Hybrid analog-digital beamforming for multiuser MIMO millimeter wave relay systems
Bibliographic record
Abstract
This paper proposed new hybrid, analog-digital, beamforming for a multiuser millimeter wave (mm-wave) relay system. For this system, we consider a sum rate maximization problem. The proposed hybrid beamforming is designed indirectly by considering a sum mean square error (MSE) minimization problem while utilizing the solution of digital beamforming. To this end, we assume that the digital beamforming utilizes the well known block diagonalization (BD) approach. Under this assumption, we solve our problem as follows: First, we formulate the sum rate maximization problem as the minimization of the MSE between the received signal of the hybrid and digital beam-forming designs. Then, we design the hybrid beamformings of the source, relay and each destination by leveraging compressive sensing techniques. Simulation results confirm that the proposed hybrid beamforming design achieves performance very close to that of the digital one. Furthermore, we have examined the effects of the number of radio frequency (RF) chains and paths together, and the accuracy of angle of arrival (AoA) and angle of departure (AoD) estimators on the sum rate of the hybrid beamforming designs. Computer simulations reveal that the total sum rate of the hybrid beamforming increases when the number of RF chains and paths increase (or the accuracy of the AoA (AoD) estimator improves).
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".