Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks
Bibliographic record
Abstract
This paper addresses the cell association problem in the downlink of a multi-tier heterogeneous network (HetNet), where base stations (BSs) have finite number of resource blocks (RBs) available to distribute among their associated users. Two problems are defined and treated in this paper: sum utility of long term rate maximization with long term rate quality of service (QoS) constraints, and global outage probability minimization with outage QoS constraints. The first problem is well-suited for low mobility environments, while the second problem provides a framework to deal with environments with fast fading. The defined optimization problems in this paper are solved in two phases: cell association phase followed by the optional RB distribution phase. We show that the cell association phase of both problems have the same structure. Based on this similarity, we propose a unified distributed algorithm with low levels of message passing to for the cell association phase. This distributed algorithm is derived by relaxing the association constraints and using Lagrange dual decomposition method. In the RB distribution phase, the remaining RBs after the cell association phase are distributed among the users. Simulation results show the superiority of our distributed cell association scheme compared to schemes that are based on maximum signal to interference plus noise ratio (SINR).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".