Guest Editorial Massive Access for 5G and Beyond—Part I
Bibliographic record
Abstract
Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. In the past few years, it has received considerable attention in academia and industry. This Special Issue (SI) of the IEEE Journal on Selected Areas in Communications (JSAC) on Massive Access for 5G and Beyond contains the latest results of researchers, industry practitioners, and individuals working on related research problems. Due to the extremely high response to the Call for Papers, this SI is split into two parts. The first part includes a guest editor-authored survey paper and 17 technical papers focusing on access models and access protocols, while the second part contains 18 papers focusing on access techniques and coverage enhancement approaches. We sincerely thank the authors, reviewers, JSAC staffs, and the Senior Editor, Prof. Wayne Stark, for their effort and time in preparing this SI.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".