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Federated Learning (FL) enables clients to share learning parameters instead of local data, reducing communication overhead. Traditional wireless networks face latency challenges with FL. In contrast, Cell-Free Massive MIMO (CFmMIMO) can serve multiple clients on shared resources, boosting spectral efficiency and reducing latency for large-scale FL. However, clients' communication resource limitations can hinder the completion of the FL training. To address this challenge, we propose an energy-efficient, low-latency FL framework featuring optimized uplink power allocation for seamless client-server collaboration. Our framework employs an adaptive quantization scheme, dynamically adjusting bit allocation for local gradient updates to reduce communication costs. We formulate a joint optimization problem covering FL model updates, local iterations, and power allocation, solved using sequential quadratic programming (SQP) to balance energy and latency. Additionally, clients use the AdaDelta method for local FL model updates, enhancing local model convergence compared to standard SGD, and we provide a comprehensive analysis of FL convergence with AdaDelta local updates. Numerical results show that, within the same energy and latency budgets, our power allocation scheme outperforms the Dinkelbach and max-sum rate methods by increasing the test accuracy up to $7$\% and $19$\%, respectively. Moreover, for the three power allocation methods, our proposed quantization scheme outperforms AQUILA and LAQ by increasing test accuracy by up to $36$\% and $35$\%, respectively.

Physical layer multicasting is an efficient transmission technique that exploits the beamforming potential at the transmitting nodes and the broadcast nature of the wireless channel, together with the demand for the same content from several UEs. This paper addresses the max-min fair multigroup multicast beamforming optimization, which is an NP-hard problem. We propose a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-global optimum rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network setup. The proposed optimization procedure shows significant improvements in computational complexity and spectral efficiency performance compared to the SDR followed by the commonly used randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The significance of the proposed procedure is that it can be utilized as a rank reduction method for any problem in conjunction with SDR., Comment: 6 pages, accepted at IEEE Global Communications Conference (Globecom), 2024

Holographic multiple-input multiple-output (MIMO) systems represent a spatially constrained MIMO architecture with a massive number of antennas with small antenna spacing as a close approximation of a spatially continuous electromagnetic aperture. Accurate channel modeling is essential for realizing the full potential of this technology. In this paper, we investigate the impact of mutual coupling and spatial channel correlation on the estimation precision in holographic MIMO systems, as well as the importance of knowing their characteristics. We demonstrate that neglecting mutual coupling can lead to significant performance degradation for the minimum mean squared error estimator, emphasizing its critical consideration when designing estimation algorithms. Conversely, the least-squares estimator is resilient to mutual coupling but only yields good performance in high signal-to-noise ratio regimes. Our findings provide insights into how to design efficient estimation algorithms in holographic MIMO systems, aiding its practical implementation.

Federated learning (FL) is a distributed learning framework where users train a global model by exchanging local model updates with a server instead of raw datasets, preserving data privacy and reducing communication overhead. However, the latency grows with the number of users and the model size, impeding the successful FL over traditional wireless networks with orthogonal access. Cell-free massive multiple-input multipleoutput (CFmMIMO) is a promising solution to serve numerous users on the same time/frequency resource with similar rates. This architecture greatly reduces uplink latency through spatial multiplexing but does not take application characteristics into account. In this paper, we co-optimize the physical layer with the FL application to mitigate the straggler effect. We introduce a novel adaptive mixed-resolution quantization scheme of the local gradient vector updates, where only the most essential entries are given high resolution. Thereafter, we propose a dynamic uplink power control scheme to manage the varying user rates and mitigate the straggler effect. The numerical results demonstrate that the proposed method achieves test accuracy comparable to classic FL while reducing communication overhead by at least 93% on the CIFAR-10, CIFAR-100, and Fashion-MNIST datasets. We compare our methods against AQUILA, Top-q, and LAQ, using the max-sum rate and Dinkelbach power control schemes. Our approach reduces the communication overhead by 75% and achieves 10% higher test accuracy than these benchmarks within a constrained total latency budget.

This paper investigates coordinated beamforming using a modular linear array (MLA), composed of a pair of physically separated uniform linear arrays (ULAs), treated as sub-arrays. We focus on how such setups can give rise to near-field effects in 6G networks without requiring many antennas. Unlike conventional far-field beamforming, near-field beamforming enables simultaneous data service to multiple users at different distances in the same angular direction, offering significant multiplexing gains. We present a detailed analysis, including analytical expressions of the beamwidth and beamdepth for the MLA. Our findings reveal that using the MLA approach, we can remove approximately 36% of the antennas in the ULA while achieving the same level of beamfocusing.

