Hybrid analog and digital beamforming for OFDM-based large-scale MIMO systems
Bibliographic record
Abstract
Hybrid analog and digital beamforming is a promising technique for large-scale MIMO systems since it can achieve a performance close to the performance of the conventional fully-digital beamforming schemes, but with much lower hardware implementation complexity and power consumption. One of the major challenges in hybrid beamforming is to design the hybrid beamformers for broadband systems with frequency-selective channels. This is because in broadband systems, it is desirable to design the same analog beamformers for the entire band while adapting digital beamformers in each frequency tone. In this paper, we consider the hybrid beamforming design for large-scale MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation. Specifically, we propose a unified heuristic design for two different hybrid beamforming structures, the fully-connected and partially-connected structures, to maximize the overall spectral efficiency of a broadband system. Numerical results show that the proposed algorithm outperforms the existing hybrid beamforming designs and further the proposed algorithm for the fully-connected structure can achieve spectral efficiency close to that of the optimal fully-digital solution with much less number of RF chains.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".