Millimeter Wave Massive MIMO with Alamouti Code and Imperfect Channel State Information
Bibliographic record
Abstract
Massive MIMO is one of the technologies considered in 5G to provide a much higher capacity without requiring more wireless spectrum. Because of the congestion in the radio frequency spectrum below 6 GHz, the millimeter Wave (mmWave) band has increasingly become more attractive to researchers since it offers a much larger unused bandwidth. However, the higher path loss at mmWave frequencies and the poor scattering nature of the mmWave channel (fewer paths exist) represent a challenge for communications. A hybrid beamforming technique with large antenna arrays and the Alamouti coding scheme are used in this paper to improve the performance of a single-user mmWave massive MIMO system. When perfect Channel State Information (CSI) is not available at the Base Station (BS), computer simulations have shown that a gain of 20 dB or more can be achieved by using the Alamouti code compared to the system with hybrid beamforming only, and more importantly, the error probability does not level off.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".