A Grant-Free Random Access Process for Low-End Distribution System Using Deep Neural Network
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Bibliographic record
Abstract
With the rising number of Internet of Things (IoT) devices joining the communication network, data exchange is increased tremendously resulting in network congestion. This paper deals with the optimal transmission of IoT devices to maximize the chances of success in random access procedures. With every machine trying to use the network for the transfer of data, IoT devices pose serious challenges to the already deployed infrastructure network. With a huge number of IoT devices and fixed limited resources, the existing handshaking-based random access process is not effective. To address this research gap, we propose a grant-free procedure while considering orthogonal transmission and devise a strategy to minimize collisions and idle events and maximize success. We use deep neural networks (DNN) that take channel conditions as an input to predict the device’s transmission for a successful maximization. In order to evaluate the performance of our proposed algorithm, we calculated the average delay with respect to channel coefficient and arrival rate in addition to the number of successes against the channel coefficient. Simulation results show that the proposed algorithm performs well and conforms with the claim of a successful maximization.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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