Cell-free massive MIMO improves the fairness among the user equipments (UEs) in the network by distributing many cooperating access points (APs) around the region while connecting them to a centralized cloud-computing unit that coordinates joint transmission/reception. However, the fiber cable deployment for the fronthaul transport network and activating all available antennas at each AP lead to increased deployment cost and power consumption for fronthaul signaling and processing. To overcome these challenges, in this work, we consider wireless fronthaul connections and propose a joint antenna activation and power allocation algorithm to minimize the end-to-end (from radio to cloud) power while satisfying the quality-of-service requirements of the UEs under wireless fronthaul capacity limitations. The results demonstrate that the proposed methodology of deactivating antennas at each AP reduces the power consumption by 50% and 84% compared to the benchmarks based on shutting down APs and minimizing only the transmit power, respectively., Comment: Presented in Asilomar Signals, Systems and Computers 2024

Reconfigurable intelligent surfaces (RISs) can improve the capacity of wireless communication links by passively beamforming the impinging signals in desired directions. This feature has been demonstrated both analytically and experimentally for conventional RISs, consisting of independently reflecting elements. To further enhance reconfigurability, a new architecture called beyond-diagonal RIS (BD-RIS) has been proposed. It allows for controllable signal flows between RIS elements, resulting in a non-diagonal reflection matrix, unlike the conventional RIS architecture. Previous studies on BD-RIS-assisted communications have predominantly considered single-antenna transmitters/receivers. One recent work provides an iterative capacity-improving algorithm for multiple-input multiple-output (MIMO) setups but without providing geometrical insights. In this paper, we derive the first closed-form capacity-maximizing BD-RIS reflection matrix for a MIMO channel. We describe how this solution pairs together propagation paths, how it behaves when the signal-to-noise ratio is high, and what capacity is achievable with ideal semi-unitary channel matrices. The analytical results are corroborated numerically., Comment: 5 pages, 4 figures, submitted as a conference paper

Terahertz (THz) communication is envisioned as a key technology for 6G and beyond wireless systems owing to its multi-GHz bandwidth. To maintain the same aperture area and the same link budget as the lower frequencies, ultra-massive multi-input and multi-output (UM-MIMO) with hybrid beamforming is promising. Nevertheless, the hardware imperfections particularly at THz frequencies, can degrade spectral efficiency and lead to a high symbol error rate (SER), which is often overlooked yet imperative to address in practical THz communication systems. In this paper, the hybrid beamforming is investigated for THz UM-MIMO systems accounting for comprehensive hardware imperfections, including DAC and ADC quantization errors, in-phase and quadrature imbalance (IQ imbalance), phase noise, amplitude and phase error of imperfect phase shifters and power amplifier (PA) nonlinearity. Then, a two-stage hardware imperfection compensation algorithm is proposed. A deep neural network (DNN) is developed in the first stage to represent the combined hardware imperfections, while in the second stage, the digital precoder in the transmitter (Tx) or the combiner in the receiver (Rx) is designed using NN to effectively compensate for these imperfections. Furthermore, to balance the performance and network complexity, three slimming methods including pruning, parameter sharing, and removing parts of the network are proposed and combined to slim the DNN in the first stage. Numerical results show that the Tx compensation can perform better than the Rx compensation. Additionally, using the combined slimming methods can reduce parameters by 97.2% and running time by 39.2% while maintaining nearly the same performance in both uncoded and coded systems.

Accurate estimation of the cascaded channel from a user equipment (UE) to a base station (BS) via each reconfigurable intelligent surface (RIS) element is critical to realizing the full potential of the RIS's ability to control the overall channel. The number of parameters to be estimated is equal to the number of RIS elements, requiring an equal number of pilots unless an underlying structure can be identified. In this paper, we show how the spatial correlation inherent in the different RIS channels provides this desired structure. We first optimize the RIS phase-shift pattern using a much-reduced pilot length (determined by the rank of the spatial correlation matrices) to minimize the mean square error (MSE) in the channel estimation under electromagnetic interference. In addition to considering the linear minimum MSE (LMMSE) channel estimator, we propose a novel channel estimator that requires only knowledge of the array geometry while not requiring any user-specific statistical information. We call this the reduced-subspace least squares (RS-LS) estimator and optimize the RIS phase-shift pattern for it. This novel estimator significantly outperforms the conventional LS estimator. For both the LMMSE and RS-LS estimators, the proposed optimized RIS configurations result in significant channel estimation improvements over the benchmarks., Comment: 16 pages, 9 figures, to appear in IEEE Transactions on Wireless Communications

