Random Access Process Enhancements for Cellular Internet of Things (CIoT)
Bibliographic record
Abstract
Random Access (RA) is an important process in Cellular Internet of Things (CIoT), which enables the IoT devices to connect to the network for set-up and data transmission. As the number of IoT devices in the network is increasing, the competition for accessing the network increases as well. Additionally, since each IoT application may have different requirements in terms of parameters such as data size, latency, and data rate, it is essential to optimize the random access process to address the requirements of different applications. Therefore, there is a need to design the uplink transmission mechanism to reduce the signaling overhead being exchanged between the User Equipment (UE) and the base station. The 3rd Generation Partnership Project (3GPP) has introduced several random access process enhancements in Release 13 through 16, for IoT devices that have small data size or the ones that not only send small amount of data, but also have a deterministic traffic or a predicated traffic pattern. In this paper, we first discuss the random access process and its advancements and then, explain the applicability of the existing features for some practical IoT use cases. The results of this study shows that RA process developed in release 15 and 16 under certain conditions can increase the efficiency of IoT applications by reducing the latency and power consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".