Joint Resource Allocation for Linear Precoding in Downlink Massive MIMO Systems
Bibliographic record
Abstract
We study joint proportional-fair (PF) resource allocation (RA), including user selection, linear precoding design, power optimization, and modulation and coding scheme selection, in a single-cell downlink massive MIMO (m-MIMO) system over consecutive time-slots when taking per-antenna power constraints (PAPCs) into account. We formulate the general PF joint RA optimization problem as a weighted sum-rate maximization problem at each time-slot and develop a solution technique to obtain a quasi-optimal feasible solution via the introduction of auxiliary variables and a carefully chosen approximation of the spectral-efficiency function. To obtain results for larger settings (i.e., larger number of antennas and users), we propose an approximation to the general problem that yields quasi-optimal feasible solutions. Moreover, we consider state-of-the-art linear precoding techniques and propose a general heuristic RA scheme that takes PAPCs into account. Numerical results show that PAPCs have significant impact on performance even for a very large number of antennas, and that the best existing linear precoding technique, RZFT (regularized zero-forcing transmission) performs very well when RA is performed carefully as long as the PAPCs are not tight. However, RZFT is far from optimal under tight PAPCs, which highlights the need for practical PAPC-aware precoding techniques in this regime.
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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".