Integrated sensing and communications (ISAC) allows networks to perform sensing alongside data transmission. While most ISAC studies focus on single-target, multi-user scenarios, multi-target sensing is scarcely researched. This letter examines the monostatic sensing performance of a multi-target massive MIMO system, aiming to minimize the sum of Cram\'er-Rao lower bounds (CRLBs) for target direction-of-arrival estimates while meeting user equipment (UE) rate requirements. We propose several precoding schemes, comparing sensing performance and complexity, and find that sensing-focused precoding with power allocation for communication achieves near-optimal performance with 20 times less complexity than joint precoding. Additionally, time-sharing between communication and sensing outperforms simple time division, highlighting the benefits of resource-sharing for ISAC.

As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. This paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis. The paper presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies., Comment: 32 pages, submitted to IEEE

The reconfigurable intelligent surface (RIS) technology emerges as a highly useful component of the rapidly evolving integrated sensing and communications paradigm, primarily owing to its remarkable signal-to-noise ratio enhancement capabilities. In this paper, our focus is on mono-static target detection while considering the communication requirement of a user equipment. Both sensing and communication benefit from the presence of an RIS, which makes the channels richer and stronger. Diverging from prior research, we comprehensively examine three target echo paths: the direct (static) channel path, the path via the RIS, and a combination of these, each characterized by distinct radar cross sections (RCSs). We take both the line-of-sight (LOS) and the non-line-of-sight (NLOS) paths into account under a clutter for which the distribution is not known, but the low-rank subspace it resides. We derive the generalized likelihood ratio test (GLRT) detector and introduce a novel approach for jointly optimizing the configuration of RIS phase-shifts and precoding. Our simulation results underscore the paramount importance of this combined design in terms of enhancing detection probability. Moreover, it becomes evident that the derived clutter-aware target detection significantly enhances detection performance, especially when the clutter is strong., Comment: 6 pages, 3 figures, accepted to be presented at IEEE GLOBECOM 2024

A large-scale MIMO (multiple-input multiple-output) system offers significant advantages in wireless communication, including potential spatial multiplexing and beamforming capabilities. However, channel estimation becomes challenging with multiple antennas at both the transmitter and receiver ends. The minimum mean-squared error (MMSE) estimator, for instance, requires a spatial correlation matrix whose dimensions scale with the square of the product of the number of antennas on the transmitter and receiver sides. This scaling presents a substantial challenge, particularly as antenna counts increase in line with current technological trends. Traditional MIMO literature offers alternative channel estimators that mitigate the need to fully acquire the spatial correlation matrix. In this paper, we revisit point-to-point MIMO channel estimation and introduce a reduced-subspace least squares (RS-LS) channel estimator designed to eliminate physically impossible channel dimensions inherent in uniform planar arrays. Additionally, we propose a cluster-aware RS-LS estimator that leverages both reduced and cluster-specific subspace properties, significantly enhancing performance over the conventional RS-LS approach. Notably, both proposed methods obviate the need for fully/partial knowledge of the spatial correlation matrix., Comment: 6 pages, 2 figures, accepted to be presented at IEEE GLOBECOM 2024

Cell-free massive MIMO (multiple-input multiple-output) is a promising network architecture for beyond 5G systems, which can particularly offer more uniform data rates across the coverage area. Recent works have shown how reconfigurable intelligent surfaces (RISs) can be used as relays in cell-free massive MIMO networks to improve data rates further. In this paper, we analyze an alternative architecture where an RIS is integrated into the antenna array at each access point and acts as an intelligent transmitting surface to expand the aperture area. This approach alleviates the multiplicative fading effect that normally makes RIS-aided systems inefficient and offers a cost-effective alternative to building large antenna arrays. We use a small number of antennas and a larger number of controllable RIS elements to match the performance of an antenna array whose size matches that of the RIS. In this paper, we explore this innovative transceiver architecture in the uplink of a cell-free massive MIMO system for the first time, demonstrating its potential benefits through analytic and numerical contributions. The simulation results validate the effectiveness of our proposed phase-shift configuration and highlight scenarios where the proposed architecture significantly enhances data rates., Comment: 5 pages, 4 figures, presented at IEEE SPAWC 2024

