In this paper, we investigate the challenge of beam focusing in near-field communications with reconfigurable intelligent surfaces (RIS), which becomes critical in wideband systems. We propose a low-complexity beam focusing strategy that leverages frequency-independent RIS phase adjustments to achieve a nearly flat channel response across a wide bandwidth. Unlike conventional methods that rely on power-hungry auxiliary components or iterative optimization, our approach exploits the passive nature of RIS to reduce hardware complexity while maintaining beam precision. To support multi-user scenarios, we extend single-user near-field beamforming by introducing a hybrid partitionsuperposition strategy, which enables user-specific phase profile modeling based on spatial configuration. The proposed design facilitates efficient bandwidth allocation and mitigates beam misfocus across all subcarriers. Simulation results demonstrate substantial spectral efficiency gains over baseline methods, confirming the practical effectiveness of our approach for RIS-assisted wideband near-field multi-user systems.

In this paper, we propose Massive Aerial Processing for X (MAP-X), an innovative framework for reconstructing spatially correlated ground information, where X represents arbitrary types of geographically referenced sensing data. MAP-X leverages distributed sensors and a high altitude platform (HAP) equipped with a planar antenna array that captures radio frequency signals transmitted simultaneously from ground sensors. MAP-X incorporates two key techniques that provide significant advantages over conventional terrestrial wireless sensor network (WSN)-based methods. First, MAP-X enables all sensors to transmit concurrently within a single subframe, resulting in the random superposition of signals at the receiver. This non-orthogonal transmission approach enhances estimation accuracy as the number of devices increases, addressing the limitations of restricted orthogonal channel availability. Second, MAP-X introduces a novel waveform design that enhances angular resolution at the receiver. Combined with linear and machine learning (ML)-based post-processing techniques implemented at the HAP, this approach significantly improves the accuracy of field-data reconstruction. This paper details the signal model, waveform design, and efficient post-processing techniques underlying MAP-X. Simulation results demonstrate that our framework surpasses the upper bound performance of orthogonal data collection combined with optimal covariance-based signal reconstruction, achieving superior latency reduction and estimation accuracy.

Dynamic metasurface antennas (DMA) provide low-power beamforming through reconfigurable radiative slots. Each slot has a tunable component that consumes low power compared to typical analog components like phase shifters. This makes DMAs a potential candidate to minimize the power consumption of multiple-input multiple-output (MIMO) antenna arrays. In this paper, we investigate the use of DMAs in a wideband communication setting with practical DMA design characteristics. We develop approximations for the DMA beamforming gain that account for the effects of waveguide attenuation, element frequency-selectivity, and limited reconfigurability of the tunable components as a function of the signal bandwidth. The approximations allow for key insights into the wideband performance of DMAs in terms of different design variables. We develop a simple successive beamforming algorithm to improve the wideband performance of DMAs by sequentially configuring each DMA element. Simulation results for a line-of-sight (LOS) wideband system show the accuracy of the approximations with the simulated DMA model in terms of spectral efficiency. We also find that the proposed successive beamforming algorithm increases the overall spectral efficiency of the DMA-based wideband system compared with a baseline DMA beamforming method.

Dynamic metasurface antennas (DMAs) are leaky-wave antennas with frequency-reconfigurable slots. While resonant frequency of individual slots on the DMA can be independently configured in a wide frequency range, the DMA's beamforming performance across different operating frequencies in the available range has not been studied yet. We show that the dominant beam direction and the operating frequency of a DMA are interdependent. Using this observation, we derive a closed-form expression for line-of-sight (LOS) beamforming gain maximization to jointly configure the operating frequency and per-slot resonant frequency configuration of the DMA. A benefit of this approach in the LOS channel case is that all slots are tuned to the same resonant frequency, eliminating the need for a computationally expensive search across all feasible resonant frequency configurations. We also apply this approach to mitigate beam training overhead in large arrays by proposing a single-shot beam training algorithm that estimates the optimal resonant frequency configuration through simultaneous probing of multiple angular directions across frequencies. Simulation results show that our proposed approach offers a comparable achievable rate to true-time-delay (TTD) based systems, but with lower power consumption and hardware cost.

