Compressed spectrum sensing for grant-free NOMA based internet of vehicles

Liang Zhang,Zengqi Li,Min Jia

doi:10.1186/s13634-022-00835-w

Abstract

With the development of communication technology, non-orthogonal multiple access (NOMA) technology is proposed to meet the requirements of high throughput and low latency in massive machine type communication (mMTC) of Internet of Vehicles (IoV). In grant-free NOMA based IoV, mMTC has the characteristics of sparse active users at the same time, which makes the detection and recovery of user information critical. In this paper, considering the sparsity of active users in mMTC, we present a new block sparsity method under compressed sensing model that enables us to detect activity of users and recover user information with high accuracy and low complexity. The recovered algorithm used in our study is known as block sparse ISD algorithm, which exploits block sparse structure based on the ISD algorithm. The simulation results show that the proposed method is able to realize more performance gains in sparse signal recovery than traditional algorithm.

Highlights

In recent years, Internet of Vehicles (IoV) has developed rapidly
We propose a new blcok sparse method based on Iterative Support Detection (ISD) algorithm by exploiting the sparsity of user activities in massive Machine Type Communications
Compared with ISD algorithm and SISD algorithm, the proposed method improves the traditional ISD algorithm with the extra block sparse structure, and the support set of each block is the same

Summary

Introduction

Internet of Vehicles (IoV) has developed rapidly. The emergence of 5G IoV has greatly improved the communication performance of IoV by using new multiple access technology. 4G mobile communications adopt orthogonal multiple access (OMA) technology to allocate a single wireless resource block to a user. Each user is assigned a unique pilot sequence in the grant-free NOMA, and the base station first detects the active users according to the pilot sequences and recovers the sparse signals with the reconstruction algorithm [3]. According to the sparsity of active users, the reconstruction problem of the sparse signals can be solved by the reconstruction algorithm of compressed sensing (CS), such as Orthogonal Matching Pursuit (OMP) and Iterative Support Detection (ISD) [4].

Results

Conclusion