Matching with externalities for decoupled uplink-downlink user association in full-duplex small cell networks
Bibliographic record
Abstract
In multi-tier cellular/small cell networks, decoupled uplink-downlink association (DUDe) enables a user to be associated with different base stations (BSs) for uplink (UL) and downlink (DL) transmissions. In this paper, we consider the overall UL and DL rate maximization problem of the users in such a network with a provisioning for decoupled association. In particular, we formulate the UL and DL user association problem as a matching game where users and BSs rank one another using well-defined preference metrics such that their total UL and DL throughput is maximized. The preference profiles of the users are dynamic subject to the unique interference conditions in DUDe networks resulting due to dynamic user associations. As such, considering the impact of externalities becomes crucial in a matching game. To this end, we propose an algorithm based on swap matching that enables players to find a stable association such that no user tries to change her association. Simulations are conducted to show the efficacy of the proposed swap matching algorithm over traditional user association in DUDe networks in which simple user association criteria are used for both the UL and DL transmissions (i.e., path-loss in the UL and received signal power in the DL).
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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 | 0.006 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".