On the Multiplexing Capability of mmWave Doubly-Massive MIMO Systems in LOS Environments
Bibliographic record
Abstract
This paper is concerned with the potential of spatial multiplexing in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) downlink system in which the base station (BS) is equipped with multiple distributed massive antenna subarrays to serve multiple users. The paper first introduces the concept of multiplexing gain when the number of antennas goes to infinity and the transmit power is finite. Assume that each of the user terminals is equipped with a large antenna array, the paper focuses on the simple and cost-effective analog beamforming scheme for the MIMO system operating in mmWave line-of-sight propagation environments. We obtain simple and approximate expressions for the sum rate of the system based on asymptotic analysis in the limit of the numbers of antennas at the BS and at the user terminals when the total transmit power is fixed or scaled down. The approximate expressions show that the employed analog beamforming scheme can achieve a multiplexing gain that is equal to the product of the number of subarrays at the BS and the number of users when the total transmit power is fixed, whereas the multiplexing gain decreases proportionally to such a product when the total transmit power is scaled down. Extensive numerical results are also provided to corroborate analytical results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".