MMSE hybrid precoder design for millimeter-wave massive MIMO systems
Bibliographic record
Abstract
This paper studies hybrid RF/baseband linear pre-coding design to minimize the mean square error (MSE) for millimeter-wave massive multiple-input multiple-output (MIMO) systems using optimal linear equalizer. Instead of dealing with the objective function of sum MSE, which involves matrix inverses, we approach this problem by minimizing the Euclidean distance between the hybrid precoder and the optimal minimum MSE precoder. In an effort to impose the optimal structure of channel diagonalization, we separate the design of modulus-constrained RF precoder from the design of unconstrained baseband pre-coder. Magnitude-least-squares approximation is introduced to formulate the RF precoder design problem, and is subsequently transformed into a simultaneous matrix diagonalization problem. Such transformation enables application of a simple and numerically stable Jacobi-like algorithm. The effective channel representing a cascade of the derived RF precoder and the MIMO channel, is diagonalized by the baseband precoder. The error performance of the proposed solution is examined by numerical results where the effectiveness is verified by its closeness to the optimal design and its noticeable gain over sparse approximation based schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".