Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In mmWave massive MIMO, the required number of radio frequency (RF) chains becomes impractical due to the expensive and power-hungry components such as variable gain power amplifiers, filters, mixers, and analog-to-digital/digital-to-analog converters (ADCs/DACs). A promising solution to this problem is reducing the number of radiofrequency (RF) chains by partitioning beamforming operations between the digital and RF domains, known as hybrid beamforming (HBF), while still achieving the near-optimal performance of the fully digital beamforming systems with much-reduced hardware complexity. This chapter reviews different HBF techniques for massive MIMO in 5G and radar systems. The basic HBF structures and their algorithm design is presented in the context of a point-to-point MIMO hybrid beamforming system. Then, some recently proposed HBF techniques for 5G and beyond networks are investigated, followed by a discussion about the benefit of HBF in MIMO radar systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it