IEEE North Jersey Section

Deep neural networks make it possible to learn key characteristics of data without having to assume mathematically tractable models. This in turn results in an ability to compress data in a model-free way. One of the promising areas for the application of deep learning to the physical layer of communication networks is compression. Hundred-fold compression with a small loss of the CSI in massive MIMO systems has been shown to be both feasible and necessary. However, model-free and data driven compression comes with a downside: the encoding and decoding models need to be trained on a large set of CSI arrays indicative of a wide spectrum of propagation and environmental conditions. As a result, in the early stages of the deployment of deep CSI compression models, it would be necessary to detect if and when users’ channels have drifted significantly away from the distribution of the CSI data on which the deep compression model was trained. In this paper, we present both 1) a technique for detecting harmful channel drift and 2) a lightweight scheme for fine-tuning the deep compression models to adjust to such shifts. Using public-domain synthetic channel data as well as 3GPP-compliant simulated data, we demonstrate the practicality of our proposed deep compression and detection framework. We close with recommendations for a viable implementation of the proposed drift detection by the standards bodies. Co-sponsored by: New Jersey Coast Section Chapters, ComSoc Chapter, COM19, Jt Chp,ED15/MTT17/PHO36 and Jt. Chapter,SP01/CAS04, and North Jersey Section Chapter,COM 19 Speaker(s): Dr. Kursat Metsav, Agenda: 06:30 p.m. - Introduction of Dr. Kursat Metsav 06:35 p.m. - Presentation by Dr. Kursat Metsav 07:10 p.m. - Question & Answer session Virtual: https://events.vtools.ieee.org/m/476641

This webinar will provide a comprehensive examination of the role of Digital Pre-Distortion (DPD) in mitigating distortion effects, enhancing linearity, and improving overall system efficiency. Participants will acquire insights into advanced DPD techniques, practical implementation strategies, and the latest tools for effective analysis of Error Vector Magnitude (EVM) and DPD performance. A thorough understanding of EVM is essential for assessing the quality of transmitted signals. The discussion will include an in-depth analysis of amplifier testing and its critical importance in evaluating system performance. Through the presentation of real-world examples and case studies, this session aims to equip engineers and industry professionals with the knowledge necessary to optimize their designs. Co-sponsored by: IEEE North Jersey Section Speaker(s): Martin Lim Virtual: https://events.vtools.ieee.org/m/481323

Robotics and AI Trip at Montclair Enjoy the robotics and AI project demonstrations and hands-on interactions with robots and AI systems at Montclair. [] Bldg: CCIS, 1 Normal Ave, Montclair, New Jersey, United States, 07043

Talk 1 The half-wave dipole is a widely used antenna in Amateur Radio and other communications. It is often one of the first antennas studied in college courses. Professor R.W.P. King dedicated over 100 years to studying dipoles, and his accurate measurements continue to validate modeling software. Professor John Kraus, through his 1950 book Antennas, inspired many in the field, including the speaker. This presentation will explore key topics related to the half-wave dipole. First, we will assess the accuracy of various antenna modeling codes, using a unique surface model in FEKO as a reference dipole compared to wire Method of Moments (MoM) methods. We will also review the famous formula 468/f, which calculates the length of a half-wave dipole in feet (where f is in MHz), and discuss its effectiveness and common misconceptions. It does not reliably tune all antennas to resonance for different wire or tubing diameters, so we will provide a simple interpolation method for adjustments. Lastly, we will present a new design method for constructing a half-wave dipole antenna that is independent of the conductor's diameter, whether wire or tubing. Talk 2 Nearly every aspect of society relies on Positioning, Navigation, and Timing (PNT) services from Global Navigation Satellite Systems (GNSS) like the Global Positioning System (GPS). However, GPS signals are vulnerable to spoofing and jamming due to their unauthenticated nature and weak signal strength at the Earth's surface. Implementing such attacks is relatively easy with low-cost hardware and open-source software, leaving many regions susceptible to these threats. Research over the past few decades has focused on improving PNT performance amid jamming and spoofing, typically categorized into five main approaches: 1) signal processing methods, 2) antenna-based methods, 3) artificial intelligence (AI) techniques, 4) non-GNSS sensors, and 5) hybrid methods combining various strategies. This presentation will provide an overview of GPS and GNSS technologies, common attack strategies, and various mitigation methods, primarily focusing on antenna-based techniques. It will introduce a taxonomy of these techniques aimed at enhancing signal reception by maximizing authentic GPS signals and minimizing those from attackers. The presentation will highlight well-known techniques and ongoing research and discuss key research gaps and future directions. Co-sponsored by: IEEE North Jersey Section AP/MTT17, ED/CAS, and PHOTONICS Chapter Speaker(s): Prof. James K. Breakall, Jack L. Burbank Agenda: 4:150 PM - Refreshments and Networking 4:30 PM-6:30 PM: Talk by Prof. James K. Breakall, Penn State University, University Park, PA 16802 and You do not have to be an IEEE Member to attend. Refreshments are free for all attendees. Please invite your friends and colleagues to take advantage of these Invited Lectures. Room: 202, Bldg: ECEC, 141 Warren St, New Jersey Institute of Technology, The Lewis and Julia P. Kieman Conference Room, Newark, New Jersey, United States, 07102