Aggregate Preamble Sequence Design for Massive Machine-Type Communications in 5G Networks
Bibliographic record
Abstract
Massive machine-type communications (mMTC) is a major use case in the fifth generation (5G) wireless networks. mMTC aims at supporting a large number of Internet of Things (IoT) connections within a coverage area. The current random access procedure in the Long Term Evolution (LTE) networks may not be able to handle a large number of simultaneous connection requests due to the limited number of random access preambles. Hence, it is essential to modify the random access procedure to support mMTC. In this paper, we propose a new preamble sequence design in which two Zadoff-Chu preamble sequences are aggregated together. This design enables us to have a larger set of random access preambles consisting of all combinations of pairing two Zadoff-Chu preamble sequences. Moreover, we consider a subset of all combinations that satisfy a certain maximum peak-to-average-power-ratio (PAPR) threshold criterion to reduce the energy consumption of the IoT devices. The proposed design requires only minor changes in the conventional transmitter and receiver design for generating and decoding the aggregated preamble sequences, respectively. Results show that the proposed design reduces the probability of preamble collision to less than 10-4, which is lower than LTE. Furthermore, it outperforms other collision avoidance techniques such as access class barring (ACB) in terms of a lower average total service time. The modified receiver detects the aggregated preambles successfully and avoids detecting false preambles. Both the probabilities of misdetection and false alarm are less than 10-3when the signal-to-noise ratio (SNR) is larger than -7 dB.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".