The use of a reconfigurable intelligent surface (RIS) for aiding user-specific transmission has been widely explored. However, little attention has been devoted to utilizing RIS for assisting cell-specific transmission, where the RIS needs to reflect signals in a broad angular range. Furthermore, although modern communication systems operate in two polarizations, the majority of the works on RIS consider a uni-polarized surface, only reflecting the signals in one polarization. To fill these gaps, we study a downlink broadcasting scenario where a base station (BS) sends a cell-specific signal to all the users residing at unknown locations with the assistance of a dual-polarized RIS. We utilize the duality between the auto-correlation function and power spectrum in the space/spatial frequency domain to design configurations for broad-beam reflection. We first consider a free-space line-of-sight BS-RIS channel and show that the RIS configuration matrices must form a Golay complementary array pair for broad-beam radiation. We also present how to form Golay complementary array pairs based on known Golay complementary sequence pairs. We then consider an arbitrary BS-RIS channel and propose an algorithm based on stochastic optimization to find RIS configurations that produce a practically broad beam by relaxing the requirement on uniform broadness. Numerical simulations are finally conducted to corroborate the analyses and evaluate the performance., Comment: Accepted for publication in IEEE Transactions on Wireless Communications

In this paper, we consider a single-anchor localization system assisted by a reconfigurable intelligent surface (RIS), where the objective is to localize multiple user equipments (UEs) placed in the radiative near-field region of the RIS by estimating their azimuth angle-of-arrival (AoA), elevation AoA, and distance to the surface. The three-dimensional (3D) locations can be accurately estimated via the conventional MUltiple SIgnal Classification (MUSIC) algorithm, albeit at the expense of tremendous complexity due to the 3D grid search. In this paper, capitalizing on the symmetric structure of the RIS, we propose a novel modified MUSIC algorithm that can efficiently decouple the AoA and distance estimation problems and drastically reduce the complexity compared to the standard 3D MUSIC algorithm. Additionally, we introduce a spatial smoothing method by partitioning the RIS into overlapping sub-RISs to address the rank-deficiency issue in the signal covariance matrix. We corroborate the effectiveness of the proposed algorithm via numerical simulations and show that it can achieve the same performance as 3D MUSIC but with much lower complexity., Comment: This paper has been accepted for presentation at IEEE GLOBECOM 2024

In this paper, we examine the energy efficiency (EE) of a base station (BS) with multiple antennas. We use a state-of-the-art power consumption model, taking into account the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number of antennas as the optimization variables. We provide novel closed-form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna. We present a novel algorithm that jointly optimizes these variables to achieve maximum EE, while fulfilling constraints on the variable ranges. We also discover a new relationship between the radiated power and the passive transceiver power consumption. We provide analytical insight into whether using maximum power or bandwidth is optimal and how many antennas a BS should utilize., Comment: 6 pages, 4 figures

Over-the-air computation (AirComp) is considered as a communication-efficient solution for data aggregation and distributed learning by exploiting the superposition properties of wireless multi-access channels. However, AirComp is significantly affected by the uneven signal attenuation experienced by different wireless devices. Recently, Cell-free Massive MIMO (mMIMO) has emerged as a promising technology to provide uniform coverage and high rates by joint coherent transmission. In this paper, we investigate AirComp in Cell-free mMIMO systems, taking into account spatially correlated fading and channel estimation errors. In particular, we propose optimal designs of transmit coefficients and receive combing at different levels of cooperation among access points. Numerical results demonstrate that Cell-free mMIMO using fully centralized processing significantly outperforms conventional Cellular mMIMO with regard to the mean squared error (MSE). Moreover, we show that Cell-free mMIMO using local processing and large-scale fading decoding can achieve a lower MSE than Cellular mMIMO when the wireless devices have limited power budgets., Comment: This paper has been accepted by IEEE CAMAD 2024

