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Polarization is a fundamental property of electromagnetic radio signals but often neglected in localization studies. In this paper, we study the potential benefits of integrating the polarization dimension into localization applications. We develop a three-dimensional (3D) geometric channel model between a base station (BS) and user equipment (UE), both equipped with dual-polarized (DP) antennas, which offers fundamental insights into the angles of departure (AoD) from the BS to the UE as well as the 3D orientation of the UE. From the model, we identify the degrees of freedom (DoF) provided by the polarization dimension for localization solutions by evaluating the rank of the equivalent Fisher information matrix. Subsequently, we leverage these DoF to introduce three distinct localization applications: (i) 3D orientation estimation, (ii) 2D AoD estimation, and (iii) mixed 2D position and 1D orientation estimation for vehicular scenarios. Furthermore, for the three localization applications we identify their regions of operation in terms of the ranges of the angles of interest, to avoid any ambiguity occurrence through the estimation process, thereby guaranteeing unique solutions. Finally, we derive the Cramér-Rao lower bounds and numerically establish the efficiency of the proposed estimators.

Reconfigurable intelligent surfaces (RISs) have received considerable attention in applications related to localization. However, operation in multi-path scenarios is challenging from both complexity and performance perspectives. This study presents a two-stage low complexity method for joint three-dimensional (3D) localization and synchronization using multiple RISs. Firstly, the received signals are preprocessed, and an efficient deep learning architecture is proposed to initially estimate the angles of departure (AODs) of the virtual line of sight paths from the RISs to the user. Then, a hybrid asynchronous AOD time-of-arrival-based approach is proposed in the first stage to estimate an initial guess of the position of the user equipment (UE). Finally, in the second stage, an optimization problem is formulated to refine the position of the UE by effectively utilizing the estimated delays and the clock offset. Our comparative study reveals that the proposed method outperforms the existing methods in terms of accuracy and complexity. Notably, the proposed method showcases enhanced robustness against multipath effects when compared to the state-of-the-art approaches.

Vertical cavity surface-emitting laser (VCSEL)-based optical interconnects (OI) are crucial for high-speed data transmission in data centers, supercomputers, and vehicles, yet their performance is challenged by harsh and fluctuating thermal conditions. This paper addresses these challenges by integrating an ordinary differential equation (ODE) solver within the VCSEL communication chain, leveraging the adjoint method to enable effective gradient-based optimization of pre-equalizer weights. We propose a machine learning (ML) approach to optimize feed-forward equalizer (FFE) weights for VCSEL transceivers, which significantly enhances signal integrity by managing inter-symbol interference (ISI) and reducing the symbol error rate (SER).

Abstract Reconfigurable intelligent surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localisation, and sustainability requirements of next-generation wireless networks. In this paper, we first present the deployment scenarios for RIS-enabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardised interfaces. We identify various RIS deployment strategies and sketch the core architectural requirements in terms of RIS control and signalling, depending on the RIS hardware architectures and respective capabilities. Furthermore, we introduce and discuss, with the aid of simulations and reflect array measurements, two novel metrics that emerge in the context of RIS-empowered wireless systems: the RIS bandwidth of influence and the RIS area of influence. Their extensive investigation corroborates the need for careful deployment and planning of the RIS technology in future wireless networks.

Fractional delay-Doppler (DD) channel estimation in orthogonal time–frequency space (OTFS) systems poses a significant challenge considering the severe effects of inter-path interference (IPI). To this end, several algorithms have been extensively explored in the literature for accurate low-complexity channel estimation in both integer and fractional DD scenarios. In this work, we develop a variant of the state-of-the-art delay-Doppler inter-path interference cancellation (DDIPIC) algorithm that progressively cancels the IPI as estimates are obtained. The key advantage of the proposed approach is that it requires only a final refinement procedure reducing the complexity of the algorithm. Specifically, the time difference in latency between the proposed approach and the DDIPIC algorithm is almost proportional to the square of the number of estimated paths. Numerical results show that the proposed algorithm outperforms the other channel estimation schemes achieving lower normalized mean square error (NMSE) and bit error rate (BER).

