On the Interference Range of Small Cells in the Wireless Backhaul of 5G Ultra-Dense Networks
Bibliographic record
Abstract
An Ultra-Dense Network (UDN) is foreseen as a key element of 5G, where small cells operating at millimeter-wave frequencies will be placed in hotspots to accommodate extreme traffic volumes. A fully distributed wired backhaul is not feasible in these UDN deployments because it is not practical to connect each small cell to the core over fiber. A likely substitute is wireless backhaul, where small cells are connected to one or few gateways over multi-hop millimeter-wave wireless links. The backhaul network capacity of the wireless backhaul is directly related to the average number of simultaneous transmissions in the wireless backhaul of an UDN. The problem of finding the average number of simultaneous transmissions is analogous to the minimum coloring problem, which is NP-Hard. In this paper, we adapt our previously proposed heuristic algorithm for solving this problem and then study the effect of varying the interference range of small cells on the backhaul network capacity. From the results of our Monte Carlo simulations, it is observed that the backhaul network capacity decreases significantly as the interference range increases.
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 | 0.008 |
| 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".