Intelligent omni-surfaces (IOSs) with 360° electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a “DISCO” IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a “DISCO ball” that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results1based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU.

This paper presents a vehicular positioning (VP) method that enables a moving vehicle to localize potential targets using multipath reflected OFDM signals. Unlike traditional Vehicle-to-Everything (V2X) localization strategies, this approach empowers individual vehicle to detect multiple potential targets without consuming additional resources. We first apply SpotFi to achieve coarse time-of-flight (ToF) and angle-of-arrival (AoA) estimates as the initial estimation. Then, leveraging the slowly time-varying nature of ToF and AoA, we aggregate reflected OFDM signals across multiple time slots to form a third-order tensor, and optimize coarse estimates via a novel gradient descent (GD)-based alternating least squares (ALS) method that exploits the Vandermonde structure in tensor decomposition. The localization of adjacent targets is achieved by tracking AoA variations during vehicle motion, with positioning significantly improved through the enhanced AoA estimation. Numerical results validate our proposed method, illustrating the feasibility in dynamic vehicular scenarios.

Frequency mixing reconfigurable intelligent surface (FMx-RIS) is an innovative concept within the realm of RIS technology. It distinguishes itself from conventional RIS by continuously modifying the phase of incident electromagnetic waves, thereby inducing frequency shifts. By uniquely associating FMx-RIS elements with a specific frequency, this approach allows receivers to distinguish channels from each propagation path by detecting the frequency shifts. This decoupling feature enables channel estimation and augments diversity gain in the frequency domain. In this paper, we explore the architectural aspects of FMx-RIS in both single-carrier and multi-carrier systems to validate its practicality. For single-carrier systems, we derive closed-form lower bounds for achievable data rates under scenarios with perfect and imperfect channel state information, alongside the corresponding power scaling laws. For multi-carrier systems, our focus lies in the scheduling of reflective patterns for FMx-RISs, recognizing that frequency mixing operations introduce additional inter-carrier interference among users. We introduce two scheduling algorithms designed to maximize the minimum user signal-to-interference-plus-noise ratio. The numerical results confirm the analytical achievable rates and demonstrate that the FMx-RIS-aided system surpasses conventional RIS-aided systems. Furthermore, we assess the effectiveness of the proposed scheduling algorithms, where the ADMM-based scheme exhibits similar performance to the SoTA scheme.

The significantly increased array aperture and the higher communication frequency band in extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems con-siderably expand the near-field propagation region compared to conventional MIMO. Hybrid-field channels, encompassing both near- and far-fields, have become more common in propagation environments. Current channel estimation (CE) methods rely on angular- and polar-domain sparsity but require a prior knowledge of near- and far-field characteristics, which can be impractical for mobile users. Initially, we introduce a beamwidth-based determination criterion for distinguishing near- and far-field path components by analyzing the angular-domain power spectrum of the far-field signals. Then, we introduce the dynamic orthogonal matching pursuit (DOMP) algorithm to reconstruct individual near- and far-field channel paths, thereby recovering the hybrid-field channel. Our simulation results illustrate that our approach can achieve superior CE performance even without relying on a priori information regarding near- and far-field path components.

Reconfigurable intelligent surface (RIS) has emerged as a highly promising infrastructure benefiting from its capability to manipulate the propagation environment and facilitate efficient aggregation of wireless transmission signals. However, a major challenge in these systems is the significant overhead incurred by the acquisition of channel state information. This paper proposes a pre-designed beam-based transmission protocol that aims to reduce the burden of pilot overhead by designing a RIS reflective pattern codebook as an alternative to channel estimation (CE). We adopt an electromagnetic-compliant RIS model and develop a theoretical approximate expression for the channel gain, incorporating the impact of location mismatch. This approximation facilitates the construction of a location-based reflective pattern codebook, wherein the chosen locations linked with the codewords represent optimal location sampling points derived from theoretical results. By utilizing the constructed codebook, our proposed transmission protocol enables the system to search reflective patterns instead of real-time optimization. To validate our approach, extensive numerical simulations are conducted. The results demonstrate the accuracy of the approximate channel gain expression and highlight the superior coverage achieved by the proposed location-based reflective pattern codebook as well as the achievable spectral efficiency of our transmission protocol.

