Keynotes
Cellular Positioning & Simultaneous Localization and Mapping (SLAM): Selected Recent Advances
Prof. Mikko Valkama, Tampere University, Finland, https://researchportal.tuni.fi/en/persons/mikko-valkama
In this talk, we discuss selected recent advances in cellular radio positioning and radio SLAM. While classical positioning seeks to provide information of the locations of the user equipment, SLAM allows to estimate and track also the locations of the landmarks and scattering points of the environment for enhanced situation awareness. We discuss and shortly review the 5G NR reference signals available in uplink and downlink for positioning and SLAM, while also present recent advances in sophisticated random finite set (RFS) based filtering solutions allowing for probabilistic modelling of landmarks and the corresponding birth and death processes. Another recent paradigm of turning the user equipment into monostatic and/or collaborative bistatic mobile radars while transmitting the uplink data signal is also discussed.
Integrated Wi-Fi Sensing and Communication: Signal Processing, Deep Learning and Standards Activities
Dr. Perry Wang, Senior Principal Research Scientist, Mitsubishi Electric Research Laboratories https://www.merl.com/people/pwang
Wi-Fi has become an integral part of daily life, permeating homes, offices, and public spaces such as shopping malls and transportation hubs. Beyond the nominal role of data transmission, its widespread adoption offers unique opportunities for indoor sensing, thereby benefiting a variety of end-user applications, including home security, entertainment, building energy management, elderly care, and assisted living, with fewer privacy concerns compared to visible light sensors. In this keynote talk, we will delve into the fundamentals of Wi-Fi waveforms and packets, examining formats such as RSSI (receiver signal strength indicator), CSI (channel state information), and beam training measurements, discuss signal processing techniques for high-resolution range, angle, and Doppler measurements, and explore recent advancements in deep learning frameworks that enable both with-device and device-free sensing applications. Finally, we will provide updates on the IEEE 802.11 Standards activities related to Wi-Fi sensing, highlighting open issues such as privacy and security concerns and noting new directions to unlock the full potential of Wi-Fi sensing technologies.
Physical Layer Secure Design of Dual-Function Radar-Communication Systems
Prof. Athina Petropulu, Rutgers University, https://www.ece.rutgers.edu/Petropulu
Dual-function radar-communication (DFRC) systems are integrated sensing-communication systems that utilize the same waveform for both probing the surroundings and communicating with other equipment. These systems offer high spectral, hardware, and power efficiency, making them attractive for 6G wireless applications, such as autonomous driving vehicles, unmanned aerial vehicles, surveillance, search and rescue, and advanced manufacturing. However, security is a significant concern for DFRC systems, as communication information embedded in the probing waveform can be intercepted by targets who are also eavesdroppers. In this talk, we will present a novel physical layer security (PLS) system design that optimizes communication secrecy rate while ensuring sufficient power in target echoes to maintain high target sensing performance. We will discuss PLS challenges that arise in dynamic environments, using a principled deep reinforcement learning (RL) framework for system design. Analytical solutions will be used as intrinsic motivation for guided exploration in RL, with the potential to scale solutions with the size of the IRS. We will also introduce a novel Directional Modulation (DM) approach for achieving PLS, in which the DFRC system delivers the signal intact to the legitimate destination and scrambles it in all other directions. We will examine the possibility of targets and eavesdroppers circumventing proposed security measures and investigate additional protection measures.