Antenna behaviors such as mutual coupling, near-field propagation, and polarization cannot be neglected in signal and channel models for wireless communication. We present an electromagnetic-based array manifold that accounts for several complicated behaviors and can model arbitrary antenna configurations. We quantize antennas into a large number of Hertzian dipoles to develop a model for the radiated array field. The resulting abstraction provides a means to predict the electric field for general non-homogeneous array geometries through a linear model that depends on the point source location, the position of each Hertzian dipole, and a set of coefficients obtained from electromagnetic simulation. We then leverage this model to formulate a beamforming gain optimization that can be adapted to account for polarization of the receive field as well as constraints on the radiated power density. Numerical results demonstrate that the proposed method achieves accuracy that is close to that of electromagnetic simulations. By leveraging the developed array manifold for beamforming, systems can achieve higher beamforming gains compared to beamforming with less accurate models.

Multi-mode fiber (MMF) significantly increases data rates by spatially multiplexing over parallel optical channels. Analog radio-over-fiber (A-RoF) achieves lower latency and greater bandwidth as compared to digital systems but is limited by nonlinear interference in single-mode fiber (SMF). MMF reduces this interference by transmitting signals across multiple spatial modes. In this work we present a MMF aided A-RoF model for uplink MIMO systems. The linear model considers fiber impairments and noise propagation over the optical and wireless link, assuming an optimum receiver at the baseband unit. We compare the spectral efficiency of MMFaided A-RoF with wavelength division multiplexing (WDM) aided A-RoF, showing MMF reduces nonlinear interference and supports higher data rates, though it is limited by a number of optical modes and wireless channels.

Dynamic metasurface antennas (DMAs) enable beamforming through low-power components that reconfigure each radiating element. Previous studies on a single user multiple-input-single-output (MISO) system with transmit DMA focused on maximizing beamforming gain in narrowband scenarios. In this paper, we analyze the beamforming performance of DMAs in wideband scenarios. By leveraging DMA's frequency reconfigurability, our proposed approach dynamically adjusts both the transmission frequency and element configuration, achieving a higher beamforming gain than the narrowband method. We also propose the optimal waveguide refractive index and DMA element spacing to obtain an equal beamforming gain as the power hungry true-time-delay (TTD) architecture in the desired angular range. We evaluate the achievable rate performance and show that the rate approaches that of TTD when the DMA tuning range increases.

Measurement design is an important sub-problem arising in the applications of sensing and wireless communications. Sensing systems are often capable of performing different types of mutually exclusive measurement actions. In such systems, it is important to select measurement actions so as to efficiently gather samples which are most informative about the phenomena of interest. Bayesian sequential experiment design (BSED) offers a model-based framework with which to address sequential variations of such measurement design problems. Prior applications of BSED to sensing problems often consider measurement selection policies which maximize notions of expected information gain (EIG). In certain related settings, EIG based approaches have been shown to be less performant than policies designed to minimize notions of regret with respect to the information gain afforded by an ideal policy. Motivate by this, we develop a general framework based on partially observable Markov decision processes which allows for the design of BSED policies with respect to a notion of regret. We argue for the consideration of policies based on a myopic version of posterior sampling, termed MPS, and consider the application of this framework to the problem of passive non-coherent signal source localization and detection using codebook-based receive beamforming. We further develop a general approach for approximating posterior inference based on variational inference and a power law generalization of Bayes’ rule. We conduct an empirical analysis of the application of MPS and EIG to our considered application. Our results indicate that MPS outperforms EIG while providing improved robustness.

Sensing information can be leveraged to reduce the overheads associated with establishing and maintaining multiple-input and multiple-output (MIMO) communication links. Such information can be acquired from integrated sensing and communication (ISAC) capabilities. In this paper, we use sensing information to make precoding for spatial multiplexing more robust in high-dynamic environments with both quasi-static and mobile scattering objects. The significant variability of scattered paths requires frequent reconfigurations of the precoding matrices. To address this, we propose a sensing-aided precoding scheme for channels with high dynamics, leveraging the identification and localization of moving targets to concentrate energy mainly on slow-varying paths. Specifically, the geometric structure of the time-varying channel matrix is analyzed to uncover deviations in the location parameters of moving targets. A channel division criterion is then devised to partition the channel matrix into two matrices characterizing the paths of background scatterers and moving targets, respectively. Exploiting the digital-analog dual orthogonality between these matrices, precoding and combining matrices are jointly designed to suppress transmission leakage over mobile paths in both analog and digital domains. Numerical simulations validate that the proposed method achieves high spectral efficiency without requiring explicit channel tracking, thereby reducing complexity and overheads compared to conventional MIMO precoding methods.

