Spatial Clustering in Slotted ALOHA Two-Hop Random Access for Machine Type Communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The LTE random access procedures proposed in 3GPP for Machine Type Communication in current cellular systems may become overwhelmed when too many machine devices attempt to upload their data. In this paper, we propose a two-hop cluster random access based on slotted ALOHA communication. In each cluster, a cluster head (CH) is selected according to the channel gains. The CH aggregates data from cluster members and then initiates the LTE random access procedure to the base station. Due to the offloading from the random access channel to the slotted ALOHA, the number of contending devices is reduced, which alleviates the collision problem and results in better performance. The simplification of access procedure can also significantly decrease the energy consumption. We define a clustering metric for machine locations and we examine the impact of the metric on the performance. The simulation results show that as machine locations become more clustered, the overall performance improves.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it