Abstract The technology of reconfigurable intelligent surfaces (RISs) has been showing promising potential in a variety of applications relying on Beyond-5G networks. RIS can indeed provide fine channel flexibility to improve communication quality of service (QoS) or restore localization capabilities in challenging operating conditions, while conventional approaches fail (e.g., due to insufficient infrastructure, severe radio obstructions). In this paper, we tackle a general low-complexity approach for optimizing the precoders that control such reflective surfaces under hardware constraints. More specifically, it allows the approximation of any desired beam pattern using a pre-characterized lookup table of feasible complex reflection coefficients for each RIS element. The proposed method is first evaluated in terms of beam fidelity for several examples of RIS hardware prototypes. Then, by means of a theoretical bounds analysis, we examine the impact of RIS beams approximation on the performance of near-field downlink positioning in non-line-of-sight conditions, while considering several RIS phase profiles (including directional, random and localization-optimal designs). Simulation results in a canonical scenario illustrate how the introduced RIS profile optimization scheme can reliably produce the desired RIS beams under realistic hardware limitations. They also highlight its sensitivity to both the underlying hardware characteristics and the required beam kinds in relation to the specificity of RIS-aided localization applications.

Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand-new use cases by embracing emerging techniques, e.g., reconfigurable intelligent surface (RIS), integrated communication, localization, and sensing, and mmWave/THz communications. The wireless intelligence empowered by state-of-the-art artificial intelligence techniques has been widely considered at the transceivers, and now the paradigm is deemed to be shifted to the smart control of radio propagation environment by virtue of RISs. In this paper, we argue that to harness the full potential of RISs, localization and communication must be tightly coupled. This is in sharp contrast to 5G and earlier generations, where localization was a minor additional service. To support this, we first introduce the fundamentals of RIS mmWave channel modeling, followed by RIS channel state information acquisition and link establishment. Then, we deal with the connection between localization and communications, from a separate and joint perspective.

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.

While 5G is being deployed and the economy and society begin to reap the associated benefits, the research and development community starts to focus on the next, 6th Generation (6G) of wireless communications. Although there are papers available in the literature on visions, requirements and technical enablers for 6G from various academic perspectives, there is a lack of joint industry and academic work towards 6G. In this paper a consolidated view on vision, values, use cases and key enabling technologies from leading industry stakeholders and academia is presented. The authors represent the mobile communications ecosystem with competences spanning hardware, link layer and networking aspects, as well as standardization and regulation. The second contribution of the paper is revisiting and analyzing the key concurrent initiatives on 6G. A third contribution of the paper is the identification and justification of six key 6G research challenges: (i) “connecting”, in the sense of empowering, exploiting and governing, intelligence; (ii) realizing a network of networks, i.e., leveraging on existing networks and investments, while reinventing roles and protocols where needed; (iii) delivering extreme experiences, when/where needed; (iv) (environmental, economic, social) sustainability to address the major challenges of current societies; (v) trustworthiness as an ingrained fundamental design principle; (vi) supporting cost-effective global service coverage. A fourth contribution is a comprehensive specification of a concrete first-set of industry and academia jointly defined use cases for 6G, e.g., massive twinning, cooperative robots, immersive telepresence, and others. Finally, the anticipated evolutions in the radio, network and management/orchestration domains are discussed.

A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.

Human driven vehicles (HDVs) with selfish objectives cause low traffic efficiency in an un-signalized intersection. On the other hand, autonomous vehicles can overcome this inefficiency through perfect coordination. In this paper, we propose an intermediate solution, where we use vehicular communication and a small number of autonomous vehicles to improve the transportation system efficiency in such intersections. In our solution, two connected autonomous vehicles (CAVs) lead multiple HDVs in a double-lane intersection in order to avoid congestion in front of the intersection. The CAVs are able to communicate and coordinate their behavior, which is controlled by a deep reinforcement learning (DRL) agent. We design an altruistic reward function which enables CAVs to adjust their velocities flexibly in order to avoid queuing in front of the intersection. The proximal policy optimization (PPO) algorithm is applied to train the policy and the generalized advantage estimation (GAE) is used to estimate state values. Training results show that two CAVs are able to achieve significantly better traffic efficiency compared to similar scenarios without and with one altruistic autonomous vehicle.

We propose pro-social control strategies for connected automated vehicles (CAVs) to mitigate jamming waves in mixed-autonomy multi-lane traffic, resulting from car-following dynamics of human-driven vehicles (HDVs). Different from existing studies, which focus mostly on ego vehicle objectives to control CAVs in an individualistic manner, we devise a pro-social control algorithm. The latter takes into account the objectives (i.e., driving comfort and traffic efficiency) of both the ego vehicle and surrounding HDVs to improve smoothness of the entire observable traffic. Under a model predictive control (MPC) framework that uses acceleration and lane change sequences of CAVs as optimization variables, the problem of individualistic, altruistic, and pro-social control is formulated as a non-convex mixed-integer nonlinear program (MINLP) and relaxed to a convex quadratic program through converting the piece-wise-linear constraints due to the optimal velocity with relative velocity (OVRV) car-following model into linear constraints by introducing slack variables. Low-fidelity simulations using the OVRV model and high-fidelity simulations using PTV VISSIM simulator show that pro-social and altruistic control can provide significant performance gains over individualistic driving in terms of efficiency and comfort on both single- and multi-lane roads.

