Joint User Selection and Hybrid Precoder Design for Massive MIMO Systems
Bibliographic record
Abstract
Massive multiple-input multiple-output (MIMO) systems are a cornerstone of modern wireless communication, enabling significant improvements in capacity and reliability. However, the joint optimization of user selection and hybrid precoder/decoder design remains challenging due to the complexity introduced by spatial correlation, noisy channel information, and the non-convex nature of the problem. This paper addresses these challenges by considering the downlink of multi-user massive MIMO systems. A noisy version of channel information with spatial correlation between antennas is assumed to be available at the transmitter, and an optimization problem is formulated for joint user selection and hybrid analog/digital precoder design. The total sum rate of the network is considered as a design metric that leads to non-convex and NP-hard mixedinteger optimization. To address the non-convexity, an iterative method is proposed which results in multiple simpler bounding and relaxed convex sub-problems with closed-form solutions for analog precoders/decoders, digital decoders, and user selection. As a by-product, the proposed algorithm also optimizes the number of selected users with perfect or imperfect channel state information (CSI). A generalized user selection metric is also derived for massive MIMO systems with multiple-antenna users under both perfect and imperfect CSI, and is further analyzed for specific scenarios such as ZF, MRT, block diagonalized precoders, and large-scale MIMO settings. Finally, the method is extended to finite-resolution phase shifters and assessed for Rayleigh fading channels. The simulation results show that the proposed method performs favorably compared to other recent joint user selection and precoder designs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".