A Matching Game for Decoupled Uplink-Downlink User Association in Full-Duplex Small Cell Networks
Bibliographic record
Abstract
In multi-tier cellular/small cell networks, user performance is largely affected by the varying transmit powers, distances, and non- uniform traffic loads of different BSs in both the downlink (DL) and uplink (UL) directions of transmission. To optimize user performance in such networks, decoupled UL-DL association (DUDe) has recently been investigated. DUDe enables a user to be associated with different BSs for UL and DL transmissions. In this paper, we investigate the feasibility of DUDe in a full-duplex two-tier cellular network. Our objective is to associate users to their preferred BSs to maximize the overall user rate both in UL and DL with a provisioning for decoupled association. 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. When compared to DUDe in half-duplex networks, in full-duplex networks it introduces new types of interferences such as UL to DL interference or DL to UL interference. The preference metrics are thus defined as a function of achievable UL and DL signal-to- interference noise ratio (SINR). Simulation results are presented to compare the performance of the proposed user association scheme with those of the traditional DUDe and coupled user association schemes where simple user association criteria (e.g., path-loss in the UL and received signal power in the DL) are used for UL and DL transmissions.
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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 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".