The design of Reconfigurable Intelligent Surfaces (RISs) is typically based on treating the RIS as an infinitely large surface that steers incident plane waves toward the desired direction. In practical implementations, however, the RIS has finite size and the incident wave is a beam of finite $k$-content, rather than a plane wave of $\delta$-like $k$-content. To understand the implications of the finite extent of both the RIS and the incident beam, here we treat the RIS as a spatial filter, the transfer function of which is determined by both the prescribed RIS operation and the shape of the RIS boundary. Following this approach, we study how the RIS transforms the incident $k$-content and we demonstrate how, by engineering the RIS shape, size, and response, it is possible to shape beams with nontrivial $k$-content to suppress unwanted interference, while concentrating the reflected power to desired directions. We also demonstrate how our framework, when applied in the context of near-field communications, provides the necessary insights into how the wavefront of the beam is tailored to enable focusing, propagation with invariant profile, and bending, beyond conventional beamforming., Comment: 13 pages, 9 figures

The forthcoming 6G and beyond wireless networks are anticipated to introduce new groundbreaking applications, such as Integrated Sensing and Communications (ISAC), potentially leveraging much wider bandwidths at higher frequencies and using significantly larger antenna arrays at base stations. This puts the system operation in the radiative near-field regime of the BS antenna array, characterized by spherical rather than flat wavefronts. In this paper, we refer to such a system as near-field ISAC. Unlike the far-field regime, the near-field regime allows for precise focusing of transmission beams on specific areas, making it possible to simultaneously determine a target's direction and range from a single base station and resolve targets located in the same direction. This work designs the transmit symbol vector in near-field ISAC to maximize a weighted combination of sensing and communication performances subject to a total power constraint using symbol-level precoding (SLP). The formulated optimization problem is convex, and the solution is used to estimate the angle and range of the considered targets using the 2D MUSIC algorithm. The simulation results suggest that the SLP-based design outperforms the block-level-based counterpart. Moreover, the 2D MUSIC algorithm accurately estimates the targets' parameters.

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading channels. We provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination., Comment: 13 pages, 8 figures, submitted to IEEE Trans

In the design of a metasurface-assisted system for indoor environments, it is essential to take into account not only the performance gains and coverage extension provided by the metasurface but also the operating costs brought by its reconfigurability, such as powering and cabling. These costs can present challenges, particularly in indoor dense spaces (IDSs). A self-sustainable metasurface (SSM), which retains reconfigurability unlike a static metasurface (SMS), achieves a lower operating cost than a reconfigurable intelligent surface (RIS) by being self-sustainable through power harvesting. In this paper, in order to find a better trade-off between metasurface gain, coverage, and operating cost, the design and performance of an SSM-assisted indoor mmWave communication system are investigated. We first simplify the design of the SSM-assisted system by considering the use of SSMs in a preset-based manner and the formation of coverage groups by associating SSMs with the closest user equipments (UEs). We propose a two-stage iterative algorithm to maximize the minimum data rate in the system by jointly deciding the association between the UEs and the SSMs, the phase-shifts of the SSMs, and allocating time resources for each UE. The non-convexities that exist in the proposed optimization problem are tackled using the feasible point pursuit successive convex approximation method and the concave-convex procedure. To understand the best scenario for using SSM, the resulting performance is compared with that achieved with RIS and SMS. Our numerical results indicate that SSMs are best utilized in a small environment where self-sustainability is easier to achieve when the budget for operating costs is tight., Comment: 13 Pages, 16 Figures, submitted to IEEE Transaction on Wireless Communication, under review

Movable antennas (MAs), traditionally explored in antenna design, have recently garnered significant attention in wireless communications due to their ability to dynamically adjust the antenna positions to changes in the propagation environment. However, previous research has primarily focused on characterizing the performance limits of various MA-assisted wireless communication systems, with less emphasis on their practical implementation. To address this gap, in this article, we propose several general MA architectures that extend existing designs by varying several key aspects to cater to different application scenarios and tradeoffs between cost and performance. Additionally, we draw from fields such as antenna design and mechanical control to provide an overview of candidate implementation methods for the proposed MA architectures, utilizing either direct mechanical or equivalent electronic control. Simulation results are finally presented to support our discussion.

