Sub-Connected Hybrid Precoding Architectures in Massive MIMO Systems
Bibliographic record
Abstract
Hybrid Precoding (HP) has been introduced to reduce the complexity/costs due to a large number of RF chains in the fully-digital massive MIMO precoding. In a fully-connected (FC) HP, each RF chain is connected to all available antenna elements to exploit the full beamforming capability of the antenna array at the expense of large connectivity. To further reduce costs/complexity associated with this large connectivity, sub-connected (SC) HP considers that each RF chain connected to a subset of selected antenna elements of the antenna array at the costs of inferior performance. This paper aims to study the complexity and performance of both FC-HP and SC-HP. For comparison, we consider a common 2-stage HP scheme with the RF-beamforming (BF) stage designed via the slow time-varying angle-of-departure (AoD) information, using both orthogonal and non-orthogonal BF approaches, while the baseband-precoding stage uses a regularized zero-forcing (RZF) technique. This common HP scheme is used by a base-station equipped with a uniform rectangular large-scale antenna-array to serve multiple single-antenna users clustered in multiple groups. Three sub-array configurations (vertical, horizontal, square) are considered for SC- HP. Illustrative simulation results are provided to compare the performance of FC-HP and SC-HP in various scenarios and indicate that for a 64-element URA and 4 RF chains, SC-HP can achieve 91.46% sum-rate performance of FC-HP with only 25% complexity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".