Two Stage Beamforming in Massive MIMO: A Combinatorial Multi-Armed Bandit Based Approach
Bibliographic record
Abstract
In frequency division duplex (FDD) massive multi-input multi-output (MIMO), the two-stage beamforming (TSB) using channel covariance matrices (CCM) can significantly reduce the downlink training length (DTL) and channel feedback. However, the overhead to estimate the CCM is large. In this paper, a combinatorial multi-armed bandit (CMAB) based TSB scheme is proposed without requirement of CMM. Particularly, the problem of the pre-beamforming matrix design is transformed into a CMAB problem. We consider the pre-beamforming matrix design in each slot as the arm selection in the CMAB, and convert the problem of the arm selection into a 0-1 integer linear programming problem, which can be solved by the branch-and-bound method. During the training process, the maximum likelihood method is used to detect the power of angle spectrum, and the angle range of each user is determined adaptively. We prove that the regret grows logarithmically with time, such that the proposed scheme converges towards the optimal action. Finally, simulation results demonstrate that the proposed scheme can significantly improve the spectral efficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".