Integrated Communication and Computing in Time-Varying mmWave Channels

We propose a novel framework for integrated communication and computing (ICC) transceiver design in time-varying millimeter-wave (mmWave) channels. In particular, in order to cope with the dynamics of time-varying mmWave channels, the detection of communication symbols and the execution of an over-the-air computing (AirComp) operation are performed in parallel with channel tracking, as opposed to existing state-of-the-art (SotA) on ICC where perfect knowledge of the channel at all time instances is typically assumed. For clarity of exposition, we consider a single-input multiple-output (SIMO) uplink scenario where multiple single-antenna user equipment (UE) transmit to a base station (BS) equipped with multiple antennas, such that each UE, or edge device (ED), precodes its own transmit signal, while the BS, or access points (APs), also performs receive beamforming. The proposed transceiver framework then estimates channel state information (CSI) and data symbols in parallel, using a bilinear Gaussian belief propagation (BiGaBP) algorithm for joint channel and data detection (JCDE), aided by a channel prediction (CP) algorithm executed before each estimation window at the BS. The AirComp operation is then executed by means of an optimal combination of the residual signal. Simulation results demonstrate the effectiveness of the proposed scheme in performing ICC in challenging time-varying mmWave channels, with minimal degradation to both communication and computing performance.

Fundamental Limits of Rigid Body Localization

We consider a novel approach to formulate the Cram\'er-Rao Lower Bound (CRLB) for the rigid body localization (RBL) problem, which allows us to assess the fundamental accuracy limits on the estimation of the translation and rotation of a rigid body with respect to a known reference. To that end, we adopt an information-centric construction of the Fisher information matrix (FIM), which allows to capture the contribution of each measurement towards the FIM, both in terms of input measurement types, as well as of their error distributions. Taking advantage of this approach, we derive a generic framework for the CRLB formulation, which is applicable to any type of rigid body localization scenario, extending the conventional FIM formulation suitable for point targets to the case of a rigid body whose location include both translation vector and the rotation matrix (or alternative the rotation angles), with respect to a reference. Closed-form expressions for all CRLBs are given, including the bound incorporating an orthonormality constraint onto the rotation matrix. Numerical results illustrate that the derived expression correctly lower-bounds the errors of estimated localization parameters obtained via various related state-of-the-art (SotA) estimators, revealing their accuracies and suggesting that SotA RBL algorithms can still be improved.

Chirp-Permuted AFDM: A New Degree of Freedom for Next-Generation Versatile Waveform Design

We present a novel multicarrier waveform, termed chirp-permuted affine frequency division multiplexing (CP-AFDM), which introduces a unique chirp-permutation domain on top of the chirp subcarriers of the conventional AFDM. Rigorous analysis of the signal model and waveform properties, supported by numerical simulations, demonstrates that the proposed CP-AFDM preserves all core characteristics of affine frequency division multiplexing (AFDM) - including robustness to doubly-dispersive channels, peak-to-average power ratio (PAPR), and full delay-Doppler representation - while further enhancing ambiguity function resolution and peak-to-sidelobe ratio (PSLR) in the Doppler domain. These improvements establish CP-AFDM as a highly attractive candidate for emerging sixth generation (6G) use cases demanding both reliability and sensing-awareness. Moreover, by exploiting the vast degree of freedom in the chirp-permutation domain, two exemplary multifunctional applications are introduced: an index modulation (IM) technique over the permutation domain which achieves significant spectral efficiency gains, and a physical-layer security scheme that ensures practically perfect security through permutation-based keying, without requiring additional transmit energy or signaling overhead.

Quaternion-Domain Super MDS for Robust 3D Localization

This paper proposes a novel low-complexity three-dimensional (3D) localization algorithm for wireless sensor networks, termed quanternion-domain super multi-dimensional scaling (QD-SMDS). The algorithm is based on a reformulation of the SMDS, originally developed in the real domain, using quaternion algebra. By representing 3D coordinates as quaternions, the method constructs a rank-1 Gram edge kernel (GEK) matrix that integrates both relative distance and angular information between nodes, which enhances the noise reduction effect achieved through low-rank truncation employing singular value decomposition (SVD), thereby improving robustness against information loss. To further reduce computational complexity, we also propose a variant of QD-SMDS that eliminates the need for the computationally expensive SVD by leveraging the inherent structure of the quaternion-domain GEK matrix. This alternative directly estimates node coordinates using only matrix multiplications within the quaternion domain. Simulation results demonstrate that the proposed method significantly improves localization accuracy compared to the original SMDS algorithm, especially in scenarios with substantial measurement errors. The proposed method also achieves comparable localization accuracy without requiring SVD.

Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications

Quantum computing is poised to redefine the algorithmic foundations of communication systems. While quantum superposition and entanglement enable quadratic or exponential speedups for specific problems, identifying use cases where these advantages yield engineering benefits is, however, still nontrivial. This article presents the fundamentals of quantum computing in a style familiar to the communications society, outlining the current limits of fault-tolerant quantum computing and uncovering a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers. Based on a systematic review of pioneering and state-of-the-art studies, we distill common design trends for the research and development of quantum-accelerated communication systems and highlight lessons learned. The key insight is that classical heuristics can sharpen certain quantum parameters, underscoring the complementary strengths of classical and quantum computing. This article aims to catalyze interdisciplinary research at the frontier of quantum information processing and future communication systems.

Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization

The doubly-dispersive (DD) channel structure has played a pivotal role in wireless communications, particularly in high-mobility scenarios and integrated sensing and communications (ISAC), due to its ability to capture the key fading effects experienced by a transmitted signal as it propagates through a dynamic medium. However, extending the DD framework to multiple-input multiple-output (MIMO) systems, especially in environments artificially enhanced by reconfigurable intelligent surfaces (RISs) and stacked intelligent metasurfaces (SIM), remains a challenging open problem. In this chapter, a novel metasurfaces-parametrized DD (MPDD) channel model that integrates an arbitrary number of RISs, while also incorporating SIM at both the transmitter and receiver is introduced. Next, the application of this model to some key waveforms optimized for DD environments -- namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM) -- is discussed. Finally, the programmability of the proposed model is highlighted through an illustrative application, demonstrating its potential for enhancing waveform performance in SIM-assisted wireless systems.

A Flexible Design Framework for Integrated Communication and Computing Receivers

We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to any nomographic function, b) scales to a massive number of user equipments (UEs) and edge devices (EDs), c) supports the computation of multiple independent functions (streams), and d) operates in a multi-access fashion whereby each transmitter can choose to transmit either data symbols, computing signals or both. For the sake of illustration, we design the proposed multi-stream and multi-access method under an uplink setting, where multiple single-antenna UEs/EDs simultaneously transmit data and computing signals to a single multiple-antenna base station (BS)/access point (AP). Under the communication functionality, the receiver aims to detect all independent communication symbols while treating the computing streams as aggregate interference which it seeks to mitigate; and conversely, under the computing functionality, to minimize the distortion over the computing streams while minimizing their mutual interference as well as the interference due to data symbols. To that end, the design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations coupled with closed-form combiners purpose-built for the AirComp operation, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 300 UEs/EDs. The efficacy of the proposed method under different loading conditions is also evaluated, with the performance of the scheme shown to approach fundamental limiting bounds in the under/fully loaded cases.

Affine Filter Bank Modulation: A New Waveform for High Mobility Communications

We propose a new waveform suitable for integrated sensing and communications (ISAC) systems facing doubly-dispersive (DD) channel conditions, as typically encountered in high mobility scenarios. Dubbed Affine Filter Bank Modulation (AFBM), this novel waveform is designed based on a filter-bank structure, known for its ability to suppress out-of-band emissions (OOBE), while integrating a discrete affine Fourier transform (DAFT) precoding stage which yields low peak-to-average power ratio (PAPR) and robustness to DD distortion, as well as other features desirable for ISAC. Analytical and simulation results demonstrate that AFBM maintains quasi-orthogonality similar to that of affine frequency division multiplexing (AFDM) in DD channels, while achieving PAPR levels 3 dB lower, in addition to OOBE as low as -100 dB when implemented with PHYDYAS prototype filters.

Parametrized Stacked Intelligent Metasurfaces for Bistatic Integrated Sensing and Communications

We consider stacked intelligent metasurfaces (SIMs) as a tool to improve the performance of bistatic integrated sensing and communications (ISAC) schemes. To that end, we optimize the SIMs and design a radar parameter estimation (RPE) scheme aimed at enhancing radar sensing capabilities as well as communication performance under ISAC-enabling waveforms known to perform well in doubly-dispersive (DD) channels. The SIM optimization is done via a min-max problem formulation solved via steepest ascent with closed-form gradients, while the RPE is carried out via a compressed sensing-based probabilistic data association (PDA) algorithm. 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.

Quaternion Domain Super MDS for 3D Localization

We propose a novel low-complexity three-dimensional (3D) localization algorithm for wireless sensor networks, termed quaternion-domain super multidimensional scaling (QD-SMDS). This algorithm reformulates the conventional SMDS, which was originally developed in the real domain, into the quaternion domain. By representing 3D coordinates as quaternions, the method enables the construction of a rank-1 Gram edge kernel (GEK) matrix that integrates both relative distance and angular (phase) information between nodes, maximizing the noise reduction effect achieved through low-rank truncation via singular value decomposition (SVD). The simulation results indicate that the proposed method demonstrates a notable enhancement in localization accuracy relative to the conventional SMDS algorithm, particularly in scenarios characterized by substantial measurement errors.