Energy Efficiency Maximization of MIMO Systems through Reconfigurable Holographic Beamforming

This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmit antenna array, and one in the immediate vicinity of the receive antenna array. The resulting architecture implements a holographic beamforming structure at both the transmitter and receiver. In this scenario, the system energy efficiency is optimized with respect to the transmit covariance matrix, and the reflection matrices of the two metasurfaces. A low-complexity algorithm is developed, which is guaranteed to converge to a first-order optimal point of the energy efficiency maximization problem. Moreover, closed-form expressions are derived for the metasurface matrices in the special case of single-antenna or single-stream transmission. The two metasurfaces are considered to be nearly-passive and subject to global reflection constraints. A numerical performance analysis is conducted to assess the performance of the proposed optimization methods, showing, in particular, that the use of holographic beamforming by metasurfaces can provide significant energy efficiency gains compared to fully digital beamforming architectures, even when the latter achieve substantial multiplexing gains.

Conformal Reconfigurable Intelligent Surfaces: A Cylindrical Geometry Perspective

Curved reconfigurable intelligent surfaces (RISs) represent a promising frontier for next-generation wireless communication, enabling adaptive wavefront control on nonplanar platforms such as unmanned aerial vehicles and urban infrastructure. This work presents a systematic investigation of cylindrical RISs, progressing from idealized surface-impedance synthesis to practical implementations based on simple one-bit meta-atoms. Exact analytical and geometrical-optics-based models are first developed to explore fundamental design limits, followed by a semi-analytical formulation tailored to discrete, reconfigurable architectures. This model enables efficient beam synthesis using both evolutionary optimization and low-complexity strategies, including the minimum power distortionless response method, and is validated through full-wave simulations. Results confirm that one-bit RISs can achieve directive scattering with manageable sidelobe levels and minimal hardware complexity. These findings establish the viability of cylindrical RISs and open the door to their integration into dual-use wireless platforms for real-world communication scenarios.

A Comprehensive Survey of Channel Estimation Techniques for OTFS in 6G and Beyond Wireless Networks

Orthogonal time-frequency space (OTFS) modulation has emerged as a powerful wireless communication technology that is specifically designed to address the challenges of high-mobility scenarios and significant Doppler effects. Unlike conventional modulation schemes that operate in the time-frequency (TF) domain, OTFS projects signals to the delay-Doppler (DD) domain, where wireless channels exhibit sparse and quasi-static characteristics. This fundamental transformation enables superior channel estimation (CE) performance in challenging propagation environments characterized by high-mobility, severe multipath effects, and rapidly time-varying channel conditions. This article provides a systematic examination of CE techniques for OTFS systems, covering the extensive research landscape from foundational methods to cutting-edge approaches. We present a detailed analysis of DD and TF domain CE techniques presented in the literature, including separate pilot, embedded pilot, and superimposed pilot approaches. The article encompasses various algorithmic frameworks including Bayesian learning, matching pursuit-based techniques, message passing algorithms, deep learning (DL)-based methods, and recent CE approaches. Additionally, we explore joint CE and signal detection (SD) strategies, the integration of OTFS with next-generation wireless systems including massive multiple-input multiple-output (MIMO), millimeter wave (mmWave) communications, reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC) systems. Critical implementation challenges are presented, including leakage suppression, inter-Doppler interference mitigation, impulsive noise handling, signaling overhead reduction, guard space requirements, peak-to-average power ratio (PAPR) management, beam squint effects, and hardware impairments.

LoS+NLoS Holographic MIMO: Analysis and Application of Wavenumber-Division Multiplexing

Holographic multiple-input multiple-output (MIMO) enables electrically large continuous apertures, overcoming the physical scaling limits of conventional MIMO architectures with half-wavelength spacing. Their near-field operating regime requires channel models that jointly capture line-of-sight (LoS) and non-line-of-sight (NLoS) components in a physically consistent manner. Existing studies typically treat these components separately or rely on environment-specific multipath models. In this work, we develop a unified LoS+NLoS channel representation for holographic lines that integrates spatial-sampling-based and expansion-based formulations. Building on this model, we extend the wavenumber-division multiplexing (WDM) framework, originally introduced for purely LoS channels, to the LoS+NLoS scenario. Applying WDM to the NLoS component yields its angular-domain representation, enabling direct characterization through the power spectral factor and power spectral density. We further derive closed-form characterizations for isotropic and non-isotropic scattering, with the former recovering Jakes' isotropic model. Lastly, we evaluate the resulting degrees of freedom and ergodic capacity, showing that incorporating the NLoS component substantially improves the performance relative to the purely LoS case.

