William C. Headley

Distributed Cyclic Spectrum Feature-Based Modulation Classification

Automatic modulation classification (AMC) is a very important process for any receiver that has no, or limited, knowledge of the modulation scheme of the received signal. In addition to military systems, AMC has been receiving an increasing amount of interest in the context of spectrum sharing cognitive radio systems. In this paper, we present an AMC system based upon the distributed processing of local classification decisions made by multiple radios. These radios consist of two stages: a cyclic spectrum feature-based AMC stage and a decision making (DM) stage that makes a local decision that is sent to a fusion center. This fusion center then makes a global decision based on its own AMC stage and on the local decisions made by the radios. A nonlinear Gauss-Seidel iterative algorithm is used to find the person-by-person optimum decision rules for the fusion center and DMs. It is shown that the proposed distributed approach results in a significant increase in the probability of signal detection and correct classification, at the expense of requiring messages to be transmitted among the fusion center and the radios in the system.

Asynchronous Classification of Digital Amplitude-Phase Modulated Signals in Flat-Fading Channels

This paper presents a new asynchronous modulation classifier for digital amplitude-phase modulated signals in flat-fading channels when the channel state is assumed unknown. In the design of this classifier, we propose new estimators for the unknown amplitude, time offset, and noise power that are blind to the modulation scheme of the received signal. It is shown that the proposed classifier performs well compared to the optimal classifier with perfect channel knowledge for an adequate estimation interval.

Practical Issues for Spectrum Management With Cognitive Radios

The policy of permanently assigning a frequency band to a single application has led to extremely low utilization of the available spectrum. Cognitive radio, with its ability to be both intelligent and frequency agile, is thought to be one of the prime contenders to provide the necessary capabilities needed for dynamic spectrum access systems. With this in mind, this paper discusses the practical issues inherent to the deployment of spectrum management systems utilizing cognitive radios.

The application of distributed spectrum sensing and available resource maps to cognitive radio systems

In order for cognitive radio systems to fulfill their potential of enabling more efficient spectrum utilization by means of opportunistic spectrum use, significant advances must be made in the areas of spectrum sensing and ldquocognitiverdquo spectrum access. In this paper, we discuss two research efforts relevant to these areas; namely the development of distributed (cyclic feature-based) spectrum sensing algorithms and of available resource maps-based cognitive radio systems. It is shown that distributed spectrum sensing is a practical and efficient approach to increase the probability of signal detection and correct modulation classification and/or to reduce sensitivity requirements of individual radios. Additionally, numerical results are presented that show significant reduction of harmful interference and greater spectrum utilization efficiency of available resource maps-based cognitive radio systems.

Low Antenna Ultra Wideband Propagation Measurements and Modeling in a Forest Environment

Over the past decade, research and development in wireless sensor networks has investigated novel and exciting applications for a number of different scenarios. Impulse ultra wideband (UWB) communications promises a number of benefits for use in wireless sensor networks-particularly in forest environments where it can provide robust operation along with the ability to combine communications with precision position location. In this paper, we present measurement results and empirical models for impulse ultra wideband (UWB) propagation in a forest environment. Path loss measurements were performed using a 620 picosecond duration UWB pulse, and over 14000 measurements were recorded in 93 different locations in four different forest environments: light brush, light, medium, and dense forest. Transmitters and receivers were separated by distances ranging from 4 to 50 meters. Path loss exponents were found to range from 2.2-4.3, depending on the antenna and forest density; results which are in agreement with the few existing broadband forest propagation studies presented in the literature. Small-scale fading analysis indicated that UWB signals experience Rician fading, with K-factors in the range of 8-24 dB. These measurements and models should aid in the development of future UWB outdoor sensor networks.

Exploiting Radio Correlation and Reliability Information in Collaborative Spectrum Sensing

Prior research has demonstrated that using radio correlation or radio reliability information can lead to more efficient collaborative spectrum sensing. However, the joint consideration of this position-dependent information is at present underdeveloped. This paper presents a novel collaborative spectrum sensing algorithm in which both radio correlation and reliability information are jointly considered. Accompanying performance analysis demonstrates that the proposed algorithm outperforms algorithms in which only one, or neither, of these position-dependent effects are considered.

Preliminary UWB Propagation Measurements in an Underground Limestone Mine

In an underground mine, wireless systems based on ultra wideband (UWB) signals have the potential to improve the response time to mine emergencies by providing fast, reliable communications as well as precision position location. Much of the UWB research to date has focused on the in-building and (to a lesser degree) outdoor propagation environments. As a result, little information is available on the nature of UWB propagation in an underground mining environment. This paper presents preliminary path loss and power delay profile measurement results for a typical room-and-pillar underground mine located in southwest Virginia. Transmitters and receivers were separated by distances ranging from 20 m to over 300 m in both line-of- sight and non-line-of-sight configurations. These results indicate that for long corridor environments, similar to indoor hallways, a waveguide effect results in better than free space propagation. Additionally, UWB signals were found to experience less fading over a local area than CW signals. UWB signals also provide the opportunity to aid in position location by achieving very accurate time-of-arrival measurements, or via RF fingerprinting techniques. We show that initial attempts to determine position based on such an approach were also very promising. The results presented here are insufficient in number to make some definitive statements about UWB propagation in underground mines but are only a first step towards characterizing that propagation. Additionally, the initial results are similar to the trends seen in indoor environments thus providing optimism that UWB may be a viable physical layer for wireless systems in underground mines.

Automatic Modulation Classification under IQ Imbalance Using Supervised Learning

The process of classifying digital modulation schemes given IQ imbalance at the transmitter or receiver is studied using fourth and sixth order cumulants as features. Various methods of supervised learning are proposed in order to mitigate the effect of IQ imbalance at the receiver, including K-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and decision tree learning. The impact of IQ imbalance at the transmitter is also observed, as well as the effect of IQ imbalance on the theoretical cumulant values for each modulation scheme. Through simulation, it is shown that supervised learning approaches are effective at compensating for the IQ imbalances that can occur at the receiver.

Maximum-likelihood modulation classification with incomplete channel information

This paper presents a discussion of the classification of digital communication signals given incomplete knowledge of the channel. Through a maximum-likelihood framework, modulation classifiers are presented which assume no or limited a priori knowledge of the fading experienced by the signal (including time offset, phase shift, and amplitude) and/or the distribution of the noise added in the channel. A recently published asynchronous classifier for digitally modulated signals, which uses a new channel estimator that is blind to the modulation scheme of the received signal, is introduced and analyzed. In addition, results are presented of our recent work on the classification of digitally modulated signals in flat fading non-Gaussian channels.

Implementation of an actor framework for a ground station

This paper describes an implementation for a ground station framework based on the actor model and software-defined radios (SDRs), allowing for a highly scalable system that can operate on low-cost receivers or scale to a collection of servers. The actor model defines a concurrent system where the individual unit of computation is the actor and communication between components is handled through a message passing interface. Pystation is an actor framework written in Python that implements a message-passing scheme where each actor has the ability to communicate with local actors or actors across a network. This paper describes some historical and background information on the actor model followed by details of its implementation and examples of use at a ground station. GNU Radio is utilized as the software for physical layer and signal processing, allowing for a highly robust and flexible ground station that can be altered without tedious hardware modifications. The purpose of this framework is to provide the software foundation to the Virginia Tech Ground Station (VTGS) in order to increase reliability and flexibility. The Virginia Tech Ground Station project is meant to serve as a ground station operations and communications sandbox for educational activities, such as student labs and space-based research projects.