Device Activity Detection for Massive Grant-Free Access Under Frequency-Selective Rayleigh Fading
Yuhang Jia, Ying Cui, and Wuyang Jiang

TL;DR
This paper introduces an OFDM-based scheme for device activity detection in wideband massive grant-free IoT systems under frequency-selective Rayleigh fading, proposing two MLE-based algorithms with comparable complexity.
Contribution
It develops two novel MLE-based detection methods for frequency-selective fading scenarios, extending existing models to improve device activity detection in grant-free massive access.
Findings
Both methods have complexity O(NPL^2)
They perform well under different system parameters
Methods complement each other for robust detection
Abstract
Device activity detection and channel estimation for massive grant-free access under frequency-selective fading have unfortunately been an outstanding problem. This paper aims to address the challenge. Specifically, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme for a wideband system with one M-antenna base station (BS), N single-antenna Internet of Things (IoT) devices, and P channel taps. We obtain two different but equivalent models for the received pilot signals under frequency-selective Rayleigh fading. Based on each model, we formulate device activity detection as a non-convex maximum likelihood estimation (MLE) problem and propose an iterative algorithm to obtain a stationary point using optimal techniques. The two proposed MLE-based methods have the identical computational complexity order O(NPL^2), irrespective of M, and…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Distributed Sensor Networks and Detection Algorithms · Sparse and Compressive Sensing Techniques
