Robust Egoistic Rigid Body Localization

We consider a robust and self-reliant (or "egoistic") variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (i.e., location and orientation) of another rigid body (or "target"), relative to its own, without the assistance of external infrastructure, without prior knowledge of the shape of the target, and taking into account the possibility that the available observations are incomplete. Three complementary contributions are then offered for such a scenario. The first is a method to estimate the translation vector between the center point of both rigid bodies, which unlike existing techniques does not require that both objects have the same shape or even the same number of landmark points. This technique is shown to significantly outperform the state-of-the-art (SotA) under complete information, but to be sensitive to data erasures, even when enhanced by matrix completion methods. The second contribution, designed to offer improved performance in the presence of incomplete information, offers a robust alternative to the latter, at the expense of a slight relative loss under complete information. Finally, the third contribution is a scheme for the estimation of the rotation matrix describing the relative orientation of the target rigid body with respect to the primary. Comparisons of the proposed schemes and SotA techniques demonstrate the advantage of the contributed methods in terms of root mean square error (RMSE) performance under fully complete information and incomplete conditions.

A Doubly-Dispersive MIMO Channel Model Parametrized with Stacked Intelligent Metasurfaces

Introduced with the advent of statistical wireless channel models for high mobility communications and having a profound role in communication-centric (CC) integrated sensing and communications (ISAC), the doubly-dispersive (DD) channel structure has long been heralded as a useful tool enabling the capture of the most important fading effects undergone by an arbitrary time-domain transmit signal propagating through some medium. However, the incorporation of this model into multiple-input multiple-output (MIMO) system setups, relying on the recent paradigm-shifting transceiver architecture based on stacked intelligent metasurfaces (SIM), in an environment with reconfigurable intelligent surfaces (RISs) remains an open problem due to the many intricate details that have to be accounted for. In this paper, we fill this gap by introducing a novel DD MIMO channel model that incorporates an arbitrary number of RISs in the ambient, as well as SIMs equipping both the transmitter and receiver. We then discuss how the proposed metasurfaces-parametrized DD (MPDD) channel model can be seamlessly applied to waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM), with each having their own inherent advantages and disadvantages. An illustrative application of the programmable functionality of the proposed model is finally presented to showcase its potential for boosting the performance of the aforementioned waveforms. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.

From Theory to Reality: A Design Framework for Integrated Communication and Computing Receivers

We propose a novel flexible and scalable framework to design integrated communication and computing (ICC) -- a.k.a. over-the-air computing (AirComp) -- receivers. To elaborate, while related literature so far has generally focused either on theoretical aspects of ICC or on the design of beamforming (BF) algorithms for AirComp, we propose a framework to design receivers capable of simultaneously detecting communication symbols and extracting the output of the AirComp operation, in a manner that can: a) be systematically generalized to any nomographic function, b) scaled to a massive number of user equipments (UEs) and edge devices (EDs), and c) support the multiple computation streams. For the sake of illustration, we demonstrate the proposed method under a setting consisting of the uplink from multiple single-antenna UEs/EDs simultaneously transmitting communication and computing signals to a single multiple-antenna base station (BS)/access point (AP). The receiver, which seeks to detect all communication symbols and minimize the distortion over the computing signals, requires that only a fraction of the transmit power be allocated to the latter, therefore coming close to the ideal (but unattainable) condition that computing is achieved "for free", without taking resources from the communication system. The design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 200 UEs/EDs. They also demonstrate the efficacy of the proposed method under all various loading conditions, with the performance of the scheme approaching fundamental limiting bounds in the under/fully loaded cases.

Low Complexity Joint Channel Estimation and Data Detection for AFDM Receivers With Oversampling

In this paper, we propose a novel low complexity time domain (TD) oversampling receiver framework under affine frequency division multiplexing (AFDM) waveforms for joint channel estimation and data detection (JCEDD). Leveraging a generalized doubly-dispersive channel model, we first derive the input-output (I/O) relationship for arbitrary waveforms when oversampled in the TD and present the I/O relationship for AFDM as an example. Subsequently, utilizing the multiple sample streams created via the oversampling procedure, we use the parametric bilinear Gaussian belief propagation (PBiGaBP) technique to conduct JCEDD for decoding the transmitted data and estimating the complex channel coefficients. Simulation results verify significant performance improvements both in terms of data decoding and complex channel coefficient estimation with improved robustness against a varying number of pilots over a conventional Nyquist sampling rate receiver.

