Statistically-Aided Codebook-Based Hybrid Precoding for mmWave Single-User MIMO Systems
Bibliographic record
Abstract
In this paper, we propose practical yet effective statistically-aided codebook-based hybrid precoding schemes for single-user massive MIMO systems operating in millimeter wave channels. We develop novel hybrid precoding algorithms for selecting analog and/or the digital precoders from DFT-based codebooks. The selection algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder given by the dominant right singular vectors of the channel and the hybrid (digital/analog) beamformer selected from statistically skewed DFT codebooks. We investigate the performance of the proposed algorithms by considering the mutual information as a performance metric. We derive lower and upper bounds on the mutual information of the channel given the proposed algorithms. Moreover, we show that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the millimeter wave channel. Then, we show that the proposed algorithms are asymptotically optimal as the number of transmit antennas M goes to infinity and the millimeter wave channel has a limited number of paths, i.e., P <; M. Finally, we verify the performance of the proposed algorithms and the DFT codebook numerically. The results illustrate that the spectral efficiency performance of the proposed algorithms approaches the optimal precoder performance in certain scenarios.
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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 | 0.003 |
| 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) | 0.002 | 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".