Evaluation of local transmission delay of MIMO-ZFBF multiplexing receivers under correlated interference
Bibliographic record
Abstract
We consider multiple-input multiple-output multiplexing (multi-stream) systems in a spatial Aloha network. The active transmitters are distributed with accordance of Poisson point process with given density and each of which has its own receiver. We mainly focus on zero-forcing beamforming (ZFBF) at the receiver and assume the transmitters do not have access to channel state information. The capacity and outage performance of such network has broadly analyzed in the literature, while a little is known about its delay performance, which is investigated in this paper. Delay measures the number of attempted retransmissions before successful reception of all transmitted data streams of a typical communication link. Due to Nakagami-type fading per data stream, the SIR correlation among data streams of each communication link and among each retransmission attempt - which is due to the common location of transmitters - the evaluation of mean delay is substantially complex. Our rigorous analysis provides a lower- and an upper-bound on the mean delay as the function of the transmitter's density, multiplexing gain, transmission activity, and number of receiver's antennas. We conduct simulations to evaluate the accuracy of the analysis. Important insights regarding the impact of transmission activity and multiplexing gain on the mean delay is highlighted.
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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 | 0.006 |
| 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".