Planning and Operation of Millimeter-Wave Downlink Systems With Hybrid Beamforming
Bibliographic record
Abstract
This paper investigates downlink radio resource management (RRM) in millimeter-wave systems with codebook-based hybrid beamforming in a single cell. We consider a practical but often overlooked multi-channel scenario where the base station is equipped with fewer radio frequency chains than there are user equipment (UEs) in the cell. In this case, analog beam selection is important because not all beams preferred by UEs can be selected simultaneously, and since the beam selection cannot vary across subchannels in a time slot, this creates a coupling between subchannels within a time slot. None of the solutions proposed in the literature deal with this important constraint. The paper begins with an offline study that analyzes the impact of different RRM procedures and system parameters on performance. An offline joint RRM optimization problem is formulated and solved that includes beam set selection, UE set selection, power distribution, modulation and coding scheme selection, and digital beamforming as a part of hybrid beamforming. The evaluation results of the offline study provide valuable insights that show the importance of not neglecting the constraint and guide the design of low-complexity and high-performance online downlink RRM schemes in the second part of the paper. The proposed online RRM algorithms perform close to the performance targets obtained from the offline study while offering acceptable runtime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".