Resource Allocation for the Uplink of a Multi-User Massive MIMO System
Bibliographic record
Abstract
We study the uplink resource management of a multi-user multiple-input-multiple-output single cell for Zero-Forcing receive combining transmission. We consider jointly power allocation, user selection and modulation and coding scheme selection over multiple subchannels. Our contributions are twofold: we first propose a quasi-optimal offline algorithm that provides a target performance and then design and validate an efficient online proportional fair algorithm that performs the above steps. Due to user power constraints, the offline optimization is conducted jointly for all subchannels within a time slot, a computationally intensive task, prompting the proposal of a greedy offline algorithm that we validate in two ways: 1) for a small number of users, by solving the general problem to quasi-optimality and 2) for a larger number of users, by solving again to quasi-optimality a transformed version of the general problem when the channels are assumed flat. From the offline study, we find that, given the right user selection, equal power allocation can be employed without much degradation in performance. We also see that the number of channels allocated to users varies widely depending upon their channel gains. Using these insights, we propose our efficient real-time online algorithm that has runtime competitiveness with a state-of-the-art benchmark.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".