Energy-Efficient MU-Massive-MIMO Hybrid Precoder Design: Low-Resolution Phase Shifters and Digital-to-Analog Converters for 2D Antenna Array Structures
Bibliographic record
Abstract
This paper investigates a multi-user massive multiple-input multiple-output (MU-mMIMO) hybrid precoding (HP) scheme using low-resolution phase shifters (PSs) and digital-to-analog converters (DACs). The proposed HP approach involves two stages: RF beamforming based on the slowly time-varying channel second-order correlation matrix, and baseband MU precoding based on the instantaneous effective baseband channel to mitigate MU-interference by a regularized zero-forcing (RZF) technique. We consider three HP design architectures: (i) HP using full-resolution PSs and DACs, with a baseband transfer block for constant-modulus RF beamformer, (ii) HP using b-bit PSs and full-resolution DACs, with an orthogonal matching pursuit (OMP) based algorithm that can approach the optimal unconstrained RF beamformer, and (iii) HP using b-bit PSs and q-bit DACs, taking into account also DAC quantization noise. Illustrative results show that the proposed HP schemes with low-resolution PSs can approach the sum-rate of full-resolution PSs by using only 2-bit PSs, while offering higher energy efficiency. Furthermore, a study of sum-rate results for various PS and DAC quantization levels reveals that HP can achieve near-optimal performance with only 2-bit PSs and 5-bit DACs. Moreover, a comparison of the different array configurations, namely, uniform linear array (ULA), uniform circular array (UCA), uniform rectangular array (URA), and concentric circular array (CCA), indicates that URA and CCA outperform UCA and ULA in terms of spectral and energy efficiencies.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".