Digitally Enhanced Mixed Signal Systems
2: Bordeaux INP, ENSEIRB-MATMECA, Bordeaux, France
Digitally enhanced analog and mixed signal techniques are increasingly important to current and future circuit and system design. This book discusses how digital enhancement can be used to address key challenges relevant to analog components in terms of shrinking CMOS technology, increasing user demand for higher flexibility and data traffic in communications networks, and the drive to reduce power consumption. The book opens with an introduction to the main trends in current digitally enhanced systems, emphasising the impact of shrinking technology, and provides an overview of the principles of non-linear models. Later chapters cover pre-distortion and post-distortion techniques, analog-to-digital and digital-to-analog converters, clock generation, fixed-point refinement and adaptive filtering. Key themes of the book are the implementation approaches common between digital enhancement techniques and the trade-offs between complexity and performance for digitally enhanced devices and circuits. The book will be of particular interest to academic researchers and engineers working in analog and mixed signal circuit and system design.
Inspec keywords: CMOS digital integrated circuits; radiofrequency integrated circuits; mixed analogue-digital integrated circuits; CMOS analogue integrated circuits
Other keywords: analog components; digital enhancement techniques; data traffic; digitally enhanced mixed signal systems; communications networks; mixed-signal components; CMOS technology; RF components
Subjects: Mixed analogue-digital circuits; Analogue circuit design, modelling and testing; Microwave integrated circuits; CMOS integrated circuits; General electrical engineering topics; Digital circuit design, modelling and testing
- Book DOI: 10.1049/PBCS040E
- Chapter DOI: 10.1049/PBCS040E
- ISBN: 9781785616099
- e-ISBN: 9781785616105
- Page count: 380
- Format: PDF
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Front Matter
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1 Digitally enhanced mixed signal systems—the big picture
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A mixed-signal processing system consists of analog and digital signal processing systems that are connected by interfaces. Analog systems process analog signals, which are continuous in amplitude and in time. By contrast, digital systems process digital signals, which are discrete in amplitude and in time. The interfaces between these domains are data converters that either convert the analog signal to a digital signal by using an analog-to-digital converter (ADC) or convert the digital signal to an analog signal by employing a digital-to-analog converter (DAC).
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2 Nonlinear modeling
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In this chapter the authors introduce some popular nonlinear models used in the literature which is split in two parts: parametric and nonparametric models. After this they study nonlinear models best fitting with the main blocks of a transceiver: power amplifiers, low noise amplifiers (LNAs) and baseband blocks. Finally, an introduction on digital compensation of nonlinear distortions is done showing the main correction architectures.
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3 Digital predistortion
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In [1], Katz etal. provide an overview about history of power amplifier (PA) linearization and draw the picture for its motivation. The concern in linearizing power amplifier dates from the beginning of broadcasting and the expansion of the telecommunications in the 1920s [1]. In these early years, the feedforward approach was introduced by the Bell Labs to mitigate the cross modulation of voice-modulated carriers transmitted through cable and repeaters. A few years later, the Bell Labs introduced the feedback linearizer architecture which has the advantage over the feedforward architecture to be self-adaptive to drifts but has the drawback to be narrowband regarding nowadays needs.
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4 Digital post-distortion of radio receivers and analog-to-digital converters
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To the authors'best knowledge, the concept of post-distorted receivers appeared in the early 1990s [1,2]. Initially, post-distortion was thought as a solution for compensating intermodulation distortions resulting from the AM-AM and AM-PM non-linear behaviour of a mobile terminal power amplifier at the base-station receiver side. It was then considered as an alternative to pre-distortion that aimed to relax out-of-band emission specifications of portable transmitters while improving their power efficiency.
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5 Time or frequency interleaved analog-to-digital converters
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Nowadays, there are is an increasing need for multimedia communication standards offering multi-access services and high data rates. Downscaling of integrated circuit technologies has made efficient and cost-effective digital signal processing (DSP) hardware possible in many modern wireless communication systems. This trend has contributed in the development of reconfigurable and flexible radio frequency (RF) systems in order to push the analog-to-digital interface close to the antenna by facilitating DSP.
