Large scale opportunistic antenna and user selection in AF relay networks with interference
Bibliographic record
Abstract
In this paper, the asymptotic performance of multiuser multiple-antenna (MU-MIMO) relay networks employing opportunistic scheduling and operating in the presence of Rayleigh fading and co-channel interference is investigated. Notwithstanding the system complexity, due to the newly found complementary moment generating function (CMGF) transform, an exact expression for the capacity, when the antenna counts at the source and the user number are allowed to grow unbound, is obtained. The large scale analysis embodies popular observations, so far intuitively or empirically disclosed, through analytically insightful new formulas. An interesting aspect of this analysis comes from an altered view of multiuser diversity in the context of cellular systems. Previously, multiuser diversity capacity gain has been known to grow as 0(ln ln(K)), from selecting the maximum of K exponentially-distributed powers. Because interference aware scheduling is considered, we find instead that the gain is 0(ln(K1/Q)) where Q is the number of interferers. Simulation results indicate a rather fast convergence to the asymptotic limits with the system's size, thereby demonstrating the practical importance of the scaling results.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".