Optimal transmit field distribution for partially obstructed continuous radiating surfaces in near-field communication systems

This paper deals with the optimal synthesis of aperture fields for (radiating) near-field communications in obstructed environments. A physically consistent model based on knife-edge diffraction is used to formulate the problem as a maximization in Hilbert space. The optimal solution is obtained as a matched filter that ``matches" the shape of a diffraction-induced kernel, thus linking wave propagation with signal processing methods. The framework supports hardware implementation using continuous apertures such as metasurfaces or lens antennas. This approach bridges physically grounded modeling, signal processing, and hardware design for efficient energy focusing in near-field obstructed channels.

The Role of ISAC in 6G Networks: Enabling Next-Generation Wireless Systems

The commencement of the sixth-generation (6G) wireless networks represents a fundamental shift in the integration of communication and sensing technologies to support next-generation applications. Integrated sensing and communication (ISAC) is a key concept in this evolution, enabling end-to-end support for both communication and sensing within a unified framework. It enhances spectrum efficiency, reduces latency, and supports diverse use cases, including smart cities, autonomous systems, and perceptive environments. This tutorial provides a comprehensive overview of ISAC's role in 6G networks, beginning with its evolution since 5G and the technical drivers behind its adoption. Core principles and system variations of ISAC are introduced, followed by an in-depth discussion of the enabling technologies that facilitate its practical deployment. The paper further analyzes current research directions to highlight key challenges, open issues, and emerging trends. Design insights and recommendations are also presented to support future development and implementation. This work ultimately try to address three central questions: Why is ISAC essential for 6G? What innovations does it bring? How will it shape the future of wireless communication?

SIM-Enabled Hybrid Digital-Wave Beamforming for Fronthaul-Constrained Cell-Free Massive MIMO Systems

As the dense deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems presents significant challenges, per-AP coverage can be expanded using large-scale antenna arrays (LAAs). However, this approach incurs high implementation costs and substantial fronthaul demands due to the need for dedicated RF chains for all antennas. To address these challenges, we propose a hybrid beamforming framework that integrates wave-domain beamforming via stacked intelligent metasurfaces (SIM) with conventional digital processing. By dynamically manipulating electromagnetic waves, SIM-equipped APs enhance beamforming gains while significantly reducing RF chain requirements. We formulate a joint optimization problem for digital and wave-domain beamforming along with fronthaul compression to maximize the weighted sum-rate for both uplink and downlink transmission under finite-capacity fronthaul constraints. Given the high dimensionality and non-convexity of the problem, we develop alternating optimization-based algorithms that iteratively optimize digital and wave-domain variables. Numerical results demonstrate that the proposed hybrid schemes outperform conventional hybrid schemes, that rely on randomly set wave-domain beamformers or restrict digital beamforming to simple power control. Moreover, the proposed scheme employing sufficiently deep SIMs achieves near fully-digital performance with fewer RF chains in most simulated cases, except in the downlink at low signal-to-noise ratios.

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.

Dynamical ON-OFF Control with Trajectory Prediction for Multi-RIS Wireless Networks

Reconfigurable intelligent surfaces (RISs) have demonstrated an unparalleled ability to reconfigure wireless environments by dynamically controlling the phase, amplitude, and polarization of impinging waves. However, as nearly passive reflective metasurfaces, RISs may not distinguish between desired and interference signals, which can lead to severe spectrum pollution and even affect performance negatively. In particular, in large-scale networks, the signal-to-interference-plus-noise ratio (SINR) at the receiving node can be degraded due to excessive interference reflected from the RIS. To overcome this fundamental limitation, we propose in this paper a trajectory prediction-based dynamical control algorithm (TPC) for anticipating RIS ON-OFF states sequence, integrating a long-short-term-memory (LSTM) scheme to predict user trajectories. In particular, through a codebook-based algorithm, the RIS controller adaptively coordinates the configuration of the RIS elements to maximize the received SINR. Our simulation results demonstrate the superiority of the proposed TPC method over various system settings.

Channel-Aware Holographic Decision Fusion

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes the need for multiple RF chains while leveraging the benefits of a large array. The optimal fusion rule for a fixed metasurface configuration is derived, alongside two suboptimal joint fusion rule and metasurface design strategies. These suboptimal approaches strike a balance between reduced complexity and lower system knowledge requirements, making them practical alternatives. The design objective focuses on effectively conveying the information regarding the phenomenon of interest to the FC while promoting energy-efficient data analytics aligned with the Internet of Things (IoT) paradigm. Simulation results underscore the viability of holographic DF, demonstrating its advantages even with suboptimal designs and highlighting the significant energy-efficiency gains achieved by the proposed system.