paradigm for modulation in next-generation networks. Such non-orthogonality degrees have become both transmitter-receiver pair specific and domain specific due to diverse channel conditions perceived by the UE involved. Furthermore, the operational cost of restoring orthogonality and demodulation varies across domains due to different synchronization and interference cancelation capabilities of receiver in different domains. To tackle these issues, we propose user-centric multi-dimensional modulation (UC-MDM) aiming to minimize receiver costs while supporting necessary data rates. Our situation-aware, cost-conscious UC-MDM optimizes resource separation across spatial, time-frequency, and delay-doppler domains, utilizing optimal resource combinations through multidimensional modulation in either spatial-time-frequency or spatial-delay-doppler domains. Simulation results under simultaneously varied domain specific non-orthogonality degrees validate that UC-MDM achieves required data rate with less operational cost from user-device compared to MIMO-OFDM and MIMO-OTFS systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".