Diversity gain of millimeter-wave massive MIMO systems with distributed antenna arrays
Bibliographic record
Abstract
This paper is concerned with diversity gain analysis for millimeter-wave (mmWave) massive MIMO systems employing distributed antenna subarray architecture. First, for a single-user mmWave system in which the transmitter and receiver consist of K t and K r subarrays, respectively, a diversity gain theorem is established when the numbers of subarray antennas go to infinity. Specifically, assuming that all subchannels have the same number of propagation paths L , the theorem states that by employing such a distributed antenna subarray architecture, a diversity gain of K r K t L − N s +1 can be achieved, where N s represents the number of data streams. This result means that compared to the co-located antenna architecture, using the distributed antenna subarray architecture can scale up the diversity gain proportionally to K r K t . The analysis of diversity gain is then extended to the multiuser scenario as well as the scenario with conventional partially connected radio-frequency structure in the literature. Simulation results obtained with the hybrid analog/digital processing corroborate the analysis results and show that the distributed subarray architecture indeed yields a significantly better diversity performance than the co-located antenna architectures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".