A completely distributed algorithm for user association in HetSNets
Bibliographic record
Abstract
In this paper, the user association problem under quality of service (QoS) requirements in a heterogeneous and small cells network (HetSNet) is considered. We have shown in a previous work that this problem is NP-hard and thus cannot be solved optimally in polynomial time unless P = NP. Therefore, new suboptimal algorithms are needed in order to solve it efficiently. Even though, it is very hard to implement the suboptimal algorithm in a centralized fashion because it needs a high amount of information exchange between the base stations and the users and it suffers from a huge computational complexity. Thus, in this paper, we model the problem of user association in HetSNets as a non-cooperative game and we propose a completely distributed algorithm inspired by the theory of learning to solve it. Specifically, we propose a modified win-stay-lose-shift learning model in order to converge to a near optimal user association. We evaluate by simulations the performance of the proposed algorithm and and we show that it is close to the performance of the computationally complex optimal centralized algorithm which assumes complete channel information knowledge.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".