Spectrum allocation for wireless backhauling of 5G small cells
Bibliographic record
Abstract
The anticipated massive deployment of the small cells entail wireless backhauling for 5G cellular networks. However, the scarcity of radio frequency (RF) spectrum in the licensed bands is a major limitation which necessitates efficient spectrum planning for backhaul/access links of 5G small cells. This paper investigates the problem of channel assignment in the backhaul/access of small cells. We first formulate a problem to maximize the common achievable rate at the backhaul and access links of the small cells. Due to the NP-hard nature of the problem, we transform the original problem into a less complex convex programming problem and solve it numerically. We then propose and comparatively analyze the performance of two simple distributed backhaul channel allocation criteria, namely, maximum received signal power (max-RSP) and minimum received signal power (min-RSP) criteria. For these criteria, we theoretically derive the number of allocated backhaul channels and coverage probability for a given target rate of each small cell given its distance from the centralized wireless backhaul hub. Simulation results provide insights about the performance gap between the centralized and distributed schemes. Further, it is observed that the min-RSP criterion outperforms the max-RSP criterion which implies that more backhaul channels should be allocated to small cells that are located near the cell-edge.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".