Decoupled Uplink-Downlink Association in Full-Duplex Cellular Networks: A Contract-Theory Approach
Bibliographic record
Abstract
User association is a crucial aspect which greatly affects the performance of wireless networks. In this work, we investigate the user association problem in full-duplex cellular networks, wherein base stations (BSs) are densely deployed with highly variable transmit powers and topologies (e.g., heterogeneous networks). To enhance the system performance, decoupled UL-DL (DUDe) association is considered, which enables each user equipment (UE) to associate with different BSs in uplink (UL) and downlink (DL), respectively. Considering the challenges raised by asymmetric information (e.g., channel gains and intercell interferences) between UEs and BSs, we propose a contract-theory based distributed user association approach. Specifically, the association process is modeled as a labor market, where the BSs act as employers and offer two-dimensional contracts to employees (i.e., UEs) for maximizing the utility of the BS. Theoretical proof for contract feasibility is presented by providing sufficient and necessary conditions. To reach the optimality, a contract-theoretic decoupled user association algorithm is developed, in which a BS broadcasts the drafted contracts, and each UE self-selects the optimal contract by considering her own demands. Numerical results are presented to demonstrate the performance of the proposed approach in terms of node utilities and social surplus. Impacts of system settings on the network performance are also investigated.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".