Hybrid Multi-Dimensional Modulation in Non-Orthogonal Spatial-Delay-Doppler Domains for Beyond 5G, and 6G Communications
Bibliographic record
Abstract
Joint utilization of orthogonal radio resources from multiple domains such as spatial, time-frequency, and delay-doppler domains has become an important paradigm to support diverse QoS requirements (higher datarate, higher spectral efficiency, and low latency) in beyond 5G, and 6G. However, due to higher carrier frequency (mmWave) communication with closely packed massive MIMO antennas, and high-speed mobility in future wireless channels, severe non-orthogonal interferences are dynamically induced in multiple domains which dramatically deteriorate the communication datarate of current OFDM systems. In high speed mobility scenarios, orthogonal time frequency space (OTFS) modulation scheme achieves better communication performance than OFDM at higher modulation cost. Based on these observations, this paper is motivated to propose a novel, situation-aware, cost efficient, switched modulation in spatial, time-frequency, and delay-doppler domains termed hybrid multi-dimensional modulation (H-MDM) scheme that jointly optimizes the radio resource separation to minimize the non-orthogonality degree in each domain, and thus achieves maximized communication datarate under dynamically varying non-orthogonality conditions in those domains. Simulation results validate that the proposed H-MDM achieves maximized datarate compared to state-of-art MIMO-OFDM, and MIMO-OTFS systems under such randomly varying non-orthogonality conditions. Furthermore, we demonstrate that the proposed H-MDM scheme is highly advantageous for high speed mobility, and massive MIMO communication.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".