Wireless Power Transfer in mmWave Massive MIMO Systems With/Without Rain Attenuation
Bibliographic record
Abstract
This paper studies the performance of wireless power transfer (WPT) in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems operating in rainy or non-rainy conditions. Accounting for rainfall effects, path loss, and small-scale fading, a comprehensive channel model suitable for modeling the energy propagation in mmWave massive MIMO systems is first developed. Based on this model, a framework for the channel estimation necessary at the hybrid data-and-energy access point (HAP) is provided, and various analytical results on the estimated channel matrix are obtained. Then, using the law of energy conservation, the downlink energy transferred by the HAP and harvested by the user equipments (UEs) is analyzed, and investigated in several important scenarios. The results reveal that the asymptotic harvested energy increases linearly with the number of HAP antennas and the number of UEs, whereas it decreases exponentially with the rain parameters which monotonically increase with the operating frequency. It is also demonstrated that severe rain attenuation can even make the WPT impossible. Afterwards, the scenario where UEs are randomly distributed is investigated, and important insights are gained. In particular, for a WPT system with coverage radius Rnand exclusion radius Re, the average asymptotic harvested energy significantly increases with decreasing Reand Rn, which confirms that small-cells configurations will be viable solutions for enhancing WPT performances in mmWave massive MIMO networks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".