Time-bandwidth Compression Research Articles

Time-bandwidth compression of microwave signals.

We report and demonstrate a reconfigurable photonic anamorphic stretch transform to realize time-bandwidth product (TBP) compression for microwave signals. A time-spectrum convolution system is employed to provide an ultra-high nonlinear dispersion up to several nanoseconds per gigahertz, which is required for processing nanosecond-long microwave signals. The group delay of the system can be engineered easily by programming a WaveShaper. Based on the proposed scheme, the TBP of a double pulse microwave signal is compressed by 1.9 times. Our proposal can provide a more efficient way to sample, digitize and store high-speed microwave signals, opening up entirely new perspectives for generation of many critical microwave signal processing modules.

Experimental Demonstration of Time-Bandwidth Expansion Using Warped Stretch Transform

We experimentally demonstrate, for the first time, a method for arbitrary waveform generation (AWG) with time-bandwidth product (TBP) expansion beyond the limitations set by a spectral encoder. We show that, by using an anamorphic stretch transform with a specific group delay (GD) profile, one can boost the TBP of the generated electrical waveform in time-stretch AWG. In particular, we show that the TBP expansion can be achieved, depending on the signal sparsity and proper choice of the warped GD dispersion profile. The new waveform generation system is implemented here using a nonlinearly chirped fiber Bragg grating with a warped GD profile. Experimental demonstrations in this paper show a TBP expansion of more than three times above the maximum achievable using conventional methods. Details of the signal dependence and spectral redundancy/sparsity requirement for time-bandwidth compression and expansion are also studied.

Reconstruction in time-bandwidth compression systems

Recently, it has been shown that the intensity time-bandwidth product of optical signals can be engineered to match that of the data acquisition instrument. In particular, it is possible to slow down an ultrafast signal, resulting in compressed RF bandwidth—a similar benefit to that offered by the Time-Stretch Dispersive Fourier Transform—but with reduced temporal record length leading to time-bandwidth compression. The compression is implemented using a warped group delay dispersion leading to non-uniform time stretching of the signal's intensity envelope. Decoding requires optical phase retrieval and reconstruction of the input temporal profile, for the case where information of interest resides in the complex field. In this paper, we present results on the general behavior of the reconstruction process and its dependence on the signal-to-noise ratio. We also discuss the role of chirp in the input signal.

Time–bandwidth engineering

We describe compression and expansion of the time–bandwidth product of signals and present tools to design optical data compression and expansion systems that solve bottlenecks in the real-time capture and generation of wideband data. Applications of this analog photonic transformation include more efficient ways to sample, digitize, and store optical data. Time–bandwidth engineering is enabled by the recently introduced Stretched Modulation (SM) Distribution function, a mathematical tool that describes the bandwidth and temporal duration of signals after arbitrary phase and amplitude transformations. We demonstrate design of time–bandwidth engineering systems in both near-field and far-field regimes that employ engineered group delay (GD), and we derive closed-form mathematical equations governing the operation of such systems. These equations identify an important criterion for the maximum curvature of warped GD that must be met to achieve time–bandwidth compression. We also show application of the SM Distribution to benchmark different GD profiles and to the analysis of tolerance to system nonidealities, such as GD ripples.

Anamorphic transformation and its application to time–bandwidth compression

A general method for compressing the modulation time-bandwidth product of analog signals is introduced. As one of its applications, this physics-based signal grooming, performed in the analog domain, allows a conventional digitizer to sample and digitize the analog signal with variable resolution. The net result is that frequency components that were beyond the digitizer bandwidth can now be captured and, at the same time, the total digital data size is reduced. This compression is lossless and is achieved through a feature selective reshaping of the signal's complex field, performed in the analog domain prior to sampling. Our method is inspired by operation of Fovea centralis in the human eye and by anamorphic transformation in visual arts. The proposed transform can also be performed in the digital domain as a data compression algorithm to alleviate the storage and transmission bottlenecks associated with "big data."