Program

6G is envisioned to continue the digital transformation from connected people and things, to connected intelligence. Applications such as: interactive immersive experience, machine collaboration in unmanned factories, smart healthcare with real-time body sensing, and ultimately autonomous driving will be realized on a large scale during the next decade. This will lead to significantly higher network performance requirements, such as Tbit/s communication rates, centimeter-level positioning precision, and millimeter-level sensing resolution. Integrated sensing and communication (ISAC) and Network for AI are believed to be key new features of next-generation wireless communications. The use of higher frequency bands (from mmWave up to THz), wider bandwidth, and massive antenna arrays in future 6G systems will enable the tight integration of RF sensing and communication to mutually enhance each other and reduce the overall cost. The cellular system can also serve as a networked sensor. It can explore the radio wave transmissions, reflections, and scattering to sense and better understand the physical world, providing a broad range of new services. Sensing-assisted communication such as location-based beamforming and tracking could improve communication performance. Furthermore, combined with AI technologies, ISAC is the key enabling technology for the fusion of physical, biological and cyber worlds in the future to come.

In this talk, I will first present a high-level view of the anticipated 6G, which will serve as the introduction for the discussion of motivations, envisioned use cases, and requirements of ISAC. In addition, the challenges, potential technologies, and research directions will be discussed.

Data communication waveforms are wideband random processes with excellent ``ambiguity function properties’’ and therefore they can be exploited for radar sensing. However, the required signal processing to extract relevant parameters from the backscattered signal is significantly more involved than with classical radar waveforms. This may require a full-duplex Tx/Rx architecture and carefully designed hybrid digital-analog beamforming in order to cope with the fact that demodulation and A/D conversion of the received signals at a large antenna array may be infeasible in terms of power consumption and complexity. When these problems can be solved, the synergy between communications and radar sensing may provide very relevant benefits ``for free’’, i.e., without using additional bandwidth and transmit power. In this talk, we shall present a number of relevant scenarios where radar sensing using OFDM and OTFS modulation formats can provide very useful information for the initial beam acquisition phase (the so-called beam alignment) and/or for the beam refinement and tracking, once the communication is established. We argue that such radar-aided communication schemes can be very useful for automotive applications at mmWave bands, where the relatively high mobility makes handovers (initial beam acquisition) and beam steering/selection particularly challenging.

The famous Shannon-Nyquist theorem has become a landmark in analog to digital conversion and the development of digital signal processing algorithms. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power. In this talk we consider a general framework for sub-Nyquist sampling and processing in space, time and frequency which allows to dramatically reduce the number of antennas, sampling rates, number of bits and band occupancy in a variety of applications. It also allows for the development of efficient joint radar-communication systems. Our framework relies on exploiting signal structure, quantization and the processing task in both standard processing and in deep learning networks.  We consider applications of these ideas to a variety of problems in wireless communications, efficient massive MIMO systems, automotive radar and ultrasound imaging and show several demos of real-time sub-Nyquist prototypes including a wireless ultrasound probe, sub-Nyquist automotive radar, cognitive radio and radar, dual radar-communication systems, analog precoding, sparse antenna arrays, and a deep Viterbi decoder.

We will present our recent work on dual-function radar communication (DFRC) systems, in particular a new MIMO radar with a sparse transmit array, that transmits wideband, OFDM waveforms. The system assigns most carriers to antennas in a shared fashion, thus efficiently exploiting the available communication bandwidth, and a small set of subcarriers to active antennas in an exclusive fashion (private subcarriers). A novel target estimation approach will be presented to overcome the coupling of target parameters introduced by subcarrier sharing. The system is endowed with beamforming capability, via waveform precoding and antenna selection. The precoding and antenna selection matrices are optimally co-designed to meet a joint sensing-communication system performance. The use of shared subcarriers enables high communication rate, while the sparse transmit array maintains low system hardware cost. The sensing problem is formulated by taking into account frequency selective fading, and a method is proposed to estimate the channel coefficients during the sensing process.

Mobile network is evolving from communication-only towards one with joint communication and radio/radar sensing (JCAS, aka, ISAC) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and may go beyond the functions of localization, tracking, and object recognition of traditional radar. In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable many novel smart applications, whilst providing non-compromised communications.

In this talk, we review systems and critical technologies that enable JCAS in PMN. We first introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. We then discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, focusing on joint waveform design and optimization, sensing parameter estimation, and resolution of sensing ambiguity due to clock asynchronism. 