Tone Reservation-Based PAPR Reduction Using Manifold Optimization for OFDM-ISAC Systems

We consider the peak-to-average power ratio (PAPR) reduction challenge of orthogonal frequency division multiplexing (OFDM) systems utilizing tone reservation (TR) under a sensing-enabling constraint, such that the signals placed in the reserved tones (RTs) can be exploited for Integrated Sensing and Communication (ISAC). To that end, the problem is first cast as an unconstrained manifold optimization problem, and then solved via an iterative projected gradient descent algorithm assisted by an approximation of the infinity norm. Simulation results show that the proposed method, while maintaining a level of PAPR reduction similar to state of the art (SotA), not only has lower computational complexity but also outperforms the alternatives in terms of sensing performance.

Grover Adaptive Search for Maximum Likelihood Detection of Generalized Spatial Modulation

We propose a quantum-assisted solution for the maximum likelihood detection (MLD) of generalized spatial modulation (GSM) signals. Specifically, the MLD of GSM is first formulated as a novel polynomial optimization problem, followed by the application of a quantum algorithm, namely, the Grover adaptive search. The performance in terms of query complexity of the proposed method is evaluated and compared to the classical alternative via a numerical analysis, which reveals that under fault-tolerant quantum computation, the proposed method outperforms the classical solution if the number of data symbols and the constellation size are relatively large.

Enabling Next-Generation V2X Perception: Wireless Rigid Body Localization and Tracking

Vehicle-to-everything (V2X) perception describes a suite of technologies used to enable vehicles to perceive their surroundings and communicate with various entities, such as other road users, infrastructure, or the network/cloud. With the development of autonomous driving, V2X perception is becoming increasingly relevant, as can be seen by the tremendous attention recently given to integrated sensing and communication (ISAC) technologies. In this context, rigid body localization (RBL) also emerges as one important technology which enables the estimation of not only target's positions, but also their shape and orientation. This article discusses the need for RBL, its benefits and opportunities, challenges and research directions, as well as its role in the standardization of the sixth-generation (6G) and beyond fifth generation (B5G) applications.

Belief Propagation-based Rotation and Translation Estimation for Rigid Body Localization

We propose a novel solution to the rigid body localization (RBL) problem, in which the three-dimensional (3D) rotation and translation is estimated by only utilizing the range measurements between the wireless sensors on the rigid body and the anchor sensors. The proposed framework first constructs a linear Gaussian belief propagation (GaBP) algorithm to estimate the absolute sensor positions utilizing the range-based received signal model, which is used for the reconstruction of the RBL transformation model, linearized with a small-angle approximation. In light of the reformulated system, a second bivariate GaBP is designed to directly estimate the 3D rotation angles and translation distances, with an interference cancellation (IC) refinement to improve the angle estimation performance. The effectiveness of the proposed method is verified via numerical simulations, highlighting the superior performance of the proposed method against the state-of-the-art (SotA) techniques for the position, rotation, and translation estimation performance.

Massive Index Modulation with Combinatorics-Free Detection for Integrated Sensing and Communications

Integrated sensing and communications (ISAC) and index modulation (IM) are promising technologies for beyond fifth generation (B5G) and sixth generation (6G) systems. While ISAC enables new applications, IM is attractive for its inherent energy and spectral efficiencies. In this article we propose massive IM as an enabler of ISAC, by considering transmit signals with information conveyed through the indexation of the resources utilized in their transmission, and pilot symbols exploited for sensing. In order to overcome the complexity hurdle arising from the large sizes of IM codebooks, we propose a novel message passing (MP) decoder designed under the Gaussian belief propagation (GaBP) framework exploiting a novel unit vector decomposition (UVD) of IM signals with purpose-derived novel probability distributions. The proposed method enjoys a low decoding complexity that is independent of combinatorial factors, while still approaching the performance of unfeasible state-of-the-art (SotA) search-based methods. The effectiveness of the proposed approach is demonstrated via complexity analysis and numerical results for piloted generalized quadrature spatial modulation (GQSM) systems of large sizes (up to 96 antennas).

Blind Bistatic Radar Parameter Estimation in Doubly-Dispersive Channels

We propose a novel method for blind bistatic radar parameter estimation (RPE), which enables integrated sensing and communications (ISAC) by allowing passive (receive) base stations (BSs) to extract radar parameters (ranges and velocities of targets), without requiring knowledge of the information sent by an active (transmit) BS to its users. The contributed method is formulated with basis on the covariance of received signals, and under a generalized doubly-dispersive channel model compatible with most of the waveforms typically considered for ISAC, such as orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) and affine frequency division multiplexing (AFDM). The original non-convex problem, which includes an $\ell_0$-norm regularization term in order to mitigate clutter, is solved not by relaxation to an $\ell_1$-norm, but by introducing an arbitrarily-tight approximation then relaxed via fractional programming (FP). Simulation results show that the performance of the proposed method approaches that of an ideal system with perfect knowledge of the transmit signal covariance with an increasing number of transmit frames.