Output Filter Aware Optimization of the Noise Shaping Properties of {\Delta}{\Sigma} Modulators via Semi-Definite Programming

The Noise Transfer Function (NTF) of {\Delta}{\Sigma} modulators is typically designed after the features of the input signal. We suggest that in many applications, and notably those involving D/D and D/A conversion or actuation, the NTF should instead be shaped after the properties of the output/reconstruction filter. To this aim, we propose a framework for optimal design based on the Kalman-Yakubovich-Popov (KYP) lemma and semi-definite programming. Some examples illustrate how in practical cases the proposed strategy can outperform more standard approaches.Comment: 14 pages, 18 figures, journal. Code accompanying the paper is available at http://pydsm.googlecode.co

The effect of coefficient quantization optimization on filtering performance and gate count

Abstract. Digital filters are an essential component of Digital Signal Processing (DSP) applications and play a crucial role in removing unwanted signal components from a desired signal. However, digital filters are known to be resource-intensive and consume a large amount of power, making it important to optimize their design in order to minimize hardware requirements such as multipliers, adders, and registers. This trade-off between filter performance and hardware consumption can be influenced by the quantization of filter coefficients. Therefore, this thesis investigates the quantization of Finite Impulse Response (FIR) filter coefficients and analyzes its impact on filter performance and hardware resource consumption. A method called dynamic quantization is introduced and an algorithm for step-by-step dynamic quantization is provided to improve upon the results obtained with the classical fixed point quantization method. To demonstrate the effectiveness of this approach, the dynamic quantization of filter coefficients for a Low-pass Equiripple FIR filter is examined and a comparative study of the magnitude response and hardware consumption of the generated filter using both the classical and dynamic quantization methods is presented. By understanding the trade-offs and benefits of each quantization method, engineers can make informed decisions about the most appropriate approach for their specific application

The role of lossless systems in modern digital signal processing: a tutorial

A self-contained discussion of discrete-time lossless systems and their properties and relevance in digital signal processing is presented. The basic concept of losslessness is introduced, and several algebraic properties of lossless systems are studied. An understanding of these properties is crucial in order to exploit the rich usefulness of lossless systems in digital signal processing. Since lossless systems typically have many input and output terminals, a brief review of multiinput multioutput systems is included. The most general form of a rational lossless transfer matrix is presented along with synthesis procedures for the FIR (finite impulse response) case. Some applications of lossless systems in signal processing are presented

Extended frequency-band-decomposition sigma–delta A/D converter

Parallelism can be used to increase the bandwidths of ADC converters based on sigma–delta modulators. Each modulator converts a part of the input signal band and is followed by a digital filter. Unfortunately, solutions using bandpass sigma–delta modulators are very sensitive to the position of the modulators' central frequencies. This paper shows the feasibility of a frequency-band-decomposition (FBD) ADC using continuous time bandpass sigma–delta modulators, even in the case of large analog mismatches. The major benefit of such a solution, called extended-frequency-band-decomposition (EFBD) is its low sensitivity to analog parameters. For example, a relative error in the central frequencies of 4% can be accepted without significant degradation in the performance (other published FBD ADCs require a precision of the central frequencies better than 0.1%). This paper will focus on the performance which can be reached with this system, and the architecture of the digital part. The quantization of coefficients and operators will be addressed. It will be shown that a 14 bit resolution can be theoretically reached using 10 sixth-order bandpass modulators at a sampling frequency of 800 MHz which results in a bandwidth of 80 MHz centered around 200 MHz (the resolution depends on the effective quality factor of the filters of the analog modulators)