Spectrum sharing enables multiple categories of users to share the same frequency bands and is becoming increasingly important in responding to the growing congestion of spectral resources. Among different solutions, joint radar-communication (JRC) achieves both radar sensing and communication objectives in shared system platforms and frequency bands. In this talk, we first present signaling strategies in JRC systems to embed digital communication information in radar functions. Information embedding is achieved by designing signals carrying information in their magnitude, phase, or their combinations and utilizing waveform diversity. Information embedding and delivery can support both multicast and unicast communications to pre-assigned directions, thereby being inherently secure against eavesdropping. We further consider resource optimization in JRC systems with optimized antenna selection. We will subsequently illustrate recent advances in array processing related to sparse antenna array design and robust beamforming for various sensing and communication applications. By exploiting the sparsity of signals distributed in the spatial domain, accurate interference-plus-noise covariance matrix reconstruction can be achieved through the estimation of sparse spatial spectra, thereby providing robust beamforming for interference cancellation. Sparsity-based synthesis of full antenna array and frequency spectrum enables effective sensing using sparse arrays and thinned frequency spectra.

Future Smart Cities will be connected, automated and secure, and will rely on high-reliability communication and sensing functionalities to accommodate a growing number of applications, such as infrastructure monitoring and security, intelligent mobility, at-home healthcare, among others. The independent growth of radar and communication systems is not sustainable and will lead to a congestion of devises, emitters and sensors. A new generation of technologies tailored for Dual-Functional Radar and Communications (DFRC) systems has been receiving growing interest in the wireless research. This talk will overview recent research on DFRC signalling design, and highlight key benefits, opportunities and challenges of DFRC.

Future Smart Cities will be connected, automated and secure, and will rely on high-reliability communication and sensing functionalities to accommodate a growing number of applications, such as infrastructure monitoring and security, intelligent mobility, at-home healthcare, among others. The independent growth of radar and communication systems is not sustainable and will lead to a congestion of devises, emitters and sensors. A new generation of technologies tailored for Dual-Functional Radar and Communications (DFRC) systems has been receiving growing interest in the wireless research. This talk will overview recent research on DFRC signalling design, and highlight key benefits, opportunities and challenges of DFRC.

Joint radar–communications (JRC) systems integrate radio frequency sensing and communications. They operate in a shared and congested, possibly even contested–spectrum with the goal of improving both communications and radar performances. We are considering JRC systems that cooperate or are co-designed for mutual benefits. Co-designed systems may share waveforms, hardware, and antenna resources. Moreover, awareness about channel state and interference is typically exchanged. The JRC systems have a number of degrees of freedom (DoF) and operational parameters that can be selected or adjusted to optimize their performance either by using structured optimization or machine learning. Examples of such parameters are frequency band, beampatterns, antenna selection, the modulation method, precoder–decoder designs, and power allocation. We focus on multicarrier waveforms used by most current and emerging wireless communication systems. Similarly, multicarrier waveforms have been employed for radar purposes. Radars have a variety of tasks such as target detection, tracking, parameter estimation and recognition with different objectives. We will present waveform optimization, reinforcement learning, interference management and signal processing methods for co-designed JRC systems that share channel and interference awareness. Model-based reinforcement learning approach is taken to exploit the rich structural knowledge of man-made communication and sensing systems and radio wave propagation. Optimizing operational parameters is modeled either as a radar-centric or communications-centric constrained optimization problem where the minimum desired performance levels for other sub-systems impose the constraints. The developed OFDM radar algorithms can take advantage of nonidealities such as carrier offsets and phase noise that are commonly considered an impairment in wireless communications.  We demonstrate the achieved performance gains in different sensing and communication tasks and interference management through extensive simulation and analytical results.

Wireless communication systems are moving to higher frequency bands (mmWave in 5G and above 100GHz in 6G and beyond) and deploying large antenna arrays at the infrastructure and mobile users (massive MIMO, mmWave/terahertz MIMO, reconfigurable intelligent surfaces, etc.). While using large antenna arrays and migrating to higher frequency bands enable satisfying the increasing demand in data rate, they also introduce new challenges that make it hard for these systems to support mobility and maintain high reliability and low latency. In this talk, I will first motivate the use of sensory data and machine learning to address these challenges. Then, I will present DeepSense 6G, the world's first large-scale real-world multi-modal sensing and communication dataset that enables the research in a wide range of integrated sensing and communication applications. After that, I will go over a few machine learning tasks enabled by the dataset such as radar, LiDAR, camera, and position aided beam and blockage prediction. Finally, I will discuss some future research directions in the interplay of communications, sensing, and positioning. 

