Wireless spectrum is a scarce commodity. 5G and beyond cellular systems are venturing more and more into unlicensed spectrum, leading to increased coexistence of a multitude of wireless systems. To tackle this coexistence, efficient spectrum utilization techniques, such as spectrum sharing (SS) and full-duplex (FD) transmission have been considered, allowing also simultaneous transmission and sensing, opening up avenues for new random-access schemes. On the other hand, much research has been done on multi-user (MU) MIMO communications. Nevertheless, almost all current spectrum sharing schemes in standardized wireless systems are based on taking turns in spectrum occupation, whereas multiple antennas can enable simultaneous co-existence. The objective of this tutorial is to provide an overview of the following ingredients: 1) key SS approaches (from cognitive radio to enhanced Licensed Shared Access (eLSA), WiFi-5G coexistence, Automated Frequency Coordination (AFC) in WiFi7 etc.); 2) reminder of what we can do with multiple antennas (SU MIMO, MU MIMO, Interference Alignment), multi-antenna cognitive radio paradigms, including Channel State Information at the Transmitter (CSIT) acquisition and channel reciprocity calibration issues, dynamic TDD, reciprocity-based beamforming; 3) state-of-the-art MU-MIMO transmitter/receiver designs for various utility optimization problems, rate balancing, weighted sum rate, uplink/downlink duality; perfect CSIT designs: from weighted sum rate to weighted sum MSE, minorization maximization, Signal to Leakage plus Noise, Interference Aware Water Filling, Deterministic Annealing for global optimization or local optimum identification, interference covariance shaping and constrained optimization duality, power method for reduced complexity generalized eigenvectors; imperfect CSIT designs: imperfect CSIT channel models, location based CSIT, pathwise and non-Kronecker models, CSIT acquisition, distributed designs, utility optimization with imperfect CSIT, naïve UL/DL duality. In summary, in this tutorial we review the state of the art on transmitter/receiver design in MIMO systems, including some recent developments. But we also point out a variety of open problems that persist. In particular we provide various takes on distributed solutions and complexity reduction (e.g. beam space), leading to a tradeoff between performance/overhead/complexity, allowing some suboptimality (e.g. reduced-order zero-forcing) for significant complexity reductions (e.g. non-iterative designs), overhead reduction (eg naïve UL/DL duality); various takes on CSIT acquisition, including covariance CSIT, prediction for TDD etc. Various takes on utility functions: can we do everything with weighted sum rate?

In this tutorial, we focus on the recent developments toward distributed integrated sensing and communications (ISAC). We consider a broad definition of coexistence, which covers ISAC, collaborative communications, and sensing with interference. Toward fully realizing the coexistence of the two systems, optimization of resources for both new/futuristic sensing and wireless communications modalities is crucial. These synergistic approaches that exploit the interplay between state sensing and communications are both driving factors and opportunities for many current signal processing and information-theoretic techniques. A large body of prior works consider colocated ISAC systems and distributed systems remain relatively unexamined. Building on the existing approaches, the tutorial focuses on highlighting emerging scenarios in collaborative and distributed ISAC, particularly at mm-Wave and THz frequencies, highly dynamic vehicular/automotive environments that would benefit from information exchange between the two systems. It presents the architectures and possible methodologies for mutually beneficial distributed co-existence and co-design, including sensor fusion and heterogeneously distributed radar and communications. The tutorial also considers recent developments such as deployment of intelligent reflecting surfaces (IRS) in ISAC, 5G systems, passive internet-of-things, and ISAC secrecy rate optimization. This tutorial aims to draw the attention of the radar, communications, and signal processing communities toward an emerging area, which can benefit from the cross-fertilization of ideas in distributed systems.

