Efficient Joint User Association and Resource Allocation for Cloud Radio Access Networks
Bibliographic record
Abstract
Coordinated scheduling is an efficient resource allocation technique employed to improve the throughput, utilization, and energy efficiency of radio networks. This work focuses on the coordinated scheduling problem for cloud radio access network (CRAN). In particular, we consider the downlink of a CRAN where a central cloud performs the scheduling and synchronization of transmitting frames across the base stations (BSs). For each BS, the transmit frame is composed of several time/frequency slots called resource blocks (RBs). We formulate an optimization problem for joint users to BS association and resource allocation with an objective to maximize the overall network utilization under practical network constraints. The formulated problem is combinatorial and an optimal solution of such a problem can be obtained by performing an exhaustive search over all possible users-to-BSs assignments that satisfy the network constraints. However, the size of search space increases exponentially with the number of users, BSs, and RBs, thus making this approach prohibitive for networks of practical size. This work proposes an interference-aware greedy heuristic algorithm for the constrained coordinated scheduling problem. The complexity analysis of the proposed heuristic is also presented and performance is compared with the optimal exhaustive search algorithm. Simulation results are presented for various network scenarios which demonstrate that the proposed solution achieves performance comparable to the optimal exhaustive search algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".