Game-Based Zero-Forcing Precoding for Multicell Multiuser Transmissions
Bibliographic record
Abstract
This paper studies the precoding design in a multicell multiuser (MU) system with universal frequency-reuse using a game-based approach. Considered is a multicell system, where the MU downlink transmissions in each cell are facilitated by a multi-antenna base-station (BS). In particular, the BS wishes to maximize the transmission sum-rate to its connected mobile-stations (MS) by the means of zero-forcing (ZF) precoding. In this context, the paper considers a strategic non-cooperative game (SNG), where each BS greedily determines its optimal power allocation in a distributed manner, based on the knowledge of the out-of-cell interference (OCI) at its connected MSs. Via the game theory framework, we study the existence and uniqueness of a Nash equilibrium (NE) of this multicell game. It is shown that a NE of the game always exists, whereas the NE uniqueness is guaranteed under a certain condition on the OCI. Numerical results confirm with the analysis that a small OCI level almost always leads to the NE's uniqueness. Simulations also show that the multicell game using known OCI knowledge provides additional sum-rate gains over the scheme with no OCI information.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".