A Blind Separation of Variable Speed Frequency Hopping Signals based on Independent Component Analysis

et al. 2022

Preprint

It has been challenging to separate the time-frequency (TF) overlapped wireless communication signals with unknown number of sources in underdetermined cases. To address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterwards, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. In this paper, the simulations are performed with digital signals of the same modulation and different modulation respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the benchmark algorithms.

Section: Introductionmentioning

confidence: 99%

An underdetermined convolutional blind separation algorithm for time-frequency overlapped wireless communication signals with unknown source number

et al. 2022

Preprint

“…As a kind of front-end technology in the field of signal processing, the BSS is of great importance in communication anti-jamming and improving spectrum utilization [1]. Unfortunately, the classical independent component analysis (ICA) and many extended algorithms [2][3][4] are based on the main assumption that the number of observed signals is greater than or equal to the number of source signals, which cannot realize the blind signal separation in underdetermined cases [5]. In practical communication scenarios, the number of observed signals is usually less than the number of source signals on account of system cost or environmental constraints, thus the study of underdetermined blind source separation (UBSS) is of more practical significance.…”

Section: Introduction 1research Background and Motivationmentioning

confidence: 99%

An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number

et al. 2022

Applied Sciences

It has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterward, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. The simulations are performed with digital signals of the same modulation and different modulation, respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the comparison algorithms.

Research on Single Antenna Co-frequency Mixed Signal Separation Based on Improved EFICA Algorithm

Ren

Zhong

et al. 2020

J. Phys.: Conf. Ser.

Signal separation under the condition of single antenna reception is a process of estimating the source signal component by using one-dimensional observation signal vector. Under the condition of single antenna receiving the same-frequency mixed signal, in view of the problem that the stability of the EFICA algorithm will be affected by the selection of a random initial iterative matrix, it is proposed to use the steepest descent method to select a suitable iterative initial matrix to improve the EFICA algorithm. An experimental comparison between the EFICA algorithm and the EFICA improved algorithm is presented. The simulation results show that the improved EFICA algorithm can achieve a better separation effect for the separation of mixed signals at the same frequency.