Advanced Algorithms for Satellite Communication Signal Processing

Dizertační práce je zaměřena na softwarově definované přijímače určené k úzkopásmové družicové komunikaci. Komunikační kanály družicových spojů zahrnujících komunikaci s hlubokým vesmírem jsou zatíženy vysokými úrovněmi šumu, typicky modelovaného AWGN, a silným Dopplerovým posuvem signálu způsobeným mimořádnou rychlostí pohybu objektu. Dizertační práce představuje možné postupy řešení výpočetně efektivní digitální downkonverze úzkopásmových signálů a systému odhadu kmitočtu nosné úzkopásmových signálů zatížených Dopplerovým posuvem v řádu násobků šířky pásma signálu. Popis navrhovaných algoritmů zahrnuje analytický postup jejich vývoje a tam, kde je to možné, i analytické hodnocení jejich chování. Algoritmy jsou modelovány v prostředí MATLAB Simulink a tyto modely jsou využity pro ověření vlastností simulacemi. Modely byly také využity k experimentálním testům na reálném signálu přijatém z družice PSAT v laboratoři experimentálních družic na ústavu radioelektroniky.The dissertation is focused on software defined receivers intended for narrowband satellite communication. The satellite communication channel including deep space communication suffers from a high level of noise, typically modeled by AWGN, and from a strong Doppler shift of a signal caused by the unprecedented speed of an object in motion. The dissertation shows possible approaches to the issues of computationally efficient digital downconversion of narrowband signals and the carrier frequency estimation of narrowband signals distorted by the Doppler shift in the order of multiples of the signal bandwidth. The description of the proposed algorithms includes an analytical approach of its development and, if possible, the analytical performance assessment. The algorithms are modeled in MATLAB Simulink and the models are used for validating the performance by the simulation. The models were also used for experimental tests on the real signal received from the PSAT satellite at the laboratory of experimental satellites at the department of radio electronics.

Digital and Mixed Domain Hardware Reduction Algorithms and Implementations for Massive MIMO

Emerging 5G and 6G based wireless communications systems largely rely on multiple-input-multiple-output (MIMO) systems to reduce inherently extensive path losses, facilitate high data rates, and high spatial diversity. Massive MIMO systems used in mmWave and sub-THz applications consists of hundreds perhaps thousands of antenna elements at base stations. Digital beamforming techniques provide the highest flexibility and better degrees of freedom for phased antenna arrays as compared to its analog and hybrid alternatives but has the highest hardware complexity. Conventional digital beamformers at the receiver require a dedicated analog to digital converter (ADC) for every antenna element, leading to ADCs for elements. The number of ADCs is the key deterministic factor for the power consumption of an antenna array system. The digital hardware consists of fast Fourier transform (FFT) cores with a multiplier complexity of (N log2N) for an element system to generate multiple beams. It is required to reduce the mixed and digital hardware complexities in MIMO systems to reduce the cost and the power consumption, while maintaining high performance. The well-known concept has been in use for ADCs to achieve reduced complexities. An extension of the architecture to multi-dimensional domain is explored in this dissertation to implement a single port ADC to replace ADCs in an element system, using the correlation of received signals in the spatial domain. This concept has applications in conventional uniform linear arrays (ULAs) as well as in focal plane array (FPA) receivers. Our analysis has shown that sparsity in the spatio-temporal frequency domain can be exploited to reduce the number of ADCs from N to where . By using the limited field of view of practical antennas, multiple sub-arrays are combined without interferences to achieve a factor of K increment in the information carrying capacity of the ADC systems. Applications of this concept include ULAs and rectangular array systems. Experimental verifications were done for a element, 1.8 - 2.1 GHz wideband array system to sample using ADCs. This dissertation proposes that frequency division multiplexing (FDM) receiver outputs at an intermediate frequency (IF) can pack multiple (M) narrowband channels with a guard band to avoid interferences. The combined output is then sampled using a single wideband ADC and baseband channels are retrieved in the digital domain. Measurement results were obtained by employing a element, 28 GHz antenna array system to combine channels together to achieve a 75% reduction of ADC requirement. Implementation of FFT cores in the digital domain is not always exact because of the finite precision. Therefore, this dissertation explores the possibility of approximating the discrete Fourier transform (DFT) matrix to achieve reduced hardware complexities at an allowable cost of accuracy. A point approximate DFT (ADFT) core was implemented on digital hardware using radix-32 to achieve savings in cost, size, weight and power (C-SWaP) and synthesized for ASIC at 45-nm technology

Subband adaptive filtering for acoustic echo control using allpass polyphase IIR filterbanks

Published versio

Active disturbance cancellation in nonlinear dynamical systems using neural networks

A proposal for the use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee convergence of the algorithm and provide insight into the necessary reduction in learning gain as a function of the system parameters. Effectiveness of the algorithm is evaluated through mathematical analysis, simulation studies, and experimental application of the technique on an acoustic duct laboratory model