Joint Downlink and Uplink Aware Cell Association in HetNets With QoS Provisioning
Bibliographic record
Abstract
This paper addresses a joint downlink and uplink aware cell association problem in a multi-tier heterogeneous network in which base stations (BSs) have finite number of resource blocks (RBs) to distribute among the users. An optimization problem is defined to maximize the sum of weighted utility of long term data rate in downlink and uplink through cell association and RB distribution while maintaining quality of service (QoS). Separate outage requirements are considered as QoS constraints for downlink and uplink of a user. Using outage QoS constraints renders the problem suitable for fast fading environments. We propose a distributed scheme for the cell association problem. As users cannot measure the uplink attributes by listening to the reference signals of BSs, a limited amount of feedback is added to the reference signals of the BSs to inform the users of the uplink interference and make the distributed algorithm design possible. Moreover, by assigning different weights to the downlink and uplink data rates, the proposed scheme can be downlink oriented, uplink oriented, or both downlink and uplink aware. Comparing the proposed scheme with the downlink oriented maximum signal to interference plus noise ratio (SINR) scheme, significant uplink rate gains are observed in the simulations as well as for the downlink rates.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".