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 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 | 0.000 | 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".