On the Performance of MIMO-SVD Multiplexing Systems in HetNets: A Stochastic Geometry Perspective
Bibliographic record
Abstract
In the paper, we study network-wise performance of multistream multi-input multi-output (MIMO) singular value decomposition communications in heterogeneous networks (HetNets). We adopt tools from stochastic geometry to model HetNets through K tiers of interfering base stations (BSs) and evaluate the coverage probability and achievable spectral efficiency (ASE), assuming max-signal-to-interference ratio (SIR) cell association rule. For this model, the main contributions are studying the coverage probability of MIMO multiplexing systems from a communication link's viewpoint; investigating the cross-stream SIR correlation coefficient and highlighting the impacts of path-loss exponent and the number of receive antennas on the growth of it; and obtaining easy-to-compute closed-form approximates of the coverage probability and ASE. The developed expressions explicitly reveal the impact of many system parameters, including the number of tiers, density of BSs, transmission power, and the number of data streams. Simulations are conducted to confirm the accuracy of our analysis. Various important aspects of HetNets with respect to densification, high multiplexing gains, and large antenna arrays are demonstrated. Results showcase the significance of channel state information at the transmitter on the network's performance. With the results of this paper, further investigations and system designs are made possible.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".