Scheduling Versus Contention for Massive Random Access in Massive MIMO Systems
Bibliographic record
Abstract
Massive machine-type communications protocols have typically been designed under the assumption that coordination between users requires significant communication overhead and is thus impractical. Recent progress in efficient activity detection and collision-free scheduling, however, indicates that the cost of coordination can be much less than the naive scheme for scheduling. This work considers a scenario in which a massive number of devices with sporadic traffic seek to access a massive multiple-input multiple-output (MIMO) base-station (BS) and explores an approach in which device activity detection is followed by a single common feedback broadcast message, which is used both to schedule the active users to different transmission slots and to assign orthogonal pilots to the users for channel estimation. The proposed coordinated communication scheme is compared to two prevalent contention-based schemes: coded pilot access, which is based on the principle of coded slotted ALOHA, and an approximate message passing scheme for joint user activity detection and channel estimation. Numerical results indicate that scheduled massive access provides significant gains in the number of successful transmissions per slot and in sum rate, due to the reduced interference, at only a small cost of feedback.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".