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Coherent Technology Transition to Access Networks
Published in Zhensheng Jia, Luis Alberto Campos, Coherent Optics for Access Networks, 2019
Similarly, the sampling rate of ADCs can also be reduced to lower the cost of optical receivers. The sampling rate of an ADC is a key factor that determines the complexity, power consumption, and cost of the circuit. Ideally, the sampling rate should be higher than the Nyquist rate of the signal by some over sampling rate. However, with advanced DSP technology, sub-Nyquist sampling, i.e. sampling with a rate lower than the Nyquist rate, can also recover the original signal with acceptable degradation. In addition, using multiple parallel ADCs can significantly reduce the sampling rate of each ADC. For example, by interleaving four parallel ADCs, the rate of each ADC can be reduced by four times. This is a useful technique for high-bandwidth coherent optical receiving [8].
High-Speed, Low-Power CMOS A/D Converter for Software Radio
Published in Krzysztof Iniewski, Circuits at the Nanoscale, 2018
James W. Haslett, Abdel-Fattah S. Yousif
High-speed analog-to-digital converter (ADC) architectures have been developing for the past two decades to support the increasing requirements for data processing. Today, high sampling rate converters are in great demand due to the advances in software-defined radio (SDR) architectures and the drive toward direct radiofrequency/intermediate frequency (RF/IF) sampling. Performance specifications for these communication systems dictate that ADC designs, in a low-cost process technology such as CMOS, must achieve high spurious-free dynamic range (SFDR) and low-power consumption [1]. On the other hand, the sampling rate must meet or exceed the Nyquist rate to ensure the correct reconstruction of the signal information. ADC designs have been gaining performance due to process technology scaling. Current demand for ADC performance has exceeded the potential performance limits of existing high-speed ADC architectures, such as the flash ADC [2]. This chapter reviews the current high-speed architectures and predicts their performance as the CMOS process technology scales down further. The focus in this chapter will be on ADC architectures that can be beneficial in meeting the industry’s application needs.
Sampling
Published in Jerry D. Gibson, The Communications Handbook, 2018
The situation shown in Fig. 2.1 (j) corresponds to the case where f < 2fM. In this case, there is an overlap between M(c) and M(c - coM). This overlap of the spectra is known as aliasing or foldover. When this aliasing occurs, the signal is distorted and it is impossible to recover the original signal m(t) from the sampled signal. To avoid aliasing, in practice, the signal is sampled at a rate slightly higher than the Nyquist rate. Iff > 2fM, then, as shown in Fig. 2.1(f), there is a gap between the upper limit coM of M(c) and the lower limit cos - wM of M(c - cs). This range from coM to cos - wM is called a guard band. As an example, speech transmitted via telephone is generally limited to fM = 3.3 kHz (by passing the sampled signal through a low-pass filter). The Nyquist rate is, thus, 6.6 kHz. For digital transmission, the speech is normally sampled at the rate f = 8 kHz. The guard band is then f - 2fM = 1.4 kHz. The use of a sampling rate higher than the Nyquist rate also has the desirable effect of making it somewhat easier to design the low-pass reconstruction filter so as to recover the original signal from the sampled signal.
Analysis and Design of Compressive Pulsed Radar Based on Adaptive Pipelined Algorithm
Published in IETE Journal of Research, 2022
Sameh Ghanem, Fathy A. Abdel Kader
High-resolution multichannel radars and wideband radar systems have a major problem in acquiring or storing huge data. According to the theorem of Nyquist-Shannon sampling, nature signals can be sampled at least twice the signal bandwidth to avoid aliasing problems. Analog-to-Digital Converter (ADC) is the key to digital radar signal processing. An ADC device represents the continuous radar signal by a stream of numbers at a finite resolution [1]. The Shannon and Nyquist theorem lies at the heart of essentially all ADC devices. ADC serves as the gate to the digital domain. A stream of numbers can be generated using an ADC device which indicates the signal amplitudes at these points. The streaming signal is acquired with a rate twice or larger than the highest frequency of the received signal which in terms known as the Nyquist rate [2]. Therefore, a problem occurs when acquiring signals with wide bandwidths which need high-speed ADC that may be non-applicable in a real-time application, or very expensive. So, there is an alternative way that deals with huge data and overcomes the problem of a high sampling rate according to the theory of Compressive Sensing (CS) [3,4]. Basic CS theory depends on two main properties: stability and recoverability. Stability represents the robustness issues in the recovery process when noise is added [5].
Knowledge Based database of arm-muscle and activity characterization during load pull exercise using Diagnostic Electromyography (D-EMG) Signal.
Published in Cogent Engineering, 2020
Pritam Chakraborty, Biswarup Neogi, Achintya Das
Apprehending and analysis of the D-EMG signal is most commonly done digitally by computer, which requires transforming the analog signal into a digital signal using an analog to digital (A/D) converter. One of the most important factors in the A/D converter is a sampling. A slow sampling rate can result in the distortion of the signal, such as aliasing, in order to avoid aliasing and other signal distortion the sampling rate must be greater than Nyquist rate (Burden & Bartlett, 1999). The major power of the D-EMG signal is accounted for by harmonics up to 400–500 Hz range and most of the frequency components of the D-EMG signal more than 500 Hz is contributed by the electrode and equipment noise or environmental interference. Thus, for D-EMG signal analysis broadly used sampling rate is 1 kHz. Utilizing a high sampling rate involves high-frequency components of the myoelectric signals captured with the surface electrodes but concurrently add prosthesis controller processing and time complexity (Youdas et al, 2008; 2010). Thus, it is desirable in signal acquisitions of EMG signal to use a low sampling rate without compromising controller performance (Hibbs et al., 2011) Quantization of the sampled signal consists of expressing the analog value in digital forms or steps, which has limited resolution. The amplitude of each digital form is referred to as the least significant bit or LSB. The quantization presents an approximation in the reconstructed signal, since all the values of the between two subsequent values will be represented by the same digital steps, it can be modeled as an additive noise that is added to the signal in order to obtain digital representation. The effect of A/D conversion of analog D-EMG signal is then limited to signal-to-noise ratio to a value equals the value of quantization signal-to-noise ratio, for a signal with uniform amplitude distribution, equals:
Sampling Theorems with Nonlinear Signal Reconstruction Scheme
Published in IETE Journal of Research, 2021
K. K. Sharma, Lokesh Sharma, Shobha Sharma
If the sampling of the signal is done below the Nyquist rate or the signal is not bandlimited in the CFD, aliasing takes place in the CFD [2]. The attempt to reconstruct the signal under such conditions by performing the filtering with an ideal low-pass filter using (1) or using any other LTI system will not work and we will not be able to recover the original signal back [1–12].