The New England Workshop on Software Defined Radio (NEWSDR) was held in May this year, and there have been several talks now uploaded to YouTube. These are typically fairly technical in nature, and discuss cutting edge research being performed with software defined radios. Below we post a few selected talks, and the rest can be viewed in this WPI playlist.
Remote Sensing of the Space Environment Using Software Defined Radio
From studies of the ionosphere to astronomical measurements with arrays of radio telescopes the manipulation and analysis of RF signals has been key to new instrumentation and many resulting discoveries. Software radio technology has been a core component of remote sensing of the space environment for several decades now. The flexibility of combining computing and radio was adopted very early on in scientific applications. This enabled new classes of scientific experiments which would otherwise have been impossible. The capability and adaptability of software radio instrumentation and systems has been growing consistently with the exponential increase in available computing power. The recent surge of low cost software radio hardware technology has enabled a new generation of instrumentation. These instruments are increasingly blurring the line between traditionally separate scientific disciplines as well as practical applications. I will discuss the science and the instrumentation enabled by software radio with highlights from studies of the ionosphere and radio astronomy. My overview will focus on the relationship to work underway at MIT Haystack Observatory. I will highlight the core architectural patterns of scientific software radio and discuss the evolution of our systems over several decades of rapid technological change. I will also look forward to the possibilities for discovery offered by the next generation of software radio and radar instrumentation.
Reinventing Wireless with Deep Learning
While wireless communications technology has advanced considerably since its invention in the 1890s, the fundamental design methodology has remained unchanged throughout its history – expert engineers hand-designing radio systems for specific applications. Deep learning enables a new, radically different approach, where systems are learned from wireless channel data. This talk will provide a high-level overview of deep learning applied to wireless communications, discuss the current state of the technology and research, and present a vision for the future of wireless engineering using a data-centric approach.
Multi-objective SDR Optimization for Wireless Access, Actuation and Attacks
Software defined radios (SDRs) have become the foundational block of agile wireless communications. The first part of the talk presents an overview of how the same SDR can alternate between multiple different and non-traditional actuation functions, such as aerial distributed beamforming and wireless energy transfer. Furthermore, as SDR technology becomes more pervasive assuming roles beyond communication, there is a growing risk of security concerns of ID spoofing and malicious hardware attacks. The second part of this talk describes our efforts of fingerprinting individual SDRs using machine learning, where we only analyze the I/Q samples collected at the receiver. We demonstrate the feasibility of achieving 90-95% classification accuracy through experiments conducted with 12 radios, at separation distances of beyond 50 feet. The talk concludes with a summary of the challenges ahead and identifies other emerging application areas of SDRs that will impact the next decade.