Adaptive Phase Shifters for Hybrid Beamforming in mmWave Systems
Bibliographic record
Abstract
Full-array (FA) hybrid beamforming, integrating analog and digital components, is employed in millimeter wave (mmWave) systems to achieve directional signal transmission with enhanced gains. The main hardware challenge in this hybrid configuration resides in the analog segment, where each antenna at the transmitters and receivers is linked to an entire network of phase shifters (PSs). To address this, researchers have investigated sub-array (SA) hybrid designs that use fewer PSs to reduce hardware complexity; however, the number of PSs scales linearly with the number of antenna subarrays. This paper introduces innovative adaptive phase shifters (APSs) designed for hybrid beamforming that feature low hardware complexity and operate independently of the number of antenna arrays and radio frequency (RF) chains. The proposed APSs hybrid scheme is designed to achieve performance comparable to the FA iterative hybrid design while minimizing the number of required PSs. Specifically, the number of PSs can vary from two per RF chain to the entire network of PSs, allowing for a more efficient and high-performance design of hybrid systems. Furthermore, the full network of PSs per RF chain, corresponding to the total number of antenna elements, is reduced using modified K-means algorithms. Simulation results demonstrate that the spectral efficiency performance of APSs hybrid designs using only two PSs surpasses that of conventional FA hybrid design and SA hybrid design and exhibits similar performance to FA iterative hybrid designs with a smaller number of PSs. The proposed APSs hybrid design represents promising technology for 6G systems and beyond, owing to its innovative design with low hardware complexity and high-gain spectral efficiency.
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.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".