Thomas P. McElwain

Recent advances in cognitive communications

This article describes recent advances in cognitive communications. We combine the concepts of signal processing, communications, pattern classification, and machine learning to make dynamic use of the spectrum, such that the emanated signals do not interfere with the existing ones. Unlike other programs such as neXt Generation communications of the Defense Advanced Research Projects Agency, where radio scene analysis is performed to find the spectrum holes or the white space, we make use of the white, as well as the gray space for non- interfering signal transmission. We examine the possibility of employing machine perception and autonomous machine learning technologies to the autonomous design and analysis of air interfaces. The underlying premise is that a learning module will facilitate adaptation in the standard classification process so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allows knowledge enhancement as more snapshots of data are processed, resulting in improved decisions. Some of the contributions of this project include technological advances in signal detection, feature identification, signal classification, sub-space tracking, adaptive waveform design, machine learning, and prediction.

Machine Learning based Cognitive Communications in White as Well as the Gray Space

This paper describes new ideas and results on machine learning based cognitive communications in White as well as the Gray space. We combine the concepts of signal processing, communications, pattern classification and machine learning to make a dynamic use of the spectrum such that the emanated signals do not interfere with the existing ones. Unlike other programs such as the neXt Generation (XG) communications program of the Defense Advanced Research Projects Agency (DARPA), where radio scene analysis is carried out to find the spectrum holes also known as the White space, we make use of the White as well as the Gray space for non-interfering signal transmission. Our assumption is that a learning module will facilitate adaptation in the signal classification process, so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allow knowledge enhancement as more snap-shots of data are processed, resulting in improved decisions. Use of non-competitive policy set results in zero interference with the already existing signals with a modest increase in the White and Gray space utilization. On the other hand competitive policy set utilizes machine learning to predict the future behavior of the signals which results in more than 90% utilization of spectrum at an expense of some interference due to errors in prediction.

Multiple quantum well (MQW) spatial light modulators (SLMs) for optical data processing and beam steering

Resonant cavity Fabry-Perot structures with embedded multiple quantum well layers are used to create spatial light modulators for a number of signal processing and optical beam steering applications. A review of the SLM development including modulator design considerations, our general approach to modulator-driver integration, and array formats previously demonstrated, will be presented (linear arrays up to 2048X1 and two-dimensional arrays up to 256x256). Optical transitions in MQW structures are inherently fast (

Large scale modulator arrays for beam steering and optical modulator applications

GTR

4D CAF for localization of co-located, moving, and RF coincident emitters

GTRns switching times) so that SLMs based on these structures can exhibit high frame rates. The optical modulator is based on a p-i-n device design operated in a reverse bias mode. The speed of the array depends primarily on the speed of the drive electronics (based on the available CMOS or other electronic drivers), the impedance of the individual modulator device, and the scheme used to bring data into the hybrid modulator/driver module. The current drivers are based on mixed signal designs that use 0.5 square m high-voltage CMOS technology and a 50-MHz data rate. Several examples of the use of the modulators will be given, including the application of the SLMs to hyperspectral data processing and optical beam steering.

Co-channel interference receiver

Resonant cavity Fabry-Perot structures with embedded multiple quantum well layers are used to create spatial light modulators. Several examples are given, including the application of the SLMs to hyperspectral data processing and optical beam steering.

Joint symbol, amplitude, and rate estimator

The current state of the art for attempting to geolocate co-located, moving RF coincident emitters employs sensing from multiple moving sensors. In this case the RF coincident emitters will be transmitting similar but not identical RF signals overlapping temporally, spectrally and spatially, e.g. two nodes near each other simultaneously transmitting Wifi and Bluetooth. We use a set of distributed sensors to capture RF emissions, distribute compressed sample data to perform 4D cross ambiguity function (CAF), i.e. time difference of arrival (TDOA), frequency difference of arrival (FDOA), frequency rate difference of arrival (FRDOA), and cyclostationary feature extraction via spectral correlation function (SCF) to uniquely identify, categorize, geolocate and track each emitters.