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6 Digitally enhanced digital-to-analogue converters
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With contemporary electronic systems processing information primarily in the digital domain, digital-to-analogue converters (DACs) play an essential role in interfacing to the physical world which often require a fine-grained range of signal levels being applied to the physical domain in question. DACs, therefore, are an integral part of driving actuators such as antennas for radio transmission, audio speakers for generation of sound or implanted electrodes for therapeutic stimulation of nervous tissue. DACs, on the other hand, are also used internally in electronic systems such as parts of analogue to digital converters (ADCs) or in built-in test systems. DACs are used with a wide variety of resolutions - from a single bit to tens of bits - over a wide variety of conversion speeds - from kHz ranges to GHz ranges. While low-resolution DACs are fairly straightforward to design by relying on component matching, the performance of medium and high-resolution DACs is limited by component variation. In this chapter, we use digital techniques - specifically dynamic element matching (DEM) - to improve DAC performance beyond the limitations imposed by component matching. The authors begin by giving a brief overview of common DAC encoding schemes, error mechanisms and error metrics; they also introduce a 32-element DAC structure that will be used for illustration purposes. They discuss techniques for DAC error mitigation and linearisation; in particular, the use of DEM where they use the 32 unit element DAC structure to illustrate the operation of various DEM techniques. They look at a case study of a harmonic-cancelling DAC (HC-DAC) used for sine-wave synthesis and do a detailed analysis of mismatch errors and the effects of DEM; they further present measurements from an HC-DAC with partial DEM.
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7 Clock generation
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In modern transceivers, clock generation and planning is one of the key aspects. As a matter of fact, with the increasing occupation of the spectrum and with the increasing use of discrete front ends, non-idealities such as reciprocal mixing are getting more and more critical. This chapter presents the different techniques to enhance the performance of the clock generation especially for all-digital phase locked loops (PLLs) (ADPLLs).
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8 Fixed-point refinement of digital signal processing systems
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For the digital signal processing (DSP) part of mixed-signal systems, fixed-point arithmetic is favoured due to its high efficiency in terms of implementation cost, namely energy consumption, area and latency. Fixed-point arithmetic provides low-cost operators at the expense of an unavoidable error between the ideal precision values and the finite precision ones. With a careful data word-length (WL) optimisation process, the designer can safely profit from the inherent trade-off between implementation cost and computation accuracy. In this chapter, we describe in detail the different stages comprising the fixed-point conversion process (i.e. dynamic range evaluation, WL optimisation, and accuracy evaluation) and propose practical solutions to address each stage.
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9 Adaptive filtering
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In this chapter, we have presented in detail the adaptive filtering methods with a special glance and analysis on their practical implementation. These methods, due to their iterative structure, offer an interesting architecture for real-time applications. However, when it comes to effective use, several questions must be raised beforehand: these questions are related to the algorithm behavior itself (what kind of iterative algorithm should be used? Are the filter coefficients converging toward the Wiener-Hopf solution? How much far away from this optimal solution (i.e., asymptotic performance) is expected at convergence? What is the convergence rate?) and to their practical uses (What is the computational load and hardware implementations for such algorithms? How the fixed-point implementation affects the performance?). The questions related to the algorithm behavior have been assessed in the first part of this chapter. For a wide range of iterative algorithms, a presentation and a study have been performed: from the legacy and simple LMS algorithms to more complex (and rapid) algorithm such as RLS and APA algorithms. Besides, we have also proposed a thorough analysis of alternatives methods-the so-called nonlinear methods. They are particularly suited when signal to recover exhibits a specific nature (sparsity, strong covariance, etc.). The second part of this chapter has been focused on the algorithm comparison: depending on the mathematical model they follow, the algorithms have a different complexity and provide various convergence rate and asymptotic performance. We have thus focused on the computational complexity and on their implementation and cost to clearly state the differences between the methods. Finally, to explain the methodology used in a practical implementation, we have derived an example of real channel estimation (when the channel to estimate is time varying). The trade-off between the complexity and the performance (convergence rate and asymptotic performance) has been clearly stated. The influence of fixed-point analysis has finally been demonstrated showing the important freedom degrees implementation perspective has to face. These algorithms combined with the exposed tools and methodologies are precious to practical implementation and have been presented in this book to pave the way for digitally enhanced mixed signal systems.
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Back Matter
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