Coverage and Capacity Analysis with Stretched Exponential Path Loss in Ultra-Dense Networks
Bibliographic record
Abstract
The distinct features of Ultra-Dense Networks (UDNs), namely, the close proximity of the users to the serving base stations (BSs), the high idle mode probability and the increasing probability of Line-of-Sight (LOS) links to the serving BS, impose a set of requirements on the realistic and accurate modeling of the performance of such networks. In this paper, we consider modeling the path loss by a stretched exponential model which accurately addresses the short distances (5m-350m) between the (serving/interfering) BSs and the users in UDN. Moreover, we study the impact of turning off inactive BSs, as an effective interference mitigation scheme, on the performance of the network in terms of the coverage probability, the network throughput, and the area spectral efficiency. We employ tools from stochastic geometry to model the network as a Homogeneous Poisson Point Process (HPPP). Also, Rayleigh channel fading is assumed for tractability purposes. The results show the significant impact of the users' density on the network performance where the system's interference is upper-bounded mainly by the density of the active users, thanks to turning off the inactive BSs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".