BANDWIDTH OF THE RECEIVER OF DIGITAL SIGNALS WITH A LARGE DYNAMIC RANGE

2021 ◽  
Vol 0 (11) ◽  
Author(s):  
A. E. Denisov ◽  
◽  
D. P. Danilaev ◽  
G. I. Il'in ◽  
◽  
...  
Keyword(s):  
Communication Channel ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Digital Signals ◽  
Resolution Time ◽  
Large Dynamic Range ◽  
Signal To Noise ◽  
Radio Engineering ◽  
Channel Parameters ◽  
Analytical Relations

The analysis of the connection between the bandwidth and the dynamic range, the signal-to-noise ratio, the resolution time and the bandwidth is carried out. The problems are solved by deriving analytical dependencies based on the Shannon – Hartley theorem, as well as the well-known postulates of the theory of radio engineering circuits and signals. The study of analytical relations allows us to identify restrictions on the choice of communication channel parameters.

An Over 120dB Dynamic Range Linear Response Single Exposure CMOS Image Sensor with Two-stage Lateral Overflow Integration Trench Capacitors

2020 ◽  
Vol 2020 (7) ◽  
pp. 143-1-143-6 ◽  
Author(s):  
Yasuyuki Fujihara ◽  
Maasa Murata ◽  
Shota Nakayama ◽  
Rihito Kuroda ◽  
Shigetoshi Sugawa
Keyword(s):  
Linear Response ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Signal To Noise ◽  
Single Exposure ◽  
Two Stage ◽  
Noise Ratio ◽  
Maximum Signal

This paper presents a prototype linear response single exposure CMOS image sensor with two-stage lateral overflow integration trench capacitors (LOFITreCs) exhibiting over 120dB dynamic range with 11.4Me- full well capacity (FWC) and maximum signal-to-noise ratio (SNR) of 70dB. The measured SNR at all switching points were over 35dB thanks to the proposed two-stage LOFITreCs.

scholarly journals Joint estimation of channel parameters for very low signal-to-noise ratio environment in mobile radio propagations

Radio Science ◽  
2010 ◽  
Vol 45 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jingyu Hua ◽  
Limin Meng ◽  
Gang Li ◽  
Dongming Wang ◽  
Bin Sheng ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Mobile Radio ◽  
Joint Estimation ◽  
Signal To Noise ◽  
Channel Parameters ◽  
Noise Ratio

scholarly journals Signal-to-noise optimization and evaluation of a home-made visible diode-array spectrophotometer

1993 ◽  
Vol 15 (6) ◽  
pp. 227-232 ◽  
Author(s):  
Ivo M. Raimundo, Jr. ◽  
Celio Pasquini
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Optical Design ◽  
Signal To Noise ◽  
Noise Optimization ◽  
Instrument Control ◽  
Automatic Methods ◽  
Photodiode Array ◽  
Variable Integration

This paper describes a simple low-cost multichannel visible spectrophotometer built with an RL512G EGG-Reticon photodiode array. A symmetric Czerny-Turner optical design was employed; instrument control was via a single-board microcomputer based on the 8085 Intel microprocessor. Spectral intensity data are stored in the single-board's RAM and then transferred to an IBM-AT 3865X compatible microcomputer through a RS-232C interface. This external microcomputer processes the data to recover transmittance, absorbance or relative intensity of the spectra. The signal-to-noise ratio and dynamic range were improved by using variable integration times, which increase during the same scan; and by the use of either weighted or unweighted sliding average of consecutive diodes. The instrument is suitable for automatic methods requiring quasi-simultaneous multiwavelength detections, such as multivariative calibration and flow-injection gradient scan techniques.

scholarly journals Special features of radio pulse spectral density analysis

2020 ◽  
Vol 27 (4) ◽  
pp. e103
Author(s):  
Andrew Chubykalo ◽  
Augusto Espinoza ◽  
Victor Kuligin
Keyword(s):  
Spectral Density ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Radio Pulse ◽  
High Signal ◽  
Large Dynamic Range ◽  
High Duty Cycle ◽  
Radio Signals ◽  
Large Period ◽  
Density Analysis

