Multihop cellular: from research to systems, standards, and applications [Guest Editorial]
Bibliographic record
Abstract
It has been more than a decade since the multihop cellular network (MCN) architecture was first proposed and analyzed in 2000 [1]. As the transmission range decreases <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> times, the number of simultaneous transmissions and hop count increase by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> times and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> times, respectively, which leads to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> times cellular capacity increase. Fundamental research projects have demonstrated the benefits of MCN in terms of system capacity, service coverage, and network connectivity. Many special issues have been devoted to this stream of research [2–6]. The actual concept behind the MCN architecture could be regarded as a hybrid of mobile ad hoc networks (MANETs) and cellular networks. This concept of "relaying within a cell" also pushed standard bodies to consider solutions embedded with mesh or ad hoc architectures, such as IEEE 802.11s [7], IEEE 802.15.5 [8], and IEEE 802.16j [9]. Now, in the recent standards of the Third Generation Partnership Project (3GPP), Proximity-Based Services (ProSe) [10, 11] related work items also cover the MCN concept. In addition to device-to-device (D2D) direct communications, user equipment (UE)–UE relay and UE–network relay are also supported features. Both infrastructural and infrastructureless architectures are considered. Among many use cases, the most urgent one is public safety. These show that MCN architecture realization is ongoing.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".