A dynamic metasurface antenna (DMA) is a slottedwaveguide antenna with tunable components that allow for beamforming with low power consumption. The resonant frequencies of the individual DMA slot elements are reconfigurable, which can be leveraged for wideband communications to support larger bandwidths. The vast majority of prior work on DMAs focuses on narrowband applications for DMAs. In this paper, we develop a beamforming algorithm to improve the performance of a multicarrier DMA-based wireless system with large signal bandwidths. We build upon prior DMA signal models to incorporate different frequency-selective aspects of the physical DMA elements, and investigate the impact of different DMA designs on wideband performance. We find that the proposed beamforming algorithm outperforms a baseline beamforming algorithm in terms of spectral efficiency and data rates under significant frequencyselectivity in the multi-carrier channel and DMA elements.

Fluid antenna systems (FASs) can reconfigure their locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance loss. In this paper, we propose the successive Bayesian reconstructor (S-BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S-BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S-BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as the maximum a posterior (MAP) estimator of FAS channels. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.

Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large MIMO systems. Mobile cellular systems like 5G use a beam management framework that joins the initial access, beamforming, CSI acquisition, and data transmission. The design of codebooks for these stages, however, is challenging due to their interrelationships, varying array sizes, and site-specific channel and user distributions. Furthermore, beam management is often focused on single-sector operations while ignoring the overarching network- and system-level optimization. In this paper, we proposed an end-to-end learned codebook design algorithm, network beamspace learning (NBL), that captures and optimizes codebooks to mitigate interference while maximizing the achievable performance with extremely large hybrid arrays. The proposed algorithm requires limited shared information yet designs codebooks that outperform traditional codebooks by over 10dB in beam alignment and achieve more than 25% improvements in network spectral efficiency.

Dynamic metasurface antennas (DMA) provide a solution to form compact, cost-effective, energy-efficient multiple-input-multiple output (MIMO) arrays. In this paper, we implement a practical hierarchical codebook with a realistic DMA design through electromagnetic simulations. We leverage existing DMA models to derive a novel method for enhancing the beamforming gain. We find that the proposed method provides better coverage and spectral efficiency results than prior methods. We also present and verify a new technique for creating wide beamwidths through the DMA and hierarchical codebook. Additionally, we use a detailed transmitter architecture model to determine the power consumption savings of the DMA compared to a typical phased array. The DMA largely outperforms a passive phased array in terms of spectral and energy efficiency due to high component loss from a high-resolution passive phase shifter. While the DMA provides lower spectral efficiency results than the active phased array, the DMA achieves a higher energy efficiency because of the significant power consumption for the active phase shifters. Therefore, we find that DMAs in a realistic wireless environment provide sufficient coverage and spectral efficiency compared to typical phased arrays while maintaining a substantially lower power consumption.

Reconfigurable intelligent surface (RIS) technology has emerged as a promising solution to address high-frequency band issues, including low penetration power and increasing shadowing regions, by artificially reconfiguring radio propagation. Previous research on RIS has pre-dominantly relied on metasurfaces based on specific diode-driven devices, and the practical challenges associated with this design have yet to be resolved. This article proposes potential RIS designs through meta-devices, including liquid crystal, 2D material, active metamaterial, and uniquely investigates an electrowetting-based beam tuning methodology. Taking into account the imperfect tuning capabilities of each device, we examine and reflect the relationship between the phase tuning range of RIS and the additional angular variation of the reflected wave. In addition, this article evaluates the RIS performance on a large scale via system-level simulation using 3D ray tracing, reflecting a realistic urban environment. We propose an evaluation methodology that integrates ray tracing with RIS's reflection characteristics and describes RIS-based beamforming throughput performance across various scenarios. Through the empirical evaluation of RIS incorporating meta-device characteristics, this work offers practical intuitions for RIS design contributing to its feasibility and efficiency for B5G/6G standardization and commercialization.