The spectrum analysis of the periodic sequence radio pulses is often described in textbooks. However, if this method is applied to short radio pulses with a large period between them, then large errors occur. In this article, we described a new method of pulse gating. This method allows us to measure the spectral density of radio signals with high duty cycle. The main advantages of our method are a high signal-to-noise ratio, a large dynamic range of measurements, and a higher accuracy of spectral density measurements.

scholarly journals Estimation and Correction of Sampling Time Offsets in Tiadcs using Optimization Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1217-1222
Keyword(s):  
Optimization Algorithm ◽  
High Frequency ◽  
Performance Metrics ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Sampling Time ◽  
Signal To Noise ◽  
High Sampling ◽  
Noise Ratio ◽  
Estimation And Correction

In recent communication technologies, very high sampling rates are required for rf signals particularly for signals coming under ultra high frequency (UHF), super high frequency (SHF) and extremely high frequency (EHF) ranges. The applications include global positioning system (GPS), satellite communication, radar, radio astronomy, 5G mobile phones etc. Such high sampling rates can be accomplished with time-interleaved analog to digital converters (TIADCs). However, sampling time offsets existing in TIADCs produce non-uniform samples. This poses a drawback in the reconstruction of the signal. The current paper addresses this drawback and offers a solution for improved signal reconstruction by estimation and correction of the offsets. A modified differential evolution (MDE) algorithm, which is an optimization algorithm, is used for estimating the sampling time offsets and the estimated offsets are used for correction. The estimation algorithm is implemented on an FPGA board and correction is implemented using MATLAB. The power consumption of FPGA for implementation is 57mW. IO utilization is 27% for 4-channel TIADCs and 13% for 2-channel TIADCs. The algorithm estimated the sampling time offsets precisely. For estimation the algorithm uses a sinusoidal signal as a test signal. Correction is performed with sinusoidal and speech signals as inputs for TIADCs. Performance metrics used for evaluating the algorithm are SNR (signal to noise ratio), SNDR (signal to noise and distortion ratio), SFDR (spurious-free dynamic range) and PSNR (peak signal to noise ratio). A noteworthy improvement is observed in the above mentioned parameters. Results are compared with the existing state of the art algorithms and superiority of the proposed algorithm is verified.

scholarly journals An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems

2020 ◽  
Vol 10 (22) ◽  
pp. 8022
Author(s):  
Desheng Wang ◽  
Yangjie Wei ◽  
Yi Wang ◽  
Jing Wang
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Measurement Methods ◽  
Performance Difference ◽  
Critical Factors ◽  
Signal To Noise ◽  
Measurement Condition ◽  
Actual Application ◽  
The Impact

The dynamic range (DR) index lacking of an official definition leads to ambiguities in performance evaluation. The existing measurement methods of DR do not always match with the various actual application conditions, and some detailed distortion behavior of the device under test (DUT) is not extracted. In this paper, a new index for evaluating the DR performance of audio systems is proposed and validated, herein referred to as the audio distortion dynamic range (ADDR). It reduces the uncertainty of measurement conditions by an explicit definition and unifies the signal-to-noise ratio (SNR) and the signal-to-noise-and-distortion ratio (SINAD) indexes if under the same measurement condition. Moreover, to comprehensively reflect the impact of harmonic, spurious, and noise components on the DUT, the definitions of the traditional indexes based on classification of distorted components are replaced by the variable thresholds in the ADDR definition. Subsequently, the detailed steps of ADDR and the critical factors influencing its accuracy, are analyzed and then the optimized measurement conditions are given. Experiments based on simulated DUTs show the ADDR index can distinguish performance difference that the traditional indexes cannot distinguish, which proves it is an effective supplementary to the existing indexes in some real applications.

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