Extremely large-scale multiple-input multipleoutput (XL-MIMO) is believed to be a cornerstone of sixth-generation (6G) wireless networks. XL-MIMO uses more antennas to both achieve unprecedented spatial degrees of freedom (DoFs) and exploit new electromagnetic (EM) phenomena occurring in the radiative near-field. The near-field effects provide the XL-MIMO array with depth perception, enabling precise localization and spatially multiplexing jointly in the angle and distance domains. This article delineates the distinctions between near-field and far-field propagation, highlighting the unique EM characteristics introduced by having large antenna arrays. It thoroughly examines the challenges these new near-field characteristics pose for user localization and channel estimation and provides a comprehensive review of new algorithms developed to address them. The article concludes by identifying critical future research directions., Comment: 9 pages, 4 figures, 2tables, submitted to IEEE WCM

As wireless technology begins to utilize physically larger arrays and/or higher frequencies, the transmitter and receiver will reside in each other's radiative near field. This fact gives rise to unusual propagation phenomena such as spherical wavefronts and beamfocusing, creating the impression that new spatial dimensions -- called degrees-of-freedom (DoF) -- can be exploited in the near field. However, this is a fallacy because the theoretically maximum DoF are already achievable in the far field. This paper sheds light on these issues by providing a tutorial on spatial frequencies, which are the fundamental components of wireless channels, and by explaining their role in characterizing the DoF in the near and far fields. In particular, we demonstrate how a single propagation path utilizes one spatial frequency in the far field and an interval of spatial frequencies in the near field. We explain how the array geometry determines the number of distinguishable spatial frequency bins and, thereby, the spatial DoF. We also describe how to model near-field multipath channels and their spatial correlation matrices. Finally, we discuss the research challenges and future directions in this field.

Reconfigurable intelligent surfaces (RISs) have emerged as one of the most studied topics in recent years, hailed as a transformative technology with the potential to revolutionize future wireless systems. While RISs are recognized for their ability to enhance spectral efficiency, coverage, and the reliability of wireless channels, several challenges remain. Notably, convincing and profitable use cases must be developed before widespread commercial deployment can be realized. The first sixth-generation (6G) networks will most likely utilize upper mid-band frequencies (i.e., 7-24\,GHz). This is regarded as the \textit{golden band} since it combines good coverage, much new spectrum, and enables many antennas in compact form factors. There has been much prior work on channel modeling, coexistence, and possible implementation scenarios for these bands. There are significant frequency-specific challenges related to RIS deployment, use cases, number of required elements, channel estimation, and control. These are previously unaddressed for the upper mid-band. In this paper, we aim to bridge this gap by exploring various use cases, including RIS-assisted fixed wireless access (FWA), enhanced capacity in mobile communications, and increased reliability at the cell edge. We identify the conditions under which RIS can provide major benefits and optimal strategies for deploying RIS to enhance the performance of 6G upper mid-band communication systems., Comment: submitted to IEEE Magazine

The initial 6G networks will likely operate in the upper mid-band (7-24 GHz), which has decent propagation conditions but underwhelming new spectrum availability. In this paper, we explore whether we can anyway reach the ambitious 6G performance goals by evolving the multiple-input multiple-output (MIMO) technology from being massive to gigantic. We describe how many antennas are needed and can realistically be deployed, and what the peak user rate and degrees-of-freedom (DOF) can become. We further suggest a new deployment strategy that enables the utilization of radiative near-field effects in these bands for precise beamfocusing, localization, and sensing from a single base station site. Finally, we identify five open research challenges that must be overcome to efficiently use gigantic MIMO dimensions in 6G from hardware, cost, and algorithmic perspectives., Comment: Submitted to publication, 7 pages, 6 figures

We reconsider the problem of joint power control and beamforming design to maximize the weighted sum rate in large and potentially cell-free massive MIMO networks. In contrast to the available short-term methods, where an iterative algorithm is run for every instantaneous channel realization, we derive an iterative algorithm that can be run only sporadically leveraging known channel statistics, with minor performance loss. In addition, our algorithm also applies to the design of non-trivial cooperative beamforming schemes subject to limited sharing of instantaneous channel state information. Furthermore, our algorithm generalizes and outperforms the competing long-term methods from the massive MIMO literature, which are restricted to long-term power control only or to long-term joint power control and large-scale fading decoding design., Comment: Accepted for presentation, IEEE SPAWC 2024. arXiv admin note: text overlap with arXiv:2312.02080

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the precoding matrix is selected in a baseband unit (BBU) and then sent over a digital fronthaul to the transmitting antenna array. The fronthaul has a limited bit resolution with a known quantization behavior. We formulate a new sum rate maximization problem where the precoding matrix elements must comply with the quantizer. We solve this non-convex mixed-integer problem to local optimality by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. The precoding