scholarly journals DESIGN OF DIGITAL DOWN CONVERTER AND SIGNAL DETECTION TECHNIQUES FOR SOFTWARE DEFINED RADIO

2019 ◽  
Vol 4 (1) ◽  
pp. 98-102
Author(s):  
Narendra N ◽  
Degala Srilakshmi ◽  
Mohankumar V
Keyword(s):  
Signal Detection ◽  
Software Defined Radio ◽  
Detection Techniques ◽  
Digital Down Converter

Blind Signal Detection Techniques for Spectrum Sensing in Satellite Communication

2017 ◽  
pp. 1-48
Author(s):  
Bilal Muhammad Khan ◽  
Rabia Bilal
Keyword(s):  
Correlation Function ◽  
Signal Detection ◽  
Communication Systems ◽  
Satellite Communication ◽  
Spectral Correlation ◽  
Detection Techniques ◽  
Blind Signal ◽  
Modulated Signals ◽  
Spectral Correlation Function ◽  
Frequency Axis

Modulated signals used in communication systems exhibits cyclic periodicity. This is primarily due to sinusoidal product modulators, repeating preambles, coding and multiplexing in modern communication. This property of signals can be analyzed using cyclostationary analysis. SCF (Spectral correlation function) of cyclic autocorrelation (CAF) has unique features for different modulated signals and noise. Different techniques are applied to SCF for extracting features on the basis of which decision of detecting a signal or noise is made. In this chapter, study and analysis of different modulated signals used in satellite communication is presented using SCF. Also comparison of several signal detection techniques is provided on the basis of utilizing unique feature exhibit by a normalized vector calculated on SCF along frequency axis. Moreover a signal detection technique is also proposed which identifies the presence of a signal or noise in the analyzed data within the defined threshold limits.

A comparison of signal detection techniques in the multifocal electroretinogram

2008 ◽  
Vol 117 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Tom Wright ◽  
Josefin Nilsson ◽  
Christina Gerth ◽  
Carol Westall
Keyword(s):  
Signal Detection ◽  
Multifocal Electroretinogram ◽  
Detection Techniques

Raman Spectroscopy Using dc Signal Detection and a Microcomputer: An Alternative Approach

1987 ◽  
Vol 41 (7) ◽  
pp. 1145-1147 ◽  
Author(s):  
E. Neil Lewis ◽  
Ira W. Levin
Keyword(s):  
Signal Detection ◽  
Laser Excitation ◽  
Excitation Power ◽  
Signal Averaging ◽  
Raman Spectroscopic ◽  
Detection Techniques ◽  
Medium Resolution ◽  
Alternative Approach ◽  
Wide Range ◽  
Generic Data

Raman spectroscopic data have been obtained with the use of direct current (dc) signal detection, an IBM PC/AT microcomputer, and commercially available software. Since photomultiplier currents of the order of nanoamps to microamps are readily attained for Raman emission under conditions of moderate laser excitation power levels (150–200 mW) and medium resolution spectral slits (1–4 cm−1), signal levels well within the domain measurable by dc signal detection techniques are achieved for a wide range of chemical and biochemical samples. Further, the digitization and signal averaging capabilities of generic data acquisition boards and microcomputers allow dc detection to yield signal-to-noise ratios competitive with those derived from complementary pulse-counting techniques.

FPGA-Based Design of Resource-Efficient Digital down Converter

2011 ◽  
Vol 128-129 ◽  
pp. 878-881
Author(s):  
Shu Lin Cui ◽  
Xu Li
Keyword(s):  
Signal Processing ◽  
Software Defined Radio ◽  
Resource Consumption ◽  
Distributed Arithmetic ◽  
Multirate Signal Processing ◽  
Communications Systems ◽  
Digital Down Converter ◽  
Xilinx Fpga ◽  
Digital Receivers

Digital down converter (DDC) is based on the theory of Software Defined Radio (SDR) and multirate signal processing, extensively applied in digital receivers of communications systems. An improved resource-efficient DDC with polyphase architecture and distributed arithmetic (DA) is presented in this paper. The design based on Xilinx FPGA Virtex-5 has more flexible characters and higher precision computation with less resource consumption.

scholarly journals Machine Learning for 5G MIMO Modulation Detection

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1556
Author(s):  
Haithem Ben Chikha ◽  
Ahmad Almadhor ◽  
Waqas Khalid
Keyword(s):  
Machine Learning ◽  
Software Defined Radio ◽  
Mimo Systems ◽  
False Positive Rate ◽  
Cognitive Radios ◽  
Machine Learning Techniques ◽  
Destination Node ◽  
Detection Techniques ◽  
Positive Rate ◽  
Modulation Detection

Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.

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