Jeff Reed - Academia.edu (original) (raw)

Papers by Jeff Reed

IEEE Access

In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use... more In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands as well as other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as mitigation approaches based on expert systems. More recently, researchers have successfully explored artificial intelligence/machine learning enabled physical layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. In this paper, we address the knowledge gap in literature with respect to the state-of-the-art in deep learning-based interference suppression. Specifically, we review a wide range of techniques that have used deep learning to suppress interference by learning interference characteristics directly from data, rather than relying on expert systems. We provide a thorough technical discussion of the prominent deep learning algorithms that have been proposed in the literature and provide comparison and guidelines regarding their successful implementation in this application. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in this critical field.

Clearing radio spectrum from an allocated but underutilized usage to repurpose the spectrum band ... more Clearing radio spectrum from an allocated but underutilized usage to repurpose the spectrum band to another usage often requires many years to accomplish. For example, the transition from

IEEE Communications Magazine, 2014

ABSTRACT Software-defined radio is a rapidly developing field that is driving the development of ... more ABSTRACT Software-defined radio is a rapidly developing field that is driving the development of and innovation in communications technology, and promises to significantly impact all communications sectors. Entities developing these SDR systems require a trained workforce that has been prepared with the mindset, knowledge, skills, and tools required to address both the system (breadth) and technical (depth) aspects of SDR systems. Developing SDRs necessarily involves a collection of disciplines including, but not limited to, electromagnetics, radio-frequency engineering, communications, digital signal processing, embedded systems, computer programming, and systems engineering. Whereas electrical engineering and computer science and engineering curricula at the university level may include courses in all of these areas, a student’s typical curriculum does not; nor does it usually involve the integration of all these topics. However, SDR can be employed as an integrative construct that facilitates systems thinking and cross-domain learning via peers. In this article, we present several significant educational efforts across six U.S. universities that have developed integrated curricula in SDR, most including a significant laboratory component.

Virginia Tech, 2002

UWB shows great promise for use in a number of wireless communications applications, in particula... more UWB shows great promise for use in a number of wireless communications applications, in particular, short range high bit rate networking. However, due to the emissions limits set by the FCC, commercial systems are limited in range for high data rates. To achieve greater ...

Analog Integrated Circuits and Signal Processing, 2011

ABSTRACT Antennas are a key enabling technology for software-defined radio (SDR). Although softwa... more ABSTRACT Antennas are a key enabling technology for software-defined radio (SDR). Although software is extremely flexible, SDR’s potential is limited by antenna size and performance. In this paper, we review typical antenna miniaturization techniques and fundamental theories that limit antenna size and performance including operational bandwidth, gain (or range), and radiation pattern. Possible antenna design strategies are discussed to meet the desired specifications in SDR based on observations from the limit theories. The application of strategies to enable multiband (resonant), continuous multiband (frequency independent), and instantaneous, ultra-wideband antennas are discussed qualitatively. Advantages, disadvantages, and design trade-off strategies for different types of antennas are compared from a system-level perspective. A design example for a compact ultra-wideband (UWB) antenna is presented for a software-defined platform. The example involves a direct-conversion radio developed in Wireless@VT that uses a Motorola RFIC having a 100MHz–6GHz operational frequency range with a 9kHz–20MHz channel bandwidth. The example antenna covers frequencies from 450MHz to 6GHz instantaneously with approximately 5-dBi realized gain over a finite-size ground plane, including return loss and omni-directional coverage. KeywordsSoftware-defined radio (SDR)–Fundamental limits on antennas–Ultra-wideband antenna–Antenna gain–Antenna bandwidth–Antenna miniaturization

2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN), 2014

Although using geolocation databases is a practical approach for enabling spectrum sharing, it po... more Although using geolocation databases is a practical approach for enabling spectrum sharing, it poses a potentially serious privacy problem. Secondary users (queriers), through seemingly innocuous queries to the database, can determine the types and locations of incumbent systems operating in a given region of interest, and thus compromise the incumbents' operational privacy. When the incumbent systems (primary users) are commercial systems, this is typically not a critical issue. However, if the incumbents are federal government systems, including military systems, then the information revealed by the databases can lead to a serious breach of operational privacy. In this paper, we propose privacy-preserving mechanisms and techniques for an obfuscated geolocation database that can enable the coexistence of primary and secondary users while preserving the operational privacy of the primary users.