Over-The-Air Extreme Learning Machines with XL Reception via Nonlinear Cascaded Metasurfaces

The recently envisioned goal-oriented communications paradigm calls for the application of inference on wirelessly transferred data via Machine Learning (ML) tools. An emerging research direction deals with the realization of inference ML models directly in the physical layer of Multiple-Input Multiple-Output (MIMO) systems, which, however, entails certain significant challenges. In this paper, leveraging the technology of programmable MetaSurfaces (MSs), we present an eXtremely Large (XL) MIMO system that acts as an Extreme Learning Machine (ELM) performing binary classification tasks completely Over-The-Air (OTA), which can be trained in closed form. The proposed system comprises a receiver architecture consisting of densely parallel placed diffractive layers of XL MSs followed by a single reception radio-frequency chain. The front layer facing the MIMO channel consists of identical unit cells of a fixed NonLinear (NL) response, while the remaining layers of elements of tunable linear responses are utilized to approximate OTA the trained ELM weights. Our numerical investigations showcase that, in the XL regime of MS elements, the proposed XL-MIMO-ELM system achieves performance comparable to that of digital and idealized ML models across diverse datasets and wireless scenarios, thereby demonstrating the feasibility of embedding OTA learning capabilities into future communication systems.

Generation-Agnostic Zero-Energy Devices for Sustainable Connectivity, Sensing, and Localization

The massive scale of Internet of Things (IoT) connectivity expected in 6G networks raises unprecedented challenges in energy use, battery waste, and lifecycle sustainability. Current cellular IoT solutions remain bound to the lifetime of underlying network generations and rely on billions of disposable batteries, creating unsustainable economic and environmental costs. This article proposes generation-agnostic zero-energy devices (XG-ZEDs), a new class of backscatter based IoT devices that are battery-less, spectrum-agnostic, and future-proof across successive network generations. XG-ZEDs exploit existing ambient wireless signals for communication, sensing, and localization, transforming infrastructure and user devices into universal enablers of ultra-low-power connectivity. We review architectural classifications, communication protocols, network integration, and representative applications such as sensing, localization, and radio-SLAM, while outlining the challenges ahead.

Scalable RIS-Aided Beamforming Strategies for Near-Field MU-MISO via Multi-Antenna Feeder

This paper investigates a modular beamforming framework for reconfigurable intelligent surface (RIS)-aided multi-user (MU) communications in the near-field regime, built upon a novel antenna architecture integrating an active multi-antenna feeder (AMAF) array with a transmissive RIS (T-RIS), referred to as AT-RIS. This decoupling enables coordinated yet independently configurable designs in the AMAF and T-RIS domains, supporting flexible strategies with diverse complexity-performance trade-offs. Several implementations are analyzed, including diagonal and non-diagonal T-RIS architectures, paired with precoding schemes based on focusing, minimum mean square error, and eigenmode decomposition. Simulation results demonstrate that while non-diagonal schemes maximize sum rate in scenarios with a limited number of User Equipments (UEs) and high angular separability, they exhibit fairness and scalability limitations as UE density increases. Conversely, diagonal T-RIS configurations, particularly the proposed focusing-based scheme with uniform feeder-side power allocation, offer robust, fair, and scalable performance with minimal channel state information. The findings emphasize the critical impact of UEs' angular separability and reveal inherent trade-offs among spectral efficiency, complexity, and fairness, positioning diagonal AT-RIS architectures as practical solutions for scalable near-field MU multiple-input single-output systems.

Frequency-selective Dynamic Scattering Arrays for Over-the-air EM Processing

In this paper, we investigate frequency-selective dynamic scattering array (DSA), a versatile antenna structure capable of performing joint wave-based computing and radiation by transitioning signal processing tasks from the digital domain to the electromagnetic (EM) domain. The numerical results demonstrate the potential of DSAs to produce space-frequency superdirective responses with minimal usage of radiofrequency (RF) chains, making it particularly attractive for future holographic multiple-input multiple-output (MIMO) systems.

An Overview on Over-the-air Electromagnetic Signal Processing

This article provides a tutorial on over-the-air electromagnetic signal processing (ESP) for next-generation wireless networks, addressing the limitations of digital processing to enhance the efficiency and sustainability of future 6th Generation (6G) systems. It explores the integration of electromagnetism and signal processing (SP) highlighting how their convergence can drive innovations for 6G technologies. Key topics include electromagnetic (EM) wave-based processing, the application of metamaterials and advanced antennas to optimize EM field manipulation with a reduced number of radiofrequency chains, and their applications in holographic multiple-input multiple-output systems. By showcasing enabling technologies and use cases, the article demonstrates how wave-based processing can minimize energy consumption, complexity, and latency, offering an effective framework for more sustainable and efficient wireless systems. This article aims to assist researchers and professionals in integrating advanced EM technologies with conventional SP methods.