Due to a production error in[1], there is an error with the definition of the real and imaginary part operators. They should be defined as Re{} and Im{}. However, the {} are missing in all the equations. We apologize for this error.

This paper evaluates the performance of multi-user massive multiple-input multiple-output (MIMO) systems in which the base station is equipped with a dynamic metasurface antenna (DMA). Due to the physical implementation of DMAs, conventional models widely-used in MIMO are no longer valid, and electromagnetic phenomena such as mutual coupling, insertion losses and reflections inside the waveguides need to be considered. Hence, starting from a recently proposed electromagnetic model for DMAs, we formulate a zero-forcing optimization problem, yielding an unconstrained objective function with known gradient. The performance is compared with that of full-digital and hybrid massive MIMO, focusing on the impact of insertion losses and mutual coupling.

Dynamic metasurface antennas (DMAs) arise as a promising technology in the field of massive multiple-input multiple-output (mMIMO) systems, offering the possibility of integrating a large number of antennas in a limited —and potentially large— aperture while keeping the required number of radio-frequency (RF) chains under control. Although envisioned as practical realizations of mMIMO systems, DMAs represent a new paradigm in the design of signal processing techniques (such as beamforming) due to the constraints inherent to their physical implementation, for which no complete models are available yet. In this work, we propose a complete and electromagnetic-compliant narrowband communication model for a generic DMA based system. Specifically, the model accounts for: i) the wave propagation and reflections throughout the waveguides that feed the antenna elements, ii) the mutual coupling both through the air and the waveguides, and iii) the insertion losses. Also, we integrate the electromagnetic model in the conventional digital communication model, providing a complete and useful framework to design and characterize the performance of these systems. Finally, the accuracy of the model is verified through full-wave simulations.

The concept of large intelligent surface (LIS)-based communication has recently attracted increasing research attention, where a LIS is considered as an antenna array whose entire surface area is available for radio signal transmission and reception. In order to provide a fundamental understanding of LIS-based communication, this paper studies the uplink performance of LIS-based communication with matched filtering in the presence of a line-of-sight channel. We first study the new features introduced by LIS. In particular, the array gain, spatial resolution, and the capability of interference suppression are theoretically presented and characterized. Then, we study two possible LIS system layouts, i.e., centralized LIS (C-LIS) and distributed LIS (D-LIS), and propose a user association scheme aiming to maximize the minimum user spectral efficiency (SE). Simulation results compare the achievable SE between two system layouts. We observe that the proposed user association algorithm significantly improves the performance of D-LIS, and with the help of it, the per-user achievable SE in D-LIS outperforms that in C-LIS in most considered scenarios.

Massive connectivity is a key requirement for the Internet of Things (IoT). In practice, the network should be capable of accommodating thousands of devices and meeting their traffic demands. In this article, we consider the access phase for IoT in a mixed-analog-to-digital converter distributed massive multiple-input-multiple-output system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion. For users with light-load traffic demands, by formulating the user activity detection (UAD) and channel estimation (CE) into a compressed sensing problem, we provide a low-complexity algorithm solver which requires no prior information. The simulation results verify that the proposed algorithm can effectively detect user activity and estimate channel state information (CSI) between the users and access points (APs). To satisfy the throughput requirements of heavy-load users, after UAD and CE, a two-step dynamic clustering is proposed for coordinated multipoint transmission using the large-scale fading (LSF) information. The impact of quantization noise on LSF estimation is investigated, as well as, a corresponding compensation method and accuracy bound. By detecting the clustering behavior among users in the first step, the complexity of the joint user and AP clustering is substantially reduced. The numerical results reveal that the proposed algorithm can offer significant performance gains in various scenarios with fast convergence.

