Low‐complexity hybrid precoding for multi‐user massive MIMO systems: a hybrid EGT/ZF approach
Bibliographic record
Abstract
Massive multiple‐input multiple‐output (MIMO) systems bring manifold improvements in the system spectral efficiency but result in high hardware and processing complexity at the base station. Employing hybrid precoding at the base station can reduce such complexity. In this study, unlike most existing work on hybrid precoding design, the authors consider a sub‐connected analogue combining structure to reduce the complexity. Starting from an important observation on the effect of sequentially designed analogue phased arrays on users' sum rate, the authors develop three low‐complexity hybrid precoding schemes for a multi‐user massive MIMO system. The proposed schemes apply equal gain transmission (EGT) based analogue beamforming to reap the diversity benefit of an analogue phased array and employ zero‐forcing (ZF) beamforming for nullifying inter‐user interference. The authors carry out an extensive computational complexity analysis and simulation study on the proposed schemes. The proposed singular‐value decomposition based EGT scheme outperforms all others but incurs the highest computational burden. On the other hand, the sequential‐EGT scheme is the least computationally intensive scheme but shows the worse performance amongst them.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".