Fair SSB Codebook Design for Multi-Cell mmWave MIMO Communications
Bibliographic record
Abstract
For millimeter wave communications, beams used to transmit synchronization signal blocks (SSBs) affect both base station coverage and beam training overhead. We therefore consider fair SSB codebook design, formulated as an optimization problem, aiming to maximize the minimum average signal-to-interference-plus-noise ratio (SINR) across user clusters. This problem is challenging due to the non-smoothness of the objective function, arising from the optimal beam-pair selection function and the minimum operator. To address this, we propose a double-loop framework, where the outer loop constructs approximations for the selection function with iteratively reduced error, and the inner loop solves the resulting approximate problems. In each inner-loop iteration, the objective function of the approximate problem is smoothed with iteratively reduced smoothness, enabling gradient derivation. This gradient is then estimated using variance-reduced estimators based on samples from users, and the result is used to update codebooks. Following this framework, we develop both first-order (FO) and zeroth-order (ZO) oracle schemes. The FO scheme requires full channel state information samples for gradient estimation while the ZO scheme only requires SINR samples. Simulation results show that in given scenarios, both schemes achieve SINR fairness comparable to or better than that of discrete Fourier transform codebooks, but with fewer beams.
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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 | 0.002 | 0.007 |
| 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 | 0.002 | 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".