Robert W. Lowdermilk

Software defined radio: Part 22 in a series of tutorials on instrumentation and measurement

A software defined radio (SDR) is a communication system that performs many of its required signal processing tasks in a programmable digital signal processing (DSP) engine. The engine is coupled to the air interface of analog circuits and antennae by analog-to-digital and digital-to-analog converters (ADCs and DACs). The SDRs software reprograms the DSP segment of the radios physical layer to reconFigure the radio system parameters and can thus synthesize multiple radios. The software can also select and alter the air interface components as well as the higher level data processing layers of the radio system.

Vector Signal Analyzer Implemented as a Synthetic Instrument

Synthetic instruments (SIs) use the substantial signal processing assets of a field-programmable gate array (FPGA) to perform the multiple tasks of targeted digital signal processing (DSP)-based instruments. The topic of this paper is vector signal analysis from which time-dependent amplitude and phase is extracted from the input time signal. With access to the time-varying amplitude-phase profiles of the input signal, the vector signal analyzer can present many of the quality measures of a modulation process. These include estimates of undesired attributes such as modulator distortion, phase noise, clock jitter, I -Q imbalance, intersymbol interference, and others. This is where the SI is asked to become a smart software-defined radio (SDR), performing all the tasks of a DSP radio receiver and reporting small variations between the observed modulated signal parameters and those of an ideal modulated signal. Various quality measures (e.g., the size of errors) have value in quantifying and probing performance boundaries of communication systems.

Synthetic Instrumentation: The road ahead

This paper provides the reader with an overview of current Synthetic Instrumentation technology and a discussion of the probable future direction of this interesting and disruptive technology. The paper opens with a brief introduction and technical overview of Synthetic Instrumentation and then presents a discourse on the primary attributes of Synthetic Instrumentation. Having reviewed the basics of Synthetic Instrumentation technology, the paper then goes on to discuss the fundamental classes/types of Synthetic Instruments (SI), their associated attributes, and typical types of users associated with these SI classes. The authors then discuss how Synthetic Instrumentation fits in the current Test and Measurement (T&M) application continuum. The paper concludes with a discussion on the possible disruptive impact of this emerging technology on the Test and Measurement (T&M) marketplace and the key technologies that will have a future impact on the continuing evolution of Synthetic Instrumentation.

Using the FFT as an arbitrary function generator

The inverse fast Fourier transform (IFFT) forms a time series from its spectral description. The time data formed by the IFFT represents a single cycle of a periodic waveform which can be accessed periodically to form an arbitrary length version of the signal. This property is similar to the periodic memory access used by arbitrary function generators. A sequence of windowed, overlapped IFFTs can be used to seamlessly extend the time series to obtain any arbitrary non-periodic time series. This option is not available from traditional arbitrary function generators.

Signal conditioning and data collection in synthetic instruments [ATE systems]

Synthetic instruments apply the flexibility and computational capabilities of a digital signal-processing platform to synthesize a wide variety of synthetic instruments. The emphasis presented in traditional descriptions of a synthetic instrument is the architecture and the capabilities of the digital signal processing (DSP) platform and the user interfaces to the same. In this paper we highlight and discuss considerations of the signal conditioning and signal collection tasks. The focal point of the signal collection process is the analog-to-digital converter (ADC), the element that defines the precision and the bandwidth of the sampled data representation of the signal processed by the DSP platform. Analog signal conditioning prior to the ADC performs the task of limiting the input signal bandwidth and possible translation of the spectral band center. Digital signal conditioning following the ADC continues to perform the same tasks by further limiting the bandwidth of the digitized input signal as well as performing spectral translation with appropriate sample rate changes. In addition, the post conversion process can correct gain, phase, and time delay imbalances between input signal paths as well as gain and phase distortion encountered in the analog signal path. We identify and address performance constraints of existing ADCs and present a number of signal processing-based options to enhance and extend the operating regimes of the analog signal conditioning and the ADC conversion process.

Extracting masked signal parameters with a synthetic instrument

Synthetic instruments apply the flexibility and computational capabilities of a digital signal-processing platform to synthesize a wide variety of synthetic instruments. We are often presented with measuring waveform characteristics containing known attributes and parameters. These parameters may include signal bandwidth, AM or FM modulation indices, spectral shape parameters and inserted pilot levels. The task of the synthetic instrument for this class of signals is to verify that the waveform parameters match the selected parameter settings. A related task is that the same waveform may contain undesired artifacts of the signal generation process. These artifacts might include spectral regrowth terms, spectral harmonics, and digital-to-analog converter noise. The task of the test equipment is to isolate and measure the parameters of these artifacts to verify they do not exceed specified acceptance criterion. Often, the undesired signal components and the desired components occupy overlapping spectral spans or the same spectral span. In this overlay scenario, the desired signal may mask the undesired components preventing the successful measurement and classification of these artifacts. This paper presents a class of signal extraction algorithms that separate desired signal components from undesired components. These algorithms use deterministic signal canceling and signal subtraction techniques to separate the desired and artifact components.

A methodology for enhancing legacy TPS/ATS sustainability via employing Synthetic Instrumentation technology

This paper presents a methodology for mitigating Test Program Set (TPS) & Automatic Test System (ATS) obsolescence and enhancing TPS/ATS sustainability via employing Synthetic Instrumentation (SI) technology. The methodology and the associated sub- processes described within this paper represent a major paradigm shift in current support equipment hardware & software sustainability approaches and will have a profound impact on the process of supporting and maintaining legacy automated test systems (ATS) and TPSs now and into the future. The subject methodology was validated employing the Tobyhanna Army Depot (TYAD) RF Test Platform as the demonstration vehicle test bed. The proof-of-concept demonstration validated the concept of emulating and replacing several legacy Commercial Off-the-Shelf (COTS) instruments with synthetic instrument technology. The primary goals of validating this technology paradigm were to provide an environment that would reduce TPS rework costs, decrease ATS maintenance and repair costs, and enhance the sustainability of legacy ATSs/TPSs going forward. During the course of this project the synthetic instrument technology insertion paradigm was demonstrated to the at-large DOD maintenance community.