Short-Boundary Signaling for Asynchronous Grant-Free Control-to-Control Communications
Abstract
In asynchronous grant-free access, receivers must detect variable-length transmissions and identify their start and end. Existing decoder-aided receivers use start and tail sequences which are as long as one codeword, causing substantial overhead for short commands. This paper treats the boundary (i.e., start and tail sequences) length as an independent design parameter and proposes a convolutional neural network that detects both boundaries from the received waveform and the decoder soft output. Its waveform input size decreases with the boundary length, while the decoder input keeps the codeword length, so the boundary can be shortened without altering the decoder. Analysis and simulations quantify the resulting tradeoff among payload efficiency, collision exposure, and boundary detection. The results show that the proposed receiver can exploit very short boundaries: in the considered setting, the shortest tested boundary, Lb=16, yields the highest effective payload efficiency at all tested loads, whereas a correlation-based benchmark with the same sequences performs best with Lb=64 at moderate and high loads.
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BibTeX
@misc{battaglioni2026shortboundary,
title = {{Short-Boundary Signaling for Asynchronous Grant-Free Control-to-Control Communications}},
author = {Massimo Battaglioni and Dania De Crescenzo and Edoardo Carnevali and Enrico Paolini},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21729},
eprint = {2610.21729},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21729},
}
RIS
TY - UNPB
TI - Short-Boundary Signaling for Asynchronous Grant-Free Control-to-Control Communications
AU - Massimo Battaglioni
AU - Dania De Crescenzo
AU - Edoardo Carnevali
AU - Enrico Paolini
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21729
UR - https://arxiv.org/abs/2610.21729
AB - In asynchronous grant-free access, receivers must detect variable-length transmissions and identify their start and end. Existing decoder-aided receivers use start and tail sequences which are as long as one codeword, causing substantial overhead for short commands. This paper treats the boundary (i.e., start and tail sequences) length as an independent design parameter and proposes a convolutional neural network that detects both boundaries from the received waveform and the decoder soft output. Its waveform input size decreases with the boundary length, while the decoder input keeps the codeword length, so the boundary can be shortened without altering the decoder. Analysis and simulations quantify the resulting tradeoff among payload efficiency, collision exposure, and boundary detection. The results show that the proposed receiver can exploit very short boundaries: in the considered setting, the shortest tested boundary, L_b=16, yields the highest effective payload efficiency at all tested loads, whereas a correlation-based benchmark with the same sequences performs best with L_b=64 at moderate and high loads.
ER -