The concept of large intelligent surface (LIS)-based communication has recently raised research attention, in which a LIS is regarded as an antenna array whose entire surface area can be used for radio signal transmission and reception. To provide a fundamental understanding of LIS-based communication, this paper studies the uplink (UL) performance of LIS-based communication with matched filtering. We first investigate the new properties introduced by LIS. In particular, the array gain, spatial resolution, and the capability of interference suppression are theoretically presented and characterized. Then, we study two possible LIS system layouts in terms of UL, i.e., centralized LIS (C-LIS) and distributed LIS (D-LIS). Our analysis showcases that a centralized system has strong capability of interference suppression; in fact, interference can nearly be eliminated if the surface area is sufficient large or the frequency band is sufficient high. For D-LIS, we propose a series of resource allocation algorithms, including user association scheme, orientation control, and power control, to extend the coverage area of a distributed system. Simulation results show that the proposed algorithms significantly improve the system performance, and even more importantly, we observe that D-LIS outperforms C-LIS in microwave bands, while C-LIS is superior to D-LIS in mmWave bands. These observations serve as useful guidelines for practical LIS deployments.

To support the ever increasing number of devices in massive multiple-input multiple-output (mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation schemes. To circumvent this fundamental constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The presence of the temporal channel correlation is due to the stationarity of the propagation environment across time. The proposed ML-based predictors involve a pattern extraction implemented via a convolutional neural network, and a CSI predictor realized by an autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network. Closed-form expressions for the user uplink and downlink achievable spectral efficiency and average per-user throughput are provided for the ML-based time division duplex schemes. Our numerical results demonstrate that the proposed ML-based predictors can remarkably improve the prediction quality for both low and high mobility scenarios, and offer great performance gains on the per-user achievable throughput.

This paper proposes a low-cost distributed massive multiple-input multiple-output (MIMO) system, which employs the mixed analog-to-digital converter (ADC) remote radio heads (RRHs). In particular, the RRHs with low-resolution ADCs connect with the baseband unit through wireless fronthaul while the RRHs with full-resolution ADCs connect with the baseband unit through fiber. After estimating the channel state information between RRHs and users, we derive the closed-form expressions for achievable downlink rate and energy efficiency. Based on these analytical results, we find that our distributed architecture can obtain remarkable gains compared with the centralized layout. Moreover, compared with the conventional distributed network with pure expensive full-resolution ADCs, our mixed-ADC architecture can achieve the rate requirement in a more energy-efficient and low-cost way, especially for the low rate demands. Additionally, we also present the optimal RRH assignment proportion that can maximize the energy efficiency under a fixed total number of RRHs, which can be used as guidelines for practical network configuration.

Ultra-dense cloud radio access networks (C-RANs) are an example of the architectures that will be critical components of the next-generation wireless systems. In a C-RAN architecture, an amorphous cellular framework, where each user connects to a few nearby remote radio heads (RRHs) to form its own cell, appears to be promising. In this paper, we study the ergodic capacity of such amorphous cellular networks at high signal-to-noise ratios (SNRs) where we model the distribution of the RRHs by a Poisson point process. We derive tractable approximations of the ergodic capacity at high-SNRs for arbitrary antenna configurations, and tight lower bounds for the ergodic capacity when the numbers of antennas are the same at both ends of the link. In contrast to prior works on distributed antenna systems, our results are derived based on random matrix theory and involve only standard functions which can be much more easier evaluated. The impact of the system parameters on the ergodic capacity is investigated. By leveraging our analytical results, we propose two efficient scheduling algorithms for RRH selection for energy-efficient transmission. Our algorithms offer a substantial improvement in energy efficiency compared with the strategy of connecting a fixed number of RRHs to each user.

Due to the difficulty in manipulating the distribution of Wishart random matrices, the performance analysis of multiple-input-multiple-output (MIMO) channels has mainly focused on deriving capacity bounds via Jensen's inequality. However, to the best of our knowledge, the tightness of Jensen's bounds has not yet been rigorously quantified in the general MIMO context. This paper proposes a new methodology for measuring the tightness of Jensen's bounds via the sandwich theorem. In particular, we first compare the tightness of two different pairs of upper/lower bounds for a general class of MIMO channels based on the unordered eigenvalue of the instantaneous correlation matrix and for arbitrary numbers of antennas. The tightness of Jensen's bounds in different channel scenarios is investigated including multiuser MIMO with maximal ratio combining. Our analysis is facilitated by deriving some new results for finite-dimensional Wishart matrices, i.e., for the arbitrary moments of the unordered eigenvalue of central and non-central Wishart matrices. Our results provide very interesting insights into the implications of the system parameters, such as the number of antennas, and signal-to-noise ratio, on the tightness of Jensen's bounds, and showcase the suitability and limitations of Jensen's bounds.