Influence of Analog-To-Digital Conversion and Decimation Parameters on the Signal-To-Noise Ratio in the Radar Path

Introduction. The digital representation of received radar signals has provided a wide range of opportunities for their processing. However, the used hardware and software impose some limits on the number of bits and sampling rate of the signal at all conversion and processing stages. These limitations lead to a decrease in the signal-to-interference ratio due to quantization noise introduced by powerful components comprising the received signal (interfering reflections; active noise interference), as well as the attenuation of a low-power reflected signal represented by a limited number of bits. In practice, the amplitude of interfering reflections can exceed that of the signal reflected from the target by a factor of thousands.Aim. In this connection, it is essential to take into account the effect of quantization noise on the signal-tointerference ratio.Materials and methods. The article presents expressions for calculating the power and power spectral density (PSD) of quantization noise, which take into account the value of the least significant bit of an analog-to-digital converter (ADC) and the signal sampling rate. These expressions are verified by simulating 4-, 8- and 16-bit ADCs in the Mathcad environment.Results. Expressions are derived for calculating the quantization noise PSD of interfering reflections, which allows the PSD to be taken into account in the signal-to-interference ratio at the output of the processing chain. In addition, a comparison of decimation options (by discarding and averaging samples) is performed drawing on the estimates of the noise PSD and the signal-to-noise ratio.Conclusion. Recommendations regarding the ADC bit depth and sampling rate for the radar receiver are presented.

Quantization noise in low bit quantization and iterative adaptation to quantization noise in quantizable neural networks

Quantization is one of the most popular and widely used methods of speeding up a neural network. At the moment, the standard is 8-bit uniform quantization. Nevertheless, the use of uniform low-bit quantization (4- and 6-bit quantization) has significant advantages in speed and resource requirements for inference. We present our quantization algorithm that offers advantages when using uniform low-bit quantization. It is faster than quantization-aware training from scratch and more accurate than methods aimed only at selecting thresholds and reducing noise from quantization. We also investigated quantization noise in neural networks for low-bit quantization and concluded that quantization noise is not always a good metric for quantization quality.

New 2 2 Sigma Delta Modulators for Different Applications

This work presents the design of a new 2-2 programmable sigma delta modulator architecture, for different applications, this transformation design of the ΣΔ modulator low-pass, band-pass and high-pass or vice versa with loopbacks addition, which improved the linearity of the converter and reduced the quantization noise. In this work, the MASH structure enables the implementation of stable and high-order modulator. This makes low voltage and low power applications ideal. The simulation result for sigma delta modulator for biomedical applications exhibit a signal to noise ratio is 95 dB @ 250Hz bandwidth and a 75dB @ 200KHz ,85dB @1MHz for pass band modulator. The SNR is about 70dB for 5MHz bandwidth and for high pass application. This tool will allow a development contribution and characterize a system optimization set from the start while remaining at a high level of design that is suitable for electronic systems and models VHDL-AMS, RF, Biomedical.

Channelized active noise elimination (CANE) for suppressing quantization noise in bitstream modulated transmitter (BMT)

Bitstream modulated transmitters may offer improved power efficiency and linearity simultaneously in RF power amplifiers. Several modulation techniques including envelop delta-sigma modulation and envelope pulse width modulation have been applied. The out-of-band quantization noise associated with these modulations may be rejected by a high-quality factor output filter, yet the in-band quantization noise needs to be further suppressed to meet the requirement of the emission mask. The proposed channelized active noise elimination technique can offer additional quantization noise suppression through software control without involving a passive filter. The essential concept is based on combining the outputs of multiple channels of Pas that have digitally controlled delays to form a FIR filter in analogue domain. A two-channel and a four-channel GaN power amplifiers are built to demonstrate this noise suppression concept and power combiners based on T-junction with quarter wavelength transmission line are proposed to retain the high power efficiency of the transmitters.

