Beyond Single-User Scheduling: Exploiting Massive MIMO for Concurrent Data Delivery With Minimum Age of Information
Bibliographic record
Abstract
With the emergence of real-time applications, modern wireless networks have witnessed the use of Age of Information (AoI) as a critical metric for evaluating the timeliness of data delivery. This paper considers multi-user scheduling, extending beyond traditional single-user scheduling, to exploit the potential of Massive Multiple-Input Multiple-Output (mMIMO) systems for concurrent data delivery over imperfectly known channel state information (CSI). We propose a novel transmission scheduling framework that leverages the spatial multiplexing capabilities of mMIMO to minimize the AoI across multiple users. This results in a joint optimization of multi-user scheduling and power allocation problem for optimum data freshness in a wireless broadcast network. We handle the non-convexity of the resulting problem by utilizing successive convex approximation to specifically reformulate the binary/integer and non-convex constraints of the problem. Extensive simulations demonstrate superior performance of the proposed framework and its solution in terms of AoI compared to existing benchmarks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".