   In this talk, we first revisit linear time-varying channel’s representations in the time-frequency domain and the delay-Doppler domain. Then we briefly review recent development of orthogonal time frequency space (OTFS) modulation. Motivated by OTFS, we introduce a general multi-carrier (MC) modulation on delay-Doppler plane. A delay-Doppler plane orthogonal pulse (DDOP), which is essential for delay-Doppler plane MC modulation waveform, is presented. We investigate the frequency domain representation of the DDOP, and compare the DDOP-based MC modulation with other modulation schemes. Various low complexity detection algorithms for the DD plane MC modulation are discussed. Interestingly, we show perfect orthogonality property of the DDOP with respect to delay-Doppler resolutions using its ambiguity function, which infers that the proposed MC signal waveform is suitable for integrated sensing and communications (ISAC).

   Wireless networks are incorporating millimeter wave spectrum and beyond, with a clear trend on going up in frequency and bandwidth. This, together with MIMO  technology using large antenna arrays, provides the key ingredients to develop integrated sensing and communication systems (ISAC) that exploit the similarities between the required hardware and algorithms for sensing and communication. In this talk, I provide an overview of how signal processing and machine learning techniques can be integrated to  enable different types of collaboration between sensing and communication. First, I discuss algorithms for sensor-aided millimeter wave communication that significantly reduce the overhead associated with link configuration and reconfiguration or enable early blockage detection by wisely combing conventional estimation approaches with machine learning techniques that match sensing and communication channels. Second, I consider the opposite setting of communication-assisted sensing. I describe a hybrid model/data driven approach to high accuracy localization that extracts the position from the downlink or uplink mmWave communication signal. Finally, I describe how RIS-aided joint localization and communication is a potential avenue to further increase position estimation accuracy that can also benefit from integrating data and model driven approaches.

   Increases in carrier frequencies and bandwidths, driven by high-rate communication applications have led to vastly improved capabilities for user positioning. With research underway towards 6G, opportunities for integrating positioning and sensing into the communication system have become even more apparent. The aim of this talk is to provide an overview of this evolution, focusing on 5G and 6G. The talk will comprise 3 main parts: first, the foundations of radio-based positioning are introduced. Second, we go deeper into 5G positioning, covering both the standard approaches, as well as more forward-looking potential modifications. In the last part, we consider 6G from the perspective of positioning and sensing, highlighting some of the novel enablers, methods, potentials, but also the corresponding challenges.

   By exploiting illuminators of opportunity typically selected among the available radio transmitters for broadcast services or networking, passive radar can be regarded as a pioneering form of ISAC where the sensing functionality is totally subject to the design of the communications system and cannot rely on any cooperation. Apparently, in passive radar, the sensing component operates in the worst conditions and offers performance that is largely dependent on the constraints posed by the communications component thus requiring several challenges to be addressed. To this purpose, effective solutions have been designed and implemented in real operational passive radar systems exploiting different illuminators of opportunity, there including digital radio and television transmitters, base stations for local area and metropolitan area networking, as well as satellite transmitters for communications and radionavigation.

   This talk addresses both consolidated techniques and recent developments in the field of passive radar in various application scenarios, which span from air traffic control, including surveillance against UAV, maritime surveillance, vehicular traffic monitoring, up to indoor surveillance. In addition to the theoretical aspects, the talk provides examples from real‐world implementations of passive radar. Walking through these results gives the chance to describe in more detail some technical aspects related to system design issues and signal processing techniques as well as to understand the current limitations and future perspectives of passive radar sensing.

   The aim is to share the lesson learnt in the framework of passive radar in order to transition the experience gained by the relevant research community into valuable inputs to the development of ISAC systems where the increased design flexibility could enable enhanced performance and advanced capabilities, and hence widen their range of uses.

   One of the most pressing problems in the area of spectral congestion and dynamic frequency allocations is to provide uncontested shared bandwidth between radar and communications. This has recently spurred extensive efforts towards devising solutions for simultaneous operations of radar target illuminations and wireless services sharing the same frequency bandwidth. Recent research is moving to enable the embedding of communication signals into radar waveforms and beamforming. In so doing, the communication systems capitalize on the resources of the radar, including high power, large bandwidth, and high-quality hardware and, in essence, deal with the legacy radar as a” system of opportunity.”