Autonomous driving is one of the megatrends in the automotive industry, and a majority of car manufacturers are already introducing various levels of autonomy into commercially available vehicles. The main task of the sensing suite in autonomous vehicles is to provide the most reliable and dense information on the vehicular surroundings. Specifically, it is necessary to acquire information on drivable areas on the road and to port all objects above the road level as obstacles to be avoided. Thus, the sensors need to detect, localize, and classify a variety of typical objects, such as vehicles, pedestrians, poles, and guardrails. Comprehensive and accurate information on vehicle surroundings cannot be achieved by any single practical sensor. Therefore, all autonomous vehicles are typically equipped with multiple sensors of multiple modalities: radars, cameras, and lidars. Lidars are expensive and cameras are sensitive to illumination and weather conditions, have to be mounted behind an optically transparent surface, and do not provide direct range and velocity measurements. Radars are robust to adverse weather conditions, are insensitive to lighting variations, provide long and accurate range measurements, and can be packaged behind optically non-transparent fascia. The uniqueness of automotive radar scenarios mandates the formulation and derivation of new signal processing approaches beyond classical military radar concepts. The reformulation of vehicular radar tasks, along with new performance requirements, provides an opportunity to develop innovative signal processing methods. This Tutorial will first describe active safety and autonomous driving features and associated sensing challenges. Next it will overview technology trends and state advantages of available sensing modalities and describe automotive radar performance requirements. It will discuss propagation phenomena experienced by typical automotive radar and radar concepts that can address them. Next this tutorial will focus on the radar equation and the radar processing chain: range and Doppler measurement estimation, beamforming, detection, range and angle-of-arrival migration, tracking and clustering. Discussing modern automotive radars, the tutorial will describe MIMO radar methods. Finally, the automotive radar applications and advanced topics, such as interference mitigation, and sensor fusion will be discussed.

Massive MIMO has been one of the breakthrough technologies that has contributed to the recent impressive evolution of wireless communications. Recently, researchers have started to go beyond the originally conceived massive MIMO idea, which envisioned the use of co-located large-scale antenna arrays and are proposing new solutions to further advance the technology and improve the network performance. Specifically, traditional multicell massive MIMO systems do not ensure good performance to cell-edge users who happen to be located at approximately the same distance from the serving base stations and from a certain number of interfering ones. In this situation, interference management and cooperative scheduling strategies are needed to avoid poor performance. Similarly, multicell massive MIMO deployments provide limited large-scale fading diversity and thus sensitive to blockages, especially when considering higher frequencies such as millimeter-waves. To overcome these limitations, the use of distributed antenna arrays, an idea that dates back well before the advent of massive MIMO, has reappeared as a serious evolution of multicell massive MIMO. In this tutorial, we first briefly underline the potentialities and limitations of the traditional massive MIMO technology and then give the audience an idea of the main technologies representing the evolution of the original idea. Then, we consider, from a signal processing perspective, two main 6G key-enabling technologies: Cell-free massive MIMO and reconfigurable intelligent surfaces. In the first technology, a large number of access points equipped with few antennas and connected to one or several central processing units serve a smaller group of users. Distinguishing features are the use of the time division duplex protocol, and the fact that both channel estimation and beamforming computation can happen locally at each access point, with no need to centralize the signal processing. Reconfigurable intelligent surfaces (RISs) are programmable structures that can control the propagation of electromagnetic waves by changing the electric and magnetic properties of the surface. These surfaces reflect the signal transmitted from a source and can change the amplitude and phase of the impinging wave to improve the performance of the communication. Finally, in the last part of the tutorial, we discuss two novel architectures: the extremely large aperture arrays and the large sequentially distributed arrays detailing the signal processing aspects of the transceiver structure and data detection. The extremely large aperture arrays have extremely large dimensions and are deployed as part of a large infrastructure, for example along the walls of buildings in a mega-city, in airports, large shopping malls or along the structure of a stadium. These kinds of architecture pose new signal processing challenges due to the larger antenna spacing and the relative concept of near-field and far-field regions. The large sequentially distributed arrays technology assumes that the topology of the network and all the signal processing is carried out in a sequential manner. It should be noted that these technologies are based on the same communication-theoretic fundamentals principles of multiantenna systems, have several common strengths (promise huge performance gains), common features (provide large-scale fading diversity), and pose common challenges. The aim of this tutorial is to put emphasis on the signal processing challenges associated to these technologies with regard to channel estimation, beamforming design, and decoding strategies. We underline the potentialities and the main features of these technologies, discussing the signal processing techniques at the basis of their functionalities.

Integrated Sensing and Communications Systems (ISAC) sense radio frequency spectrum and transfer wireless data jointly. They operate in a shared and congested, possibly even contested-spectrum with the goal of improving both communications and radar performances. We are considering ISAC 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 ISAC 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 ISAC 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 can be modeled 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 in ISAC 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.

How do you imagine the future communication networks? Which are going to be their enabling new technologies: holographic arrays, quantum communications? Trying to answer these and related questions, researchers worldwide have begun to study new avenues, because the future networks are expected to be a wise combination of disruptive technologies and improved existing ones in 5G. Can you imagine a user centric network that you can activate whenever and wherever you are? A network with distributed intelligence and memory, that is able to transmit at terabits per second and to carry out fast computing over the air, in order to automate decisions and to enable a sustainable and always-best-connected network? You should not think only about big cities, but also about small villages, ad-hoc communities, oceans, … Such a vision is only possible if terrestrial and satellite communications become just one. We are most familiar with terrestrial radio communications, but what about satellite communications? When and where are they used? How do they operate? This talk brings satellite communications (satcom) closer to the audience with a combination of tutorial description and new avenues for research focused on the role of array processing at the physical and access layer. This will pave the way towards a new communication paradigm that allows terrestrial and satellite segments to better integrate into a 3D network.

The Green Transition is the combined efforts of the global community to move towards a sustainable society and combat the effects of climate change. This will impact all facets of our society, and require bold political, societal, and technological change to succeed. The EU has responded with a "European Green Deal": a set of policy initiatives with the main goal to make EU climate neutral by 2050. To highlight the need for digital technologies, the European Commission has stated: "There is no Green Deal without digital". The strong link between the Green and Digital Transition, also called "The Twin Transition", lies at the heart of the strategy and research activities at SINTEF Digital. We conduct research and innovation in digital technologies and technology-oriented social sciences. Covering the entire digital value chain from advanced sensors to big data and AI, our strategy contains prioritized areas of research that directly addresses the challenges of the Twin Transition. In this keynote, we focus on research activities in SINTEF Digital that target the specific challenges that stem from the exponential growth of sensors, sensor data and advanced signal processing. Examples from ongoing research activities are presented along with the role of SINTEF Digital as a partner for research, development, and innovation. From local involvement with start-ups and SMEs, to international collaboration with academic peers, we strive to stay ahead in the rapidly evolving research areas of digital sciences.

With today's technology, radio frequency front-end architectures are very similar in radar and wireless communication systems. Further, in an effort to access more bandwidth, wireless systems have been shifting to frequency bands that have been traditionally occupied by radar systems. Given the hardware and frequency convergence, there is a lot of recent interest in the integration of the radar and communication functions in one system. Such integration will enable more efficient use of spectrum, reduce device size/cost and power consumption, and will also offer the potential for significant performance enhancement of both sensing and communication functions. Dual Function Radar-Communication (DFRC) systems is a class of integrated sensing-communication (ISC) systems that use the same waveform as well as the same hardware platform for both sensing and communication purposes. Thus, DFRC systems can achieve higher spectral efficiency than most ISC systems, require simpler transmitter hardware and a smaller, less expensive device. DFRC systems are prime candidates for autonomous driving vehicles, unmanned aerial vehicles, surveillance, search and rescue, and networked robots in advanced manufacturing applications that rely on censing and communications.

In the talk, we will present a novel DFRC system that uses the available bandwidth efficiently for both communication as well as sensing. The system transmits wideband, orthogonal frequency division multiplexing (OFDM) waveforms and allows the transmit antennas to use subcarriers in a shared fashion. When all subcarriers are used in a shared fashion, the proposed system achieves high communication rate, while its sensing performance is limited by the size of the receive array. By reserving some subcarriers for exclusive use by transmit antennas (private subcarriers), the communication rate can be traded off for improved sensing performance. The improvement is achieved by using the private subcarriers to construct a large virtual array that yields higher resolution angle estimates. The system is endowed with beamforming capability, via waveform precoding, where the precoding matrix is optimally designed to meet a joint sensing-communication system performance metric. We also present novel hybrid analog-digital architectures for achieving good performance with reduced hardware and energy cost via the use of double-phase shifters.

In this talk, we present a gridless channel estimation scheme for MIMO OFDM systems in the millimeter wave (mmWave) band that is based on R-D Unitary Tensor-ESPRIT in DFT beamspace. Compared to conventional ESPRIT based algorithms in element space, the beamspace approach can be applied to MIMO systems with hybrid architectures. Moreover, the proposed scheme significantly reduces the training overhead for communication systems operating in the mmWave band. The proposed procedure involves coarse and fine estimation steps. During the coarse estimation step, Unitary Tensor-ESPRIT in element space may be applied to the array with a reduced size aperture to obtain initial information about the directions of arrival, the directions of departure, and the propagation delays of the dominant multipath components. Based on these estimates, a more accurate estimation of the angular profiles, propagation delays, and channel gains is performed in a second step by applying 3-D Unitary Tensor-ESPRIT in DFT beamspace in the spatial domains combined with the element space version in the frequency domain. We explain how to combine the received signals from different spatial sectors of interest and how to perform joint processing. The simulation results confirm the tensor gain of the proposed procedure in addition to the improved channel estimation accuracy.