2D Antenna Array Structures for Hybrid Massive MIMO Precoding
Bibliographic record
Abstract
This paper investigates the performance behaviours of various antenna array structures for hybrid massive-MIMO precoding schemes. In particular, the proposed hybrid scheme includes two cascaded stages: the RF-beamforming stage is designed via the eigen-decomposition of the massive-MIMO channel second-order correlation matrix while the baseband multi-user (MU) precoding stage is constructed via the regularized zero-forcing (RZF) technique to mitigating the MU-interference in the reduced-dimension effective MU-channel. A transfer block is introduced between the RF-beamforming and baseband precoding stages to significantly reduce the number of required RF chains. For the same number of antenna elements with half-wavelength spacing, we examine the achieved sum-rate performance of different 2D antenna array structures, namely, uniform linear array (ULA), uniform rectangular array (URA), uniform circular array (UCA), and concentric circular array (CCA), in serving multiple users in various angle-of-departure (AoD) settings. Simulation results indicate that, among the array structures, URA and CCA can offer both smaller array sizes and higher achieved sum-rate. Furthermore, for various user angular locations, the sum-rate of URA can vary about 2 bits/s/Hz while CCA can give an invariant sum-rate performance.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".