As MIMO dimensions become more extreme in 6G, users are likely to be in the near-field (NF) of the array. As a result, spatial focusing can be used to separate users in line-of-sight (LoS) channels who are in the same direction, unlike in the far-field (FF). In commercial deployments of MIMO, though, it seems likely that arrays will be of moderate size so that users are in a mix of NF and FF. In this paper, we propose a new characterization of mixed NF and FF operation. The idea is to determine the distances where a second NF user can be supported in the same direction as a FF user in terms of the sum rate. We use this new indicator to study the regions where near-field operation of a user is beneficial as a function of carrier and array size in upper mid-band frequencies.

Beam management is the defacto approach for configuring the antennas in 5G MIMO communication systems. Extending the beam management framework to larger arrays— also known as extreme MIMO systems—is challenging as the overheads grow with the array dimensions. One solution is to make use of the wealth of sensor data that is becoming available in integrated sensing and communication (ISAC) systems. In this paper, we propose a neural architecture for codebook design using environmental context derived from sensor data. In particular, we combine beamspace transformations with local occupancy grids obtained through network sensing to maximize the achievable rate in vehicular operations. Our results show significant performance gains over traditional codebooks while requiring less overhead than standard 5G beam management.

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents. The wide variety of scenarios supported in 5G and beyond 5G networks makes the RRS problem in RAN slicing scenario a significant challenge. This paper proposes an intent-aware reinforcement learning method to perform the RRS function in a RAN slicing scenario. The slice's quality of service intents is described in a common intent model in a service-level agreement. The proposed method tries to prevent intent faults by making the management of radio resources available among slices. This method uses slices' and user equipment network metrics in the observation space. The proposed method is evaluated under different network conditions and outperforms different baselines considering the slices' intents fulfillment.

From an information theoretic perspective, joint communication and sensing (JCAS) represents a natural generalization of communication network functionality. However, it requires the re-evaluation of network performance from a multi-objective perspective. We develop a novel mathematical framework for characterizing the sensing and communication coverage probability and ergodic rate in JCAS networks. We employ a formulation of sensing parameter estimation based on mutual information to extend the notions of coverage probability and ergodic rate to the radar setting. We define sensing coverage probability as the probability that the rate of information extracted about the parameters of interest associated with a typical radar target exceeds some threshold, and sensing ergodic rate as the spatial average of the aforementioned rate of information. Using this framework, we analyze the downlink sensing and communication coverage and rate of a mmWave JCAS network employing a shared waveform, directional beamforming, and monostatic sensing. Leveraging tools from stochastic geometry, we derive upper and lower bounds for these quantities. We also develop several general technical results including: i) a generic method for obtaining closed form upper and lower bounds on the Laplace Transform of a shot noise process, ii) a new analog of Hölder’s Inequality to the setting of harmonic means, and iii) a relation between the Laplace and Mellin Transforms of a non-negative random variable. We use the derived bounds to numerically investigate the performance of JCAS networks under varying base station and blockage density. Among several insights, our numerical analysis indicates that network densification improves sensing SINR performance – in contrast to communications.

With increasing frequencies, bandwidths, and array apertures, the phenomenon of beam squint arises as a serious impairment to beamforming. Fully digital arrays with true time delay per antenna element are a potential solution, but they require downconversion at each element. This paper shows that hybrid arrays can perform essentially as well as digital arrays once the number of radio-frequency chains exceeds a certain threshold that is far below the number of elements. The result is robust, holding also for suboptimum but highly appealing beamspace architectures.

In this paper, we propose a novel architecture for a lens antenna array (LAA) designed to work with a small number of antennas and enable angle-of-arrival (AoA) estimation for advanced 5G vehicle-to-everything (V2X) use cases that demand wider bandwidths and higher data rates. We derive a received signal in terms of optical analysis to consider the variability of the focal region for different carrier frequencies in a wideband multi-carrier system. By taking full advantage of the beam squint effect for multiple pilot signals with different frequencies, we propose a novel reconfiguration of antenna array (RAA) for the sparse LAA and a max-energy antenna selection (MS) algorithm for the AoA estimation. In addition, this paper presents an analysis of the received power at the single antenna with the maximum energy and compares it to simulation results. In contrast to previous studies on LAA that assumed a large number of antennas, which can require high complexity and hardware costs, the proposed RAA with MS estimation algorithm is shown that meets the requirements of 5G V2X in a vehicular environment while utilizing limited RF hardware and has low complexity.