Capacity Analysis of Downlink NOMA-Based Coexistent HTC/MTC in UDN
Bibliographic record
Abstract
The coexistence of human-type communications (HTC) users and machine-type communications (MTC) devices is inevitable in the next generation of cellular communications. In this paper, we study the impact of the association schemes on the downlink capacity of ultra-dense networks (UDNs). We adopt non-orthogonal multiple access (NOMA) where HTC users and MTC devices share the spectrum based on power-NOMA approach. The capacity in terms of average rate and area spectral efficiency (ASE) is analyzed for two extreme association schemes, namely, connect to active (C2A) and connect to closest (C2C). Furthermore, we investigate the network performance while moving from one extreme to the other. On one hand, C2A is an efficient scheme from HTC users' perspective where it can keep most of the existing base stations (BSs) in the idle mode; saving energy and reducing interference. However, it provides unsatisfactory performance for the MTC devices. On the other hand, moving towards C2C is favorable to MTC devices, however, it deteriorates the ASE of the HTC users and increases the transmission power density. Accordingly, we seek a compromise between these two contradicting schemes. Accurate expressions for the network performance are derived using tools from stochastic geometry and validated by Monte Carlo simulations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".