Optimization methods for integrated and programmable photonics in next-generation classical and quantum smart communication and signal processing systems

Error free recovery of signals from irregularly spaced samples in terms of completeness of sets of nonharmonic exponentials

The TMS320C5x DSP Starter Kit is a simple audio-frequency digital signal processing system integrated with ADC, DAC and TMS320C5x DSP. The development time for the design of digital signal processing software and of experiments based on these resources will be greatly reduced. This paper introduces a real-time digital signal wave library and processing system based on TMS320C5x DSK. The system consists of two C5x DSKs, one is a signal generator and the other is a signal processor. The wave generator has the capacity to generate many types of signal, including deterministic signals with variable parameters, noise signals etc. The signal processor has all the functions of classic and modern signal processing. The system not only can accelerate the speed of DSP research, but also is particularly useful for DSP educational courses. This paper presents the main idea of the signal generator and processor design in detail, introducing the key software techniques used for generating and processing.

Future bandwidth demand in optical communication and signal processing systems will soon exceed 100 Gb s−1 as is commonly forecasted from a throughput experience curve for communication systems. However, such systems cannot be realized without introducing ultrafast, all-optical devices, since existing optoelectronic and electronic devices and integrated circuits would not be able to function at a bit rate exceeding 100 Gb s−1, because of the speed limit intrinsic to conventional semiconductor materials and devices. All-optical devices based on completely new principles, not being restricted by properties of existing materials and device principles, must be developed for the realization of ultrafast communication and signal processing systems. This paper reviews requirements of ultrafast all-optical devices and recent progress in ultrafast light sources and all-optical switches based on either novel device principles or ultrafast phenomena in novel materials such as quantum-confined nanostructures. Recent developments described here include mode-locked lasers and a variety of all-optical switches based on different phenomena including Mach–Zehnder interferometer structures, spin relaxation, intersubband transition, and ultrafast absorption recovery in organic thin films and semiconductor quantum dots. Some of the recent developments have already shown capability of basic functions such as ultrafast pulse generation and signal processing at the bit rate of 500 Gb s−1 to 1 Tb s−1. Technical challenges expected for the future are discussed in view of their applications in real systems.

It is now possible to move easily from a top-level simulation model of a system toward its implementation. While this path is seamless and nearly complete for the digital implementation, gaps still exist for the implementation of RF and optical portions of communication systems. The author describes the state of the art in waveform (or functional) level simulation tools for communication and signal processing systems with links to digital implementation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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. >

Marine radar commonly used for ship navigation. Nowadays, utilization of this radar to another application has expanded. For further applications, several marine radars have some limitations such as monochrome display and require new signal acquisition and processing. This research developed marine radar signal acquisition and processing system based on Furuno x-band marine radar 1932 Mark-2. The developed tool consist of marine radar signal conditioner, ADC and marine radar signal processing using Matlab. This signal processing system display provide signal echo strength visualization, gain control, sea clutter control and rain clutter control. The simulation test was conducted to test the signal processing system. The result was the GUI display at full and half gain are quite similar with marine radar display unit view. The sea and rain clutter control test results were relevant with its function in reducing more close echoes rather than far echoes. While the time required for creating an image is still need for improvement.

Signal processing theory and practice are enabling and driving forces behind multimedia devices, communications systems, and even such diverse fields as automotive and medical systems. Over 90% of the signal processing systems on the market used fixed-point arithmetic because of the cost, power, and area savings that fixed-point systems provide. However, most colleges and universities do not teach or teach only a very little fixed-point signal processing. This issue is being addressed slowly around the country but now a new challenge or opportunity presents itself. As reconfigurable logic technology matures, field-programmable gate arrays (FPGAs) are increasingly used for signal processing systems. They have the advantage of tremendous throughput, great flexibility, and system integration. The challenge is that signal processing in FPGAs is a much less constrained problem than signal processing in special purpose microprocessors. The opportunity arises in that it is now possible to explore more options and, more especially, to take a more systems-level approach to signal processing systems. In short, designing a signal processing system using FPGAs provides opportunities to look at many system design issues and trade-offs in a classroom setting. We have developed a course to teach signal processing in FPGAs at Georgia Institute of Technology and in this paper we consider the challenges and methods of teaching fixedpoint system design in this course. We discuss the topics chosen and how they differ from traditional microprocessor-based courses. We also discuss how systems engineering concepts are woven into the course. 1 Motivation This paper describes the early development of a system design curriculum that uses field programmable gate arrays (FPGAs) to implement digital signal processing (DSP) systems. The goals of this curriculum are several-fold, including:

Bandwidth and noise are fundamental considerations in all communication and signal processing systems. The group-velocity dispersion of optical fibers creates nulls in their frequency response, limiting the bandwidth and hence the temporal response of communication and signal processing systems. Intensity noise is often the dominant optical noise source for semiconductor lasers in data communication. In this paper, we propose and demonstrate a class of electrooptic modulators that is capable of mitigating both of these problems. The modulator, fabricated in thin-film lithium niobate, simultaneously achieves phase diversity and differential operations. The former compensates for the fiber’s dispersion penalty, while the latter overcomes intensity noise and other common mode fluctuations. Applications of the so-called four-phase electrooptic modulator in time-stretch data acquisition and in optical communication are demonstrated.

Digital filters are extensively used in video processing, digital communications, frequency analysis, auto control and so on. Digital filter is one of the most important part of digital signal process, almost appeared in all digital signal process system. Based on theory, the digital filters can be divided into FIR digital filter and IIR digital filter. FIR digital filter is achieved for these requirements and then widely used in modern signal processing. IIR digital filter is a kind of digital filter that its unit pulse response is unlimited long, and is also an important research task in modern electronics, communication and signal processing, many scholars did much research on the optimization design of IIR digital filter. High-density FPGA have real-time performance and flexibility, and it can provide powerful hardware support for the implementation of digital filters. In this dissertation, author will make some analysis on digital filters, and make realization of digital filters based on FPGA.

Simulation and verification using electronic design automation (EDA) tools are key steps in the design process for communication and signal processing systems. The synchronous dataflow (SDF) model of computation is widely used in EDA tools for system modeling and simulation in the communication and signal processing domains. Behavioral representations of practical wireless communication systems typically result in critical SDF graphs - they consist of hundreds of components (or more) and involve complex inter-component connections with highly multirate relationships (i.e., with large variations in average rates of data transfer or component execution across different subsystems). Simulating such systems using conventional SDF scheduling techniques generally leads to unacceptable simulation time and memory requirements on modern workstations and high-end PCs. In this paper, we present a novel simulation-oriented SDF scheduler (SOS) that strategically integrates several techniques for graph decomposition and SDF scheduling to provide effective, joint minimization of time and memory requirements for simulating large-scale and heavily multirate SDF graphs. We have implemented the SOS scheduler in the advanced design system (ADS) from Agilent Technologies. Our results from this implementation demonstrate large improvements in simulating real-world wireless communication systems (e.g. 3GPP, Bluetooth, 802.16e, CDMA 2000, and XM radio).

Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.

The authors discuss current trends and present three examples of simulation-based CAAD (computer-aided analysis and design) packages for communication systems and signal processing that are representative of the state of the art. The authors survey the hierarchical approach to CAAD tools, software structure and features, the operational flow of simulation-based approaches, and choice of platforms. Network and link level CAAD tools are considered, along with CAAD tools for signal processing. >

Research Progress of The Algebraic and Geometric Signal Processing

Efficient sample rate conversion is of widespread importance in modern communication and signal processing systems. Although many efficient kinds of polyphase filterbank structures exist for this purpose, they are mainly geared toward serial, custom, dedicated hardware implementation for a single task. There is, therefore, a need for more flexible sample rate conversion systems that are resource-efficient, and provide high performance. To address these challenges, we present in this paper an all-software-based, fully parallel, multirate resampling method based on graphics processing units (GPUs). The proposed approach is well-suited for wireless communication systems that have simultaneous requirements on high throughput and low latency. Utilizing the multidimensional architecture of GPUs, our design allows efficient parallel processing across multiple channels and frequency bands at baseband. The resulting architecture provides flexible sample rate conversion that is designed to address modern communication requirements, including real-time processing of multiple carriers simultaneously.

Integrated and reconfigurable optical filters have been widely used in modern communication and signal processing systems, and the inverse design of them remains an important problem. In this study, we introduce an anti-non-uniqueness deep learning network for the inverse design of a multi-tap reconfigurable optical filter. By employing eight optical switches configuration between cross and bar status, the integrated tunable optical delay line can act as a reconfigurable multi-tap optical filter with at most 256 taps. For its inverse design, the proposed deep learning model aims to figure out the relationship between control parameters and spectrum features of the optical filter. An improved tandem neural network has been developed with its loss function comprehensively optimized. Experiments have proved that the model helps making precise predictions regardless of the multi-solution challenge of this optical filter's inverse design, facilitating its real application in practice.