Resource Allocation for an OFDMA Cloud-RAN of Small Cells Underlaying a Macrocell
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Bibliographic record
Abstract
We present a joint resource allocation (RA) and admission control (AC) framework for an orthogonal frequency-division multiple access (OFDMA)-based downlink cellular network composed of a macrocell underlaid by a cloud radio access network (C-RAN) of small cells. In this framework, the RA problems for both the macrocell and small cells are formulated as optimization problems. In particular, the macrocell, being aware of the existence of the small cells, maximizes the sum of the interference levels it can tolerate subject to the macrocell power budget and the quality-of-service (QoS) constraints of macrocell user equipments (MUEs). On the other hand, the small cells minimize the total downlink transmit power subject to their power budget, QoS requirements of small cell UEs (SUEs), interference thresholds for MUEs, and fronthaul constraints. Moreover, AC is considered in the resource allocation problem for the small cells to account for the case where it is not possible to support all SUEs. Besides, to allow for the existence of other network tiers, small cells have a constraint on the number of sub-channels that can be allocated. Both optimization problems are shown to be mixed integer nonlinear problems (MINLPs) for which, lower complexity algorithms are proposed that are based on the framework of successive convex approximation (SCA). Numerical results demonstrate the importance of the careful selection of the resource allocation policy at the macrocell and its impact on the performance of small cells. Moreover, we investigate the effect of the different parameters of the RA problem for the C-RAN of small cells on the overall performance of small cells.
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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 it