scholarly journals Data-Driven and Model-Driven Joint Detection Algorithm for Faster-Than-Nyquist Signaling in Multipath Channels

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 257
Author(s):  
Xiuqi Deng ◽  
Xin Bian ◽  
Mingqi Li
Keyword(s):  
Signal Detection ◽  
Channel Model ◽  
Additive White Gaussian Noise ◽  
Detection Algorithm ◽  
Multipath Channels ◽  
Spectrum Efficiency ◽  
Data Driven ◽  
Joint Detection ◽  
Model Driven ◽  
High Compression

In recent years, Faster-than-Nyquist (FTN) transmission has been regarded as one of the key technologies for future 6G due to its advantages in high spectrum efficiency. However, as a price to improve the spectrum efficiency, the FTN system introduces inter-symbol interference (ISI) at the transmitting end, whicheads to a serious deterioration in the performance of traditional receiving algorithms under high compression rates and harsh channel environments. The data-driven detection algorithm has performance advantages for the detection of high compression rate FTN signaling, but the current related work is mainly focused on the application in the Additive White Gaussian Noise (AWGN) channel. In this article, for FTN signaling in multipath channels, a data and model-driven joint detection algorithm, i.e., DMD-JD algorithm is proposed. This algorithm first uses the traditional MMSE or ZFinear equalizer to complete the channel equalization, and then processes the serious ISI introduced by FTN through the deepearning network based on CNN or LSTM, thereby effectively avoiding the problem of insufficient generalization of the deepearning algorithm in different channel scenarios. The simulation results show that in multipath channels, the performance of the proposed DMD-JD algorithm is better than that of purely model-based or data-driven algorithms; in addition, the deepearning network trained based on a single channel model can be well adapted to FTN signal detection under other channel models, thereby improving the engineering practicability of the FTN signal detection algorithm based on deepearning.

scholarly journals Detection of weak multi-target with adjacent frequency based on chaotic system

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771989024
Author(s):  
Dawei Chen ◽  
Shuo Shi ◽  
Xuemai Gu ◽  
Byonghyo Shim ◽  
Qianyao Ren
Keyword(s):  
Signal Detection ◽  
Target Detection ◽  
Narrow Band ◽  
Detection System ◽  
Additive White Gaussian Noise ◽  
Melnikov Method ◽  
Detection Algorithm ◽  
Weak Signal Detection ◽  
Narrow Band Noise ◽  
Band Noise

As a promising technology in signal detection, the chaotic detection system can significantly improve the accuracy of weak signal detection in strong background noise. It benefits from its characteristics of the sensitivity to the initial condition and the immunity to the Additive White Gaussian Noise. However, the fundamental challenges of the existing chaotic detection system are the sensitivity to narrow-band noise and the influences of multi-target detection with adjacent frequency, which bring great difficulties in the real application. To address these problems, in this article, we focus on the weak multi-target detection with adjacent frequency under the narrow-band noise, and a novel chaotic detection system that integrates the detection algorithm based on period-chaos duration ratio is proposed. In order to enhance the robustness to narrow-band noise, the Melnikov method is used to analyze the Duffing difference system. To realize the detection of weak multi-target with adjacent frequency, we proposed the detection system using the rule named general critical state. Furthermore, simulation results corroborate that the proposed system based on period-chaos duration ratio can achieve satisfactory performance in terms of the weak multi-target detection under narrow-band noise, and it is well investigated by extensive simulation for testing its effectiveness.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

scholarly journals Joint UAV channel modeling and power control for 5G IoT networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiuhua Fu ◽  
Tian Ding ◽  
Rongqun Peng ◽  
Cong Liu ◽  
Mohamed Cheriet
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Channel Model ◽  
Path Loss ◽  
Channel Modeling ◽  
Signal Propagation ◽  
Spectrum Efficiency ◽  
Propagation Path ◽  
Control Factor ◽  
The Impact

AbstractThis paper studies the communication problem between UAVs and cellular base stations in a 5G IoT scenario where multiple UAVs work together. We are dedicated to the uplink channel modeling and the performance analysis of the uplink transmission. In the channel model, we consider the impact of 3D distance and multi-UAVs reflection on wireless signal propagation. The 3D distance is used to calculate the path loss, which can better reflect the actual path loss. The power control factor is used to adjust the UAV's uplink transmit power to compensate for different propagation path losses, so as to achieve precise power control. This paper proposes a binary exponential power control algorithm suitable for 5G networked UAV transmitters and presents the entire power control process including the open-loop phase and the closed-loop phase. The effects of power control factors on coverage probability, spectrum efficiency and energy efficiency under different 3D distances are simulated and analyzed. The results show that the optimal power control factor can be found from the point of view of energy efficiency.

An automatic seismic signal detection algorithm based on the Walsh transform

1981 ◽  
Vol 71 (4) ◽  
pp. 1351-1360
Author(s):  
Tom Goforth ◽  
Eugene Herrin
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Seismic Signal ◽  
Detection Algorithm ◽  
Machine Language ◽  
Walsh Transform ◽  
Time Signal ◽  
Data Systems ◽  
Detection Capability ◽  
Short Period

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.

The influence of sampling frequency and interpolation on the bit error rate of GMSK signal

2021 ◽  
pp. 108-114
Author(s):  
D.D. Privalov
Keyword(s):  
Signal Detection ◽  
Bit Error Rate ◽  
Error Rate ◽  
Clock Synchronization ◽  
Additive White Gaussian Noise ◽  
Sampling Rate ◽  
Digital Signal ◽  
Sampling Frequency ◽  
Synchronization Error ◽  
Minimum Number

The sampling rate at a given bit rate is a requirement for the speed of digital signal processors. In this regard, it is necessary to strive to reduce it in the development of electronic devices, especially portable ones. However, this can lead to an increase in the bit error rate during signal detection. Therefore, it is important to determine the degradation of signal detection with decreasing sampling frequency and to develop practical recommendations to ensure the specified quality of communication. The aim of the article is to study the influence of sampling frequency and interpolation on the bit error rate of GMSK Signal. The article considers the incoherent detection of a GMSK signal in a channel with additive white Gaussian noise, taking into account the influence of the clock synchronization error. Numerical results are presented that characterize an increase in the bit error rate with a decrease in the signal sampling frequency. It is shown that when using the cubic Farrow interpolator, there is no significant degradation in the bit error probability. The minimum number of samples per symbol is determined, at which the bit error rate is close to the theoretical values in the absence of synchronization error. The presented results can be used in development of wireless data transmission systems.

scholarly journals Fast inverse lithography based on dual-channel model-driven deep learning

2020 ◽  
Vol 28 (14) ◽  
pp. 20404 ◽  
Author(s):  
Xu Ma ◽  
Xianqiang Zheng ◽  
Gonzalo R. Arce
Keyword(s):  
Deep Learning ◽  
Channel Model ◽  
Model Driven ◽  
Dual Channel
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