Robust Channel Estimation and Scheduling for Heterogeneous Multiuser Massive MIMO Systems
Bibliographic record
Abstract
We consider a correlated multiuser (MU) massive multiple-input multiple-output (MIMO) downlink channel in which many heterogeneous users have different channel qualities (i.e., different path-losses) to a base station (BS) equipped with a large antenna array. Using the theory of extreme values of regularly varying functions, we characterize the scaling laws of the achievable sum-rate of the system, when both numbers of BS antennas and users grow large. We then prove that for a large number of users, a simple user scheduling that chooses the users with the largest instantaneous channel vector norms based on the global channel state information (CSI) can significantly improve the achievable system sum-rate. Finally, since the scheduling method needs the global CSI estimate to operate, we propose an efficient algorithm based on low-rank matrix approximation to estimate the global CSI with a moderate number of training signals. Analysis and numerical simulations show that the proposed scheme provides favourable results in terms of system sum-rate performance and computational complexity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".