Auction Based Distributed Resource Allocation for Delay Aware OFDM Based Cloud-RAN System
Bibliographic record
Abstract
Cloud-radio access network (C-RAN) is regarded as a promising solution to manage heterogeneity and scalability of future wireless networks. The centralized cooperative resource allocation and interference cancellation methods in C-RAN significantly reduce the interference levels to provide high data rates. However, the centralized solution will not be scalable due to the dense deployment of small cells with fractional frequency reuse by small cells, causing severe inter-tier and inter-cell interference turning the resource allocation and user association into a more challenging problem. In this paper, we propose an auction based distributed resource allocation method (ADRA) for a two-tier OFDM based C-RAN system. We investigate a joint user association, radio resource and power allocation problem for small cells underlying a macro C-RAN system. First, we establish a queueing model in C- RAN. We then formulate an optimization problem for joint user association and resource allocation with the aim to minimize mean response time. Resource allocation, interference and queueing stability constraints are considered in the optimization problem. To solve this problem, we propose a distributed method where small cell users and small cell base stations jointly participate using the concept of auction theory. The ADRA method is evaluated via simulations by considering the different ratio of bandwidth utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".