QoS-Aware Hybrid Beamforming With Minimal Power in mmWave Massive MIMO Systems
Bibliographic record
Abstract
Hybrid beamforming is used to leverage the benefits of both massive multiple input and multiple output (MIMO) systems and millimeter waves for significantly increasing the capacity of wireless networks. Existing schemes for hybrid beamforming in multiple radio frequency (RF) chains optimize a global measure of performance and ignore quality of service (QoS) per data stream. In this paper, we propose a novel scheme for hybrid beamforming to minimize the transmit power while satisfying the QoS defined as the mean square error (MSE), but other QoS measures can also be considered. To do so, we propose a two-stage cascade structure for the baseband precoder and combiner, and obtain their respective matrices in both single- and multi-user systems.We also propose a simplified scheme for designing the hybrid precoder and combiner with no nested while loops in the alternating optimization method. Simulations show less than 2 dB optimality gap for the multi-user scenario, with significantly less transmit power as compared to other existing schemes. Our simplified scheme exhibits negligible degradation in performance.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".