A Hybrid NOMA/OMA Scheme for MTC in Ultra-Dense Networks
Bibliographic record
Abstract
Non-Orthogonal Multiple-Access (NOMA) where multiple users share the same resources simultaneously, is one promising candidate for beyond 5G. Besides, an Ultra-Dense Network (UDN) with massive numbers of deployed Small Cells (SCs) can significantly boost the performance of the network. In this paper, we propose a hybrid NOMA/OMA scenario deployed within a UDN environment to support a massive number of devices under the umbrella of the massive MachineType Communication (mMTC) use case. NOMA is performed through pairing devices from two disjoint groups with different normalized Signal-to-Interference Ratio (SIR). Using tools from stochastic geometry, we derive an analytical expression for the Area Spectral Efficiency (ASE) gain when deploying our proposed hybrid NOMA/OMA scheme and compare it to a scenario of pure Orthogonal Multiple-Access (OMA). Moreover, in order to reflect the characteristics of the UDN environment, we model the large scale fading using the Stretched Exponential Path Loss (SEPL) model. We show through both simulations and analyses that the gain obtained from the hybrid NOMA/OMA scheme can be optimized based on the different system parameters. We also investigate the impact of densifying the network on the significance of NOMA deployment.
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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 | 0.000 | 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) | 0.000 | 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".