Design of wireless audio transmission system for college English language teaching

In the past, the use of wireless microphone was affected by overload noise, resulting in incomplete transmission of teaching content. In order to solve this problem, a wireless audio transmission system design for College English language teaching is proposed. The overall architecture of SEP6010 wireless voice system is designed, which is output by PWM channel to complete voice playback. UDA1341TS low-power stereo analog-to-digital converter is used to control audio transmission. The voice circuit is designed by adding clock signal or internal programmable clock. The A/D conversion module is used to amplify the collected signal, and the original voice signal is restored through the audio playback module. MATLAB is used to simulate the process of encoding, decoding and filtering. According to CVSD quantization integral coding algorithm, the quantization noise is reduced to a given allowable value. Start Audacity software to complete wireless audio transmission. The experimental results show that the highest transmission teaching content integrity of the system under single frequency input is 98%, and the highest transmission teaching content integrity under mixing input is 93%.

Data-Driven Convolutional Model for Digital Color Image Demosaicing

Modern digital cameras use specific arrangement of Color Filter Array to sample light wavelength corresponding to visible colors. The most common Color Filter Array is the Bayer filter that samples only one color per pixel. To recover the full resolution image, an interpolation algorithm can be used. This process is called demosaicing and it is one of the first processing stages of a digital imaging pipeline. We introduce a novel data-driven model for demosaicing that takes into account the different requirements for reconstruction of the image Luma and Chrominance channels. The final model is a parallel composition of two reconstruction networks with individual architecture and trained with distinct loss functions. In order to solve the overfitting problem, we prepared a dataset that contains groups of patches that share common chromatic and spectral characteristics. We reported the reconstruction error on noise-free images and measured the effect of random noise and quantization noise in the demosaicing reconstruction. To test our model performance, we implemented the network on NVIDIA Jetson Nano, obtaining an end-to-end running time of less than one second for a full frame 12 MPixel image.

Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs

In this paper, we propose rate-splitting (RS) multiple access to mitigate the effects of quantization noise (QN) inherent in low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We consider the downlink (DL) of a multiuser massive multiple-input multiple-output (MIMO) system where the base station (BS) is equipped with low-resolution ADCs/DACs. The BS employs the RS scheme for data transmission. Under imperfect channel state information (CSI), we characterize the spectral efficiency (SE) and energy efficiency (EE) by deriving the asymptotic signal-to-interference-and-noise ratio (SINR). For 1-bit resolution, the QN is very high, and the RS scheme shows no rate gain over the non-RS scheme. As the ADC/DAC resolution increases (i.e., 2–3 bits), the RS scheme achieves higher SE in the high signal-to-noise ratio (SNR) regime compared to that of the non-RS scheme. For a 3-bit resolution, the number of antennas can be reduced by 27% in the RS scheme to achieve the same SE as the non-RS scheme. Low-resolution DACs degrades the system performance more than low-resolution ADCs. Hence, it is preferable to equip the system with low-resolution ADCs than low-resolution DACs. The system achieves the best SE/EE tradeoff for 4-bit resolution ADCs/DACs.

Quantization of Weights of Neural Networks with Negligible Decreasing of Prediction Accuracy

Quantization and compression of neural network parameters using the uniform scalar quantization is carried out in this paper. The attractiveness of the uniform scalar quantizer is reflected in a low complexity and relatively good performance, making it the most popular quantization model. We present a design approach for the memoryless Laplacian source with zero-mean and unit variance, which is based on iterative rule and uses the minimal mean-squared error distortion as a performance criterion. In addition, we derive closed-form expressions for SQNR (Signal to Quantization Noise Ratio) in a wide dynamic range of variance of input data. To show effectiveness on real data, the proposed quantizer is used to compress the weights of neural networks using bit rates from 9 to 16 bps (bits/sample) instead of standardly used 32 bps full precision bit rate. The impact of weights compression on the NN (neural network) performance is analyzed, indicating good matching with the theoretical results and showing negligible decreasing of the prediction accuracy of the NN even in the case of high variance-mismatch between the variance of NN weights and the variance used for the design of quantizer, if the value of the bit-rate is properly chosen according to the rule proposed in the paper.