An Overview on Over-the-airElectromagnetic Signal Processing

This article provides a tutorial on over-the-air electromagnetic signal processing (ESP) for next-generation wireless networks, addressing the limitations of digital processing to enhance the efficiency and sustainability of future 6th Generation (6G) systems. It explores the integration of electromagnetism and signal processing (SP) highlighting how their convergence can drive innovations for 6G technologies. Key topics include electromagnetic (EM) wave-based processing, the application of metamaterials and advanced antennas to optimize EM field manipulation with a reduced number of radiofrequency chains, and their applications in holographic multiple-input multiple-output systems. By showcasing enabling technologies and use cases, the article demonstrates how wave-based processing can minimize energy consumption, complexity, and latency, offering an effective framework for more sustainable and efficient wireless systems. This article aims to assist researchers and professionals in integrating advanced EM technologies with conventional SP methods.

Beam Focusing for Near-Field Multi-User Localization

Extremely large-scale antenna arrays are poised to play a pivotal role in sixth-generation (6G) networks. Utilizing such arrays often results in a near-field spherical wave transmission environment, enabling the generation of focused beams, which introduces new degrees of freedom for wireless localization. In this paper, we consider a beam-focusing design for localizing multiple sources in the radiating near-field. Our formulation accommodates various expected types of implementations of large antenna arrays, including hybrid analog/digital architectures and dynamic metasurface antennas (DMAs). We consider a direct localization estimation method exploiting curvature-of-arrival of impinging spherical wavefront to obtain user positions. In this regard, we adopt a two-stage approach configuring the array to optimize near-field positioning. In the first step, we focus only on adjusting the array coefficients to minimize the estimation error. We obtain a closed-form approximate solution based on projection and the better one based on the Riemann gradient algorithm. We then extend this approach to simultaneously localize and focus the beams via a sub-optimal iterative approach that does not rely on such knowledge. The simulation results show that near-field localization accuracy based on a hybrid array or DMA can achieve performance close to that of fully digital arrays at a lower cost, and DMAs can attain better performance than hybrid solutions with the same aperture.

Bistatic Sensing at THz Frequencies via a Two-Stage Ultra-Wideband MIMO-OFDM System

Only the chairs can edit The availability of abundant bandwidth at terahertz (THz) frequencies holds promise for significantly enhancing the sensing performance of integrated sensing and communication (ISAC) systems in the next-generation wireless systems, enabling high accuracy and resolution for precise target localization. In orthogonal frequency-division multiplexing (OFDM) systems, wide bandwidth can be achieved by increasing the subcarrier spacing rather than the number of subcarriers, thereby keeping the complexity of the sensing system low. However, this approach may lead to an ambiguity problem in target range estimation. To address this issue, this work proposes a two-stage maximum likelihood method for estimating target position in an ultra-wideband bistatic multiple-antenna OFDM-based ISAC system operating at THz frequencies. Numerical results show that the proposed estimation approach effectively resolves the ambiguity problem while achieving high resolution and accuracy target position estimation at a very low signal-to-noise ratio regime.

A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV

In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly design the transmit beamforming with the GBS association to optimize communication performance while ensuring high sensing accuracy. We propose a predictive beamforming framework based on a dual DNN solution to solve the formulated nonconvex optimization problem. A first DNN is trained to produce the required beamforming matrix for any point of the UAV flying area in a reduced time compared to state-of-the-art beamforming optimizers. A second DNN is trained to learn the optimal mapping from the input features, power, and EIRP constraints to the GBS association decision. Finally, we provide an extensive simulation analysis to corroborate the proposed approach and show the benefits of EIRP, SINR performance and computational speed.

Dynamic Scattering Arrays for Simultaneous Electromagnetic Processing and Radiation in Holographic MIMO Systems

To meet the stringent requirements of next-generation wireless networks, multiple-input multiple-output (MIMO) technology is expected to become massive and pervasive. Unfortunately, this could pose scalability issues in terms of complexity, power consumption, cost, and processing latency. Therefore, novel technologies and design approaches, such as the recently introduced holographic MIMO paradigm, must be investigated to make future networks sustainable. In this context, we propose the concept of a dynamic scattering array (DSA) as a versatile 3D structure capable of performing joint wave-based computing and radiation by moving the processing from the digital domain to the electromagnetic (EM) domain. We provide a general analytical framework for modeling DSAs, introduce specific design algorithms, and apply them to various use cases. The examples presented in the numerical results demonstrate the potential of DSAs to further reduce complexity and the number of radiofrequency (RF) chains in holographic MIMO systems while achieving enhanced EM wave processing and radiation flexibility for tasks such as beamforming and single- and multi-user MIMO.