Activity Detection for Grant-Free NOMA in Massive IoT Networks

TL;DR

This paper introduces a CNN-based activity detection method for grant-free NOMA in massive IoT networks, effectively handling unknown and time-varying activity rates to improve detection accuracy and efficiency.

Contribution

It proposes a novel deep learning approach for activity detection that outperforms existing methods and does not require prior knowledge of activity rates.

Findings

CNN-AD achieves higher detection accuracy than existing methods.

The method effectively handles unknown and time-varying activity rates.

Convexity analysis enables optimal threshold determination.

Abstract

Recently, grant-free transmission paradigm has been introduced for massive Internet of Things (IoT) networks to save both time and bandwidth and transmit the message with low latency. In order to accurately decode the message of each device at the base station (BS), first, the active devices at each transmission frame must be identified. In this work, first we investigate the problem of activity detection as a threshold comparing problem. We show the convexity of the activity detection method through analyzing its probability of error which makes it possible to find the optimal threshold for minimizing the activity detection error. Consequently, to achieve an optimum solution, we propose a deep learning (DL)-based method called convolutional neural network (CNN)-activity detection (AD). In order to make it more practical, we consider unknown and time-varying activity rate for the IoT devices. Our simulations verify that our proposed CNN-AD method can achieve higher performance compared to the existing non-Bayesian greedy-based methods. This is while existing methods need to know the activity rate of IoT devices, while our method works for unknown and even time-varying activity rates