User Grouping and Hybrid RF/Baseband Precoding for Multi-User Massive MIMO Systems
Bibliographic record
Abstract
In this paper, we investigate hybrid precoders for massive MIMO systems with the objective to reduce the number of radio-frequency (RF) chains. The users are first partitioned into groups that share similar angles of departure (AoDs) to design an angular-based RF-beamforming stage. Then a multi-user-Multiple Input Multiple Output (MIMO) baseband precoding stage is developed with reduced channel state information (CSI) dimension in massive MIMO systems. We develop a systematic approach to simultaneously determine the number of groups and to cluster the users accordingly. The partition is done by minimizing the inter-group interference using a similarity graph. We also incorporate a simple transfer block between the digital precoding and RF-beamforming stages to reduce the number of RF chains in massive MIMO systems. For a sufficiently large number of antennas and over a wide range of signal-to-noise ratio (SNR), the proposed hybrid RF/baseband precoding schemes can offer a sum-rate performance approaching that of the single-stage full digital optimal precoder, while keeping the number of RF chains equal to the number of users. Next, we propose a simple way to design the RF and baseband digital precoding stages in the case of multiple scatterings to fully exploit the multiple reflections that occur in dense urban environments. The proposed hybrid 2-stage RF/baseband schemes significantly outperform other hybrid RF/baseband precoding schemes using orthogonal multiplexing.
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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) | 0.001 | 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 | 0.001 | 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".