论文标题
在频率选择性雷利（Rayleigh）下淡出的设备活动检测
Device Activity Detection for Massive Grant-Free Access Under Frequency-Selective Rayleigh Fading
论文作者
论文摘要
不幸的是，在频率选择性褪色下，设备活动检测和通道估计是大规模无授予访问的问题。本文旨在应对挑战。具体而言，我们提出了一个基于一个M-Antenna基站（BS），n个单一Antenna物联网（IoT）设备和P Channel Taps的正交频施加多路复用（OFDM）的宽带系统的大规模拨款访问方案。在频率选择性雷利褪色下，我们为接收的飞行员信号获得了两个不同但同等的模型。基于每个模型，我们将设备活动检测作为非凸的最大似然估计（MLE）问题，并提出了使用最佳技术获得固定点的迭代算法。提出的两种基于MLE的方法具有相同的计算复杂度O（NPL^2），而不论M，并且当p = 1时降解为现有的基于MLE的设备活动检测方法。基于接收到的飞行员信号的派生模型之一，可以轻松地将常规的通道估计方法用于在频率选择性雷利褪色下检测到的活动设备的通道估计。数值结果表明，这两种提出的方法具有不同的优选系统参数，并相互补充，以在频率选择性雷利褪色下提供有希望的设备活动检测设计。
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 degrade to the existing MLE-based device activity detection method when P=1. Conventional channel estimation methods can be readily applied for channel estimation of detected active devices under frequency-selective Rayleigh fading, based on one of the derived models for the received pilot signals. Numerical results show that the two proposed methods have different preferable system parameters and complement each other to offer promising device activity detection design for grant-free massive access under frequency-selective Rayleigh fading.
