Outage Performance and Average Rate for Large-Scale Millimeter-Wave NOMA Networks
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) and millimeter wave communications are key technologies for the fifth-generation of cellular networks and beyond, and the coexistence of these two techniques is critical. This paper characterizes the system performance through the outage probability and achievable downlink rate for large-scale millimeter wave NOMA networks by using stochastic geometry. In order to reflect spatial randomness, we consider homogeneous Poisson point processes to model the base stations and user equipments. Moreover, blockages which affect the channel characteristics, power allocation based on the combined channel gain, and imperfections in the successive interference cancellation are considered. The aggregate co-channel interference at a user is characterized based on the moment generating function. Finally, the outage probability and downlink rate are derived for a two-user NOMA scenario under two user-base station association schemes: 1) closest base station association and 2) closest line-of-sight base station association. It is seen that using NOMA under millimeter wave channels increases the achievable downlink rate while keeping the performance impact on individual users low, and that the closest line-of-sight base station association scheme is comparatively advantageous. Moreover, a dense base station deployment generally improves the performance further.
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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 | 0.008 |
| 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 | 0.001 |
| 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".