Mean Delay Analysis of MIMO-ZFBF Multiplexing in Random Networks Under LOS/NLOS Path-Loss Model
Bibliographic record
Abstract
We analyze the performance of a multiple-input multiple-output multiplexing system in a Poisson bipolar network under line-of-sight/non-line-of-sight (LOS/NLOS) path-loss model and zero-forcing beamforming at receivers. The capacity and outage performance of such a configuration, commonly under the standard path-loss model, have been broadly analyzed; yet little is known about its local transmission delay with considering the traits of LOS/NLOS model. As the effective fading power gain on each data stream is Nakagami-type, and due to the interference correlation across data streams of a link as well as the retransmission attempts, the evaluation of the mean delay is more involved than the capacity/coverage evaluation. Our rigorous analysis provides a lower bound and an approximate upper bound on the mean delay as the functions of density, multiplexing gain, transmission activity, and LOS/NLOS model, which sheds some light on the effect of the LOS component in circumventing the possible divergence of the mean delay. Simulations show the lower bound is very accurate and demonstrate several aspects of multiplexing, path-loss traits, and interference correlation on the mean delay. Exploiting the analysis, we further explore the optimization of effective spatial throughput of the network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".