Secondary User Interference Characterization for Spatially Random Underlay Networks With Massive MIMO and Power Control
Bibliographic record
Abstract
In an underlay (secondary) network, the receiver nodes are subject to both primary and intra-underlay interference. What are the characteristics of this interference when considering the use of massive multiple-input multiple-output (MIMO) systems with pilot contamination, path-loss-inversion power control, receiver association policies, spatially random nodes, and propagation characteristics with power-law path loss and Rayleigh fading? To answer this question, we derive the average and the moment generating function of the aggregate interference and its average due to both primary and underlay transmissions from nodes modeled as Poisson point processes and analyze how the interference impacts the outage performance of an underlay receiver. Our analysis considers all of the above factors and both single antenna type and massive MIMO base stations. We show that massive MIMO improves the outage performance, and a higher path loss exponent reduces the outage probability. This is in contrast to single antenna systems where a higher path loss exponent increases the outage. Furthermore, it is shown that the different node densities and power thresholds significantly affect the outage performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".