Effective Capacity of Multi-Stream MIMO-ZFBF Communications in Large Wireless Networks
Bibliographic record
Abstract
We consider multiple-antenna (MIMO) multiplexing (multi-stream) systems in a spatial Aloha network under quality of service (QoS) constraints. The active transmitters form a bipolar Poisson point process with given density, each with its own communicating receiver. The QoS requirement is specified in terms of the asymptotic decay rate of the buffer occupancy, characterizing the statistical queuing constraint. Assuming open-loop zero-forcing beamforming (ZFBF) at the receiver, we therefore evaluate the effective capacity (EC), which is the throughput metric subject to a given queuing delay requirement. Due to Nakagami-type fading per data stream as well as the SIR correlation among data streams of each communication link- stemmed from the common location of transmitters across data streams-the evaluation of EC is, however, substantially complex, and has not yet considered in the related literature. We in this paper provide a number of approximations that formulate EC as a function of multiplexing gain, the number of antennas, QoS requirement, density of transmitters, and path- loss exponent. Approximations are numerically friendly, and their accuracy are corroborated against simulations. It is seen that for given QoS requirement, there is a multiplexing gain that optimizes EC. We further observe that for less stringent delay requirement, large multiplexing gain is preferable, while by increasing delay exponent it is advocated to shift the operating point toward the single-stream communication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".