Hybrid beamforming designs for 5G new radio with fronthaul compression and functional splits
Bibliographic record
Abstract
In this study, the authors investigate the intra‐physical functional splits of 5G new radio protocol stack proposed by different groups. Based on the location of the digital beamforming block, the radio units (RUs) are divided into two categories: Category A and Category B. Two implementation modes of hybrid beamforming at the physical layer are considered, in which digital beamforming is performed either at the distributed unit (DU), as in the Category A RU based hybrid beamforming (HBF‐A) scheme, or at the RUs, as in the Category B RU based hybrid beamforming (HBF‐B) scheme. To maximise the weighted sum rate, the authors formulate the problems of jointly designing hybrid beamforming, analogue combining and fronthaul compression strategies for both HBF‐A and HBF‐B. The formulated problems are simplified by adopting the codebook‐based design, and further tackled by leveraging the majorisation–minimisation algorithm. Finally, numerical results confirm that the HBF‐A scheme outperforms the HBF‐B scheme in the large power regime. Compared with the HBF‐A method, the HBF‐B method is more sensitive to changes in system parameters, such as the compression noise and the number of receive antennas, in the large power regime, while it is less sensitive in the small power regime.
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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 | 0.001 |
| 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 | 0.001 | 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".