This Webinar discusses different approaches that allow radar to house voice and data transmission, leading to technological advances in radar and communications systems. It touches on novel signaling schemes for embedding information into the radar pulsed emissions which, in most cases, is blind to the legacy radar operation and radar ambiguity function. It considers different antenna configurations, including multiple-input multiple-output (MIMO) radars and shows how to achieve high data rate communications by combining amplitude-shift keying, phase-shift keying, and code shift keying modulations with waveform-diversity and spatial degrees of freedom. The Webinar also examines the dual functionality problem for radar networks.

   This talk discusses how humans interact over a social network and make decisions based on sensor information. The interaction of social sensors presents several challenges from a statistical signal processing viewpoint: sensors  interact with and influence other social sensors resulting in herding behavior. The talk consists of two parts. 

Part 1 discusses change detection involving a human decision maker and a sequential detector. The human is modeled as an anticipatory agent that makes decisions by taking into account the probability of future decisions. We also consider a quantum decision framework which captures order effects and violation of the total probability rule that have been empirically observed in human decision making. The problems are time inconsistent and require a sub-game Nash equilibrium formalism.

Part 2 discusses novel methods to poll social networks based on expectation polling and the friendship paradox. We also discuss dynamic models for the glass ceiling effect in social networks where certain nodes control important positions and prevent other nodes from assuming important roles.

The seminar draws from ideas in statistical signal processing, controlled sensing, behavioral economics and mathematical psychology. Our methodology yields a useful framework for human decision makers interacting with sequential detectors.

In communications-enabled sensing, of particular interest in the millimeter Waves (mmWave) bandwidth - wherein radar and communications tend to be similar in both channel characteristics and signal processing - a radar receive chain is co-located with a communications transmitter, possibly sharing information, and suitably processes the backscattered signals in order to undertake short-range sensing of the environment.

Conversely, radar-enabled communications rely on ambient backscattering, produced by low-complexity and nearly zero-consumption devices, in order to sustain communication links: among the enabling factors for these architectures we find the recently developed technology of Reconfigurable Intelligent Surfaces (RIS), which are low-consumption, intelligent mirrors capable of redirecting astray rays towards a desired point.

The talk reviews some recent results concerning these two complementary paradigms, both inspired by the Integrated Sensing and Communications (ISAC) philosophy. 

This lecture deals with radar waveform design in spectrally dense environments with the goal of optimizing the radar detection performance without affecting the spectral compatibility with some licensed overlaid electromagnetic radiators. The signal synthesis is formalized in terms of some non-convex optimization problems under a variety of constraints reflecting the different characteristics to be forced on the radar waveform as well as the diverse available a-priori information on the environment. Receiver optimization is also included in the design for some situations where the radar operates in the presence of a reverberating scenario. Tradeoffs among detection performance, desirable features of the radar signal, and spectral compatibility are shown. 

WiFi/4G/5G based wireless sensing has attracted a lot of attention from both academia and industry in the last decade. However, fundamental questions such as the sensing limit, sensing boundary and sensing quality of WiFi/4G/5G signals have not been explored, making the wireless sensing system design and deployment in a trial-and-error manner. In this talk, I will first introduce the Fresnel zone model as a generic theoretic basis for device-free and contactless human sensing with WiFi/4G/5G signals, revealing the relationship among the received CSI signal, the distance between the two transceivers, the location and heading of the sensing target with respect to the transceivers, and the environment; as well as why most existing CSI-based human sensing systems don’t work robustly in real settings. Then we propose to define and deploy the Sensing Signal to Noise Ratio (SSNR) as a new metric to inform the sensing limit, sensing boundary and sensing signal quality of WiFi/4G/5G-based human sensing systems. We further apply the SSNR metric to show how we can push the sensing range of a commodity WiFi-based human respiration monitoring system to more than 30 meters by exploiting the time, space and frequency diversity of WiFi signals.

Integrated Sensing and Communication (ISAC) is recognized as a promising technology for both the next-generation wireless networks and radar systems. In this talk, we consider a P2P ISAC model under vector Gaussian channels, and propose to use the CRB-rate region as a basic tool for depicting the fundamental sensing and communications (S&C) tradeoff. We characterize the S&C performance at the two corner points of the CRB-rate region. In particular, we derive the high-SNR communication capacity at the sensing-optimal point, and provide lower and upper bounds for the sensing CRB at the communication-optimal point. Our main results reveal a two-fold tradeoff in ISAC systems, consisting of the subspace tradeoff (ST) and deterministic-random tradeoff (DRT) that depend on the resource allocation and data modulation schemes employed for S&C, respectively.