Two-Timescale Hybrid RF-Baseband Precoding With MMSE-VP for Multi-User Massive MIMO Broadcast Channels
Bibliographic record
Abstract
This paper explores joint design of two-timescale hybrid RF-baseband precoding with minimum-mean-square-error (MMSE)-vector perturbation (VP) for multi-user massive multiple-input multiple-output systems, where users on the downlink are separated into geographical clusters, and each user cluster experiences identical transmit spatial correlation. Considering the perfect effective channel state information-based MMSE-VP at baseband, the spatial correlation-based RF precoder design is formulated as orthonormality-constrained stochastic optimization problems, where the objective functions cannot be characterized in closed form. RF eigen-beamforming is shown as an optimal solution for single-cluster transmission. In multi-cluster scenarios, mathematically tractable lower bounds are proposed and numerically optimized by trust-region Newton methods on Riemannian manifolds. Additionally, constant-modulus RF precoding based on the discrete Fourier transform (DFT) codebook is addressed. By recognizing the objective functions as a difference of increasing functions, branch-reduce-and-bound techniques are developed to find the globally optimal solutions to such combinatorial problems with reduced computational complexity. Simulation results demonstrate that the proposed nonlinear hybrid schemes deliver a superior bit error rate to other state-of-the-art baselines. The effectiveness of the suboptimal DFT-based RF solutions is also verified.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".