Stochastic geometry analysis of error probability in interference limited wireless networks
Bibliographic record
Abstract
In this paper, we present mathematical frameworks for error performance analysis in interference limited networks such as cellular networks. Due to the increasing irregularity in the spatial deployment of nodes in the emerging heterogeneous cellular networks (HCNs), we employ stochastic geometry approach by abstracting the node locations as a homogeneous Poisson point process (PPP). First, we characterize the average error probability of an intended communication link with a given transmitter-receiver separation, which is subject to interference from these Poisson distributed nodes. More specifically, we develop uniform approximation (UA), which is highly accurate over the whole range of signal-to-interference ratio (SIR) and hence, serve as an alternative to existing complex analytical results. Error probability UAs for both single-antenna and maximal ratio combining (MRC) receivers are derived in this paper. Next, we evaluate the average error probability of any typical user in the network, which is served by the node providing the maximum received power. Mellin-transform based method is proposed in this case, which often yield closed-form solution. An example of BPSK modulation is given in the paper.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".