AREE-Based Decoupled Design of Hybrid Beamformers in mmWave XL-MIMO Systems

Hybrid beamforming has been widely employed in mmWave communications such as vehicular-to-everything (V2X) scenarios, as a compromise between hardware complexity and spectral efficiency. However, the inherent coupling between analog and digital precoders in hybrid array architecture significantly limits the computational and spectral efficiency of existing algorithms. To address this issue, we propose an alternating residual error elimination (AREE) algorithm, which decomposes the hybrid beamforming problem into two low-dimensional subproblems, each exhibiting a favorable matrix structure that enables effective decoupling of analog and digital precoders from the matrix product formulation. These subproblems iteratively eliminate each other's residual errors, driving the original problem toward the optimal hybrid beamforming performance. The proposed initialization ensures rapid convergence, while a low-complexity geometric channel SVD algorithm is developed by transforming the high-dimensional sparse channel into a low-dimensional equivalent, thereby simplifying the derivation of subproblems. Simulation results demonstrate that the AREE algorithm effectively decouples analog and digital precoders with low complexity, achieves fast convergence, and offers higher spectral efficiency than existing beamforming methods.

Performance Bounds for Velocity Estimation with Large Antenna Arrays

Joint communication and sensing (JCS) is envisioned as an enabler of future 6G networks. One of the key features of these networks will be the use of extremely large aperture arrays (ELAAs) and high operating frequencies, which will result in significant near-field propagation effects. This unique property can be harnessed to improve sensing capabilities. In this paper, we focus on velocity sensing, as using ELAAs allows the estimation of not just the radial component but also the transverse component. We derive analytical performance bounds for both velocity components, demonstrating how they are affected by the different system parameters and geometries. These insights offer a foundational understanding of how near-field effects play in velocity sensing differently from the far field and from position estimate.

Localization Based on MIMO Backscattering from Retro-Directive Antenna Arrays

In next-generation vehicular environments, precise localization is crucial for facilitating advanced applications such as autonomous driving. As automation levels escalate, the demand rises for enhanced accuracy, reliability, energy efficiency, update rate, and reduced latency in position information delivery. In this paper, we propose the exploitation of backscattering from retro-directive antenna arrays (RAAs) to address these imperatives. We introduce and discuss two RAA-based architectures designed for various applications, including network localization and navigation. These architectures enable swift and simple angle-of-arrival estimation by using signals backscattered from RAAs. They also leverage multiple antennas to capitalize on multiple-input-multiple-output (MIMO) gains, thereby addressing the challenges posed by the inherent path loss in backscatter communication, especially when operating at high frequencies. Consequently, angle-based localization becomes achievable with remarkably low latency, ideal for mobile and vehicular applications. This paper introduces ad-hoc signalling and processing schemes for this purpose, and their performance is analytically investigated. Numerical results underscore the potential of these schemes, offering precise and ultra-low-latency localization with low complexity and ultra-low energy consumption devices.

Holographic MIMO Communications exploiting the Orbital Angular Momentum

This study delves into the potential of harnessing the orbital angular momentum (OAM) property of electromagnetic waves in near-field and line-of-sight scenarios by utilizing large intelligent surfaces, in the context of holographic multiple-input multiple-output (MIMO) communications. The paper starts by characterizing OAM-based communications and investigating the connection between OAM-carrying waves and optimum communication modes recently analyzed for communicating with smart surfaces. Subsequently, it proposes implementable strategies for generating and detecting OAM-based communication signals using intelligent surfaces and optimization methods that leverage focusing techniques. Then, the performance of these strategies is quantitatively evaluated through simulations. The numerical results show that OAM waves while constituting a viable and more practical alternative to optimum communication modes are sub-optimal in terms of achievable capacity.

Grant-free Random Access with Self-conjugating Metasurfaces

Recently, grant-free random access schemes have received significant attention in the scientific community as a solution for extremely low-latency massive communications in new industrial Internet-of-things (IIoT) and digital twins applications. Unfortunately, the adoption of such schemes in the mmWave and THz bands is challenging because massive antenna arrays are needed to counteract the high path loss and provide massive access with consequent significant signaling overhead for channel estimation and slow beam alignment procedures between the base station (BS) and user equipments (UEs), which are in contrast to the ultra-low-latency requirement, as well as to the need for low hardware complexity and energy consumption. In this paper, we propose the adoption of a self-conjugating metasurface (SCM) at the UE side, where the signal sent by the BS is reflected after being conjugated and phase-modulated according to the UE data. Then, a novel grant-free random access protocol is presented capable to detect new UEs and establish parallel multiple-input multiple-output (MIMO) uplink communications with almost zero latency and jitter. This is done in a blind way without the need for RF/ADC chains at the UE side as well as without explicit channel estimation and time-consuming beam alignment schemes.

Communication Modes with Large Intelligent Surfaces in the Near Field

This paper proposes a practical method for the definition of multiple communication modes when antennas operate in the near-field region, by realizing ad-hoc beams exploiting the focusing capability of large antennas. The beamspace modeling proposed to define the communication modes is then exploited to derive expressions for the number of communication modes (i.e., degrees of freedom) in a generic setup, beyond the traditional paraxial approximation, together with closed-form definitions for the basis set at the transmitting and receiving antennas for several cases of interest, such as for the communication between a large antenna and a small antenna. Numerical results indicate that quasi-optimal communication can be obtained starting from focusing functions. This translates into the possibility of a significant enhancement of the channel capacity even in line-of-sight channel condition without the need of resorting to optimal but complex phase/amplitude antenna profiles as well as intensive numerical simulations. Traditional results valid under paraxial approximation are revised in light of the proposed modeling, showing that similar conclusions can be obtained from different perspectives.