A Deep Learning-based Receiver for Asynchronous Grant-Free Random Access in Control-to-Control Networks

2026-05-12

AI Summary

This work addresses the challenges of multi-user signal overlap and command unit boundary detection in asynchronous, grant-free random access for indoor controller-to-controller (C2C) networks. The paper proposes a deep learning-based receiver architecture that, for the first time, employs a convolutional neural network (CNN) to jointly detect preamble and tail sequences in an end-to-end manner. It innovatively enhances tail detection accuracy by integrating LDPC decoding soft information with channel estimates, and combines this with successive interference cancellation (SIC) to enable efficient multi-user decoding. Under high-load and uncoordinated transmission conditions, the proposed method significantly improves boundary detection accuracy and achieves an extremely low end-to-end packet loss rate.

Abstract

In this paper, we study grant-free, asynchronous control-to-control (C2C) communications in an indoor scenario with a shared wireless channel. Each communication node transmits command units, each consisting of a variable-length low-density parity-check (LDPC)--coded payload preceded by a start sequence and followed by a tail sequence. Due to the asynchronous nature of the access, transmissions from different nodes are not aligned over time. As a result, each receiving controller observes the superposition of multiple command units transmitted by different nodes over a receiver-defined superframe interval. Each node transmits one or more replicas of the same command unit. We propose a receiver architecture in which the detection of command unit boundaries (start/tail sequences) is carried out by a single convolutional neural network (CNN) operating directly on the received signal. We show that, while start-sequence detection must rely only on the received waveform, tail-sequence detection can additionally exploit the soft information produced by the LDPC decoder, together with channel estimates. Finally, once commands units are successfully decoded, successive interference cancellation (SIC) can be applied. Simulation results demonstrate that the receiver we propose achieves reliable packet-boundary identification and a low end-to-end packet loss rate, even under uncoordinated and high-traffic operating conditions.

Problem

• grant-free
• asynchronous random access
• control-to-control networks
• packet boundary detection
• superposition signals

Innovation

• deep learning-based receiver
• asynchronous grant-free access
• convolutional neural network (CNN)
• LDPC soft information
• successive interference cancellation (SIC)