A Joint Optimization Algorithm Using Adaptive Minimum Coset Number Based Discrete Multi-Coset Sampling

Jiannan Dong,Zhenfang Fan,Shungang Hua,Hongkun Li,Qiang Zhou

doi:10.1109/access.2020.3023457

Abstract

We address the problem of sparse multi-band signal reconstruction in the case of unknown band position through the discrete multi-coset sampling (DMCS). In this article, the signal has complex frequency components, and the minimum coset number is determined on the assumption that there is only one frequency component with same characteristics. According to the frequency characteristics, we analyze the influence of the parameterized compressed matrix on the two reconstruction algorithms, and get that a single algorithm does not have universal adaptability to different frequency components. In order to solve this problem, under the discrete multi-coset sampling model, a joint optimization algorithm with discriminant factor (DF-JOA) is proposed to identify the different characteristics and automatically select an appropriate algorithm for signal reconstruction, numerical simulation experiments show the effectiveness of the algorithm. We also simulate the reconstruction success ratio of amplitude and the total coset number under different compressed matrices, determine the influence law, and confirm the improvement of signal reconstruction probability by joint optimization algorithm. Our method ensures the spectrum reconstruction of the multi-band signal. This article can guide how to better select the coset parameters under the condition that the channels of the discrete multi-coset sampling system are limited but the minimum coset number can be guaranteed. It will have a great significance to the sub-Nyquist sampling technique.

Highlights

The signal is the carrier of the message and the tool to carry the message, the sampling of the signal is the basic way to obtain the signal information
For the spectrum reconstruction of discrete multi-coset sampling model, the orthogonal matching pursuit (OMP) algorithm and Compressive sampling matching pursuit (CoSaMP) algorithm can be fused to combine the advantages of the two algorithms under different frequencies to form a joint optimization algorithm for discrete multi-coset sampling (DMCS-DF-JOA)
In this article, based on the discrete multi-coset sampling model, we suggest a joint optimization algorithm for identifying multi-band sparse signals, which adapts to the minimum coset number

Summary

INTRODUCTION

The signal is the carrier of the message and the tool to carry the message, the sampling of the signal is the basic way to obtain the signal information. According to the compressive sensing theory, the spectral information of the original signal Nyquist sampling sequence is reconstructed by solving the optimization problem. There are two special cases for the spectrum division matrix YL: (1) There is a multiple relation f0 = nf between the frequency f0 of the original signal and the discrete multi-coset sampling frequency f. What is studied in this article is the minimum number of channels for the reconstruction of the original signal by the discrete multi-coset sampling system, namely p = 4. This weakens the contribution of the 13th column atom corresponding to another non-zero term Their positions in the sparse vector cannot be found accurately, resulting in reconstruction failure

COMPRESSIVE SAMPLING MATCHING PURSUIT ALGORITHM

INTRODUCTION OF ALGORITHM

SIMULATION ANALYSIS

Findings

CONCLUSION