Optimal Node Density for Multi-RAT Coexistence in Unlicensed Spectrum
Bibliographic record
Abstract
Network densification provides coverage by reducing load factor and path loss between user equipments (UEs) and the serving base stations (BSs) or access points (APs) [1]. To increase capacity, a wide range of spectrum in diverse bands could be harnessed by deploying dense multiple radio access technologies (RATs). This benefit of densification is contingent on the interference level generated by high density coexisting nodes and multi-RATs in the same spectrum. To this effect, we seek to optimize the maximum node density that should coexist in a network to maximize throughput performance for high density networks. This is important because increasing network density (densification) increases interference and contention, and subsequently degrades aggregate performance. Using stochastic geometry tools, a special case of two co-existing RATs is considered. Due to the unplanned nature of APs and/or BSs deployments, the nodes are assumed to be realizations of Poisson point processes (PPPs). Numerical results reveal that optimizing node density results in throughput gains for mid to high density networks.
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 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".