Exact Outage Analysis for Stochastic Cellular Networks under Multi-User MIMO
Bibliographic record
Abstract
The next generations of cellular wireless systems promise an improved user experience with respect to the throughput, reliability, latency, and connectivity. To this end, multiple-input multiple-output (MIMO) and massive MIMO systems hold enormous potential. Moreover, due to heterogeneity and densification, user and base station locations are increasingly random. Thus, in this paper, we characterize the performance of a typical downlink user within a massive MIMO Voronoi cell when the base stations employ matched filter based precoding in the downlink. We consider a typical Voronoi cell where the base stations follow a Poisson point process (PPP), and also consider the users to be randomly distributed according to a PPP independent from the base stations. Furthermore, a wireless channel with log-distance path loss and Rayleigh fading is assumed. Using a novel framework to model the signal to noise ratio, the outage probability of a user is derived in closed-form while taking into account full and partial loading for the cell's base station due to the randomness of user numbers. Numerical results show that the outage probability is heavily dependent on the base station density, and that the performance decreases when the maximum number of users served by a base station increases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".