A multi-channel backscatter communication and radar sensing system is proposed and demonstrated in this paper. Frequency modulated continuous wave (FMCW) radar ranging is integrated with simultaneous uplink data transmission from a self-packaged active radio frequency (RF) tag. A novel package solution is proposed for the RF tag. With the proposed package, the RF tag can transmit a 32-QAM signal up to 2.5 Gbps and QPSK signal up to 8 Gbps. For a multi-tag scenario, we proposed using spread spectrum code to separate the data from each tag. In this case, tags can be placed at arbitrary locations without adjacent channel interference. Proof-of-concept simulations and measurements are demonstrated. A 625 Mbps data rate is achieved in a dual-tag scenario for two tags.

A promising solution to meet the demands on accurate positioning and real-time situational awareness in future intelligent transportation systems (ITSs) is cooperative positioning, where vehicles share sensor information over the wireless channel. However, the sensing and communication technologies required for this will be gradually introduced into the market, and it is, therefore, important to understand what performance we can expect from cooperative positioning systems as we transition to a more modern vehicle fleet. In this paper, we study what effects a gradual market penetration has on cooperative positioning applications, through a Fisher information analysis. The simulation results indicate that solely introducing a small fraction of automated vehicles with high-end sensors significantly improves the positioning quality but is not enough to meet the stringent demands posed by safety critical ITS applications. Furthermore, we find that retrofitting vehicles with low-cost satellite navigation receivers and communication have marginal impact when the positioning requirements are stringent and that the longitudinal road position can be estimated more accurately than lateral.

Millimeter-wave (mm-wave) and massive multiple-input-multiple-output technologies appear as key enablers in future emerging wireless communication systems. Propagation in mm-wave communications is well described by geometric channel models, where a clear relation between communication and positioning arises. On one hand, initial access, a procedure that precedes high data rate transmission and consists of beam selection and alignment between two devices, can benefit from position information. On the other hand, accurate positioning relies on high-quality communication links with proper beam alignment. This work analyzes the interplay between communication and positioning, proposes a new in-band position-aided beam selection protocol considering scenarios with line-of-sight and reflected paths, and possible beam alignment errors. Simulation results show significant reductions in latency with respect to standard beam selection protocols.

In this paper, we propose various localization error optimal beamforming strategies and subsequently study the trade-off between data and localization services while budgeting time and frequency resources in a multi-user millimeter-wave framework. Allocating more resources for the data service phase instead of localization would imply higher data rate but, concurrently, also a higher position and orientation estimation error. In order to characterize this trade-off, we firstly derive a flexible application-dependent localization error cost function combining the Cramér-Rao lower bounds of delay, angle of departure and/or angle of arrival estimates at a mobile receiver over the downlink. Consequently we devise different fairness criteria based localization error optimal beamforming strategies in a multi-user context. Finally, we show the advantage of the latter beamforming strategies and assess the communication-localization trade-off with respect to various time-frequency resource division schemes.

Intersection management of automated vehicles relies on wireless communication, whereby communication resources should be allocated to vehicles while maintaining safety. We present a collision-aware resource allocation (CARA) strategy for coordination of automated and connected vehicles by a centralized intersection manager. The proposed strategy is based on a self-triggered approach and proactively reduces the risk of channel congestion by only assigning communication resources to vehicles that are in critical configurations, i.e., when there is a risk for a future collision. Compared with collision-agnostic communication strategies, typically considered for automated intersection management, the CARA strategy aims to bridge the gap between control, sensing, and communication. It is shown to significantly reduce the required amount of communication (albeit with a slight increase in the control cost), without compromising safety. Furthermore, control cost can be reduced by allowing more frequent communication, which we demonstrate through a trade-off analysis between control performance and communication load. Hence, CARA can operate in communication-limited scenarios, but also be modified for scenarios where the control cost is of primary interest.

5G communication systems operating above 24 GHz have promising properties for user localization and environment mapping. Existing studies have either relied on simplified abstract models of the signal propagation and the measurements, or are based on direct positioning approaches, which directly map the received waveform to a position. In this study, we consider an intermediate approach, which consists of four phases—downlink data transmission, multi-dimensional channel estimation, channel parameter clustering, and simultaneous localization and mapping (SLAM) based on a novel likelihood function. This approach can decompose the problem into simpler steps, thus leading to lower complexity. At the same time, by considering an end-to-end processing chain, we are accounting for a wide variety of practical impairments. Simulation results demonstrate the efficacy of the proposed approach.