Aggregate Preamble Sequence Design and Detection for Massive IoT With Deep Learning
Bibliographic record
Abstract
Massive Internet of Things (mIoT) is a major use case of the fifth generation (5G) wireless systems. mIoT aims to support a large number of connection requests from IoT devices. However, the conventional Long Term Evolution (LTE) random access procedure hinders the support of mIoT due to the limited number of available preambles. In this paper, we propose to aggregate two Zadoff-Chu preamble sequences from two different roots to obtain a larger set of preambles by considering all possible combinations of preamble sequence pairs. Decoding the aggregate preambles is challenging because the receiver needs to decode two preamble sequences where each one is allocated half of the transmit power. We propose two receiver architectures for preamble decoding. The first one is a threshold-based receiver which only requires minor changes to the LTE preamble receiver architecture. The second proposed preamble decoder architecture exploits a deep neural network. Simulations show that the proposed aggregate preamble design results in a lower service time for backlogged IoT devices compared to existing collision avoidance techniques. Moreover, the proposed receiver architectures can decode the aggregate preambles with low probabilities of misdetection and false alarm (less than 11%), especially in the high signal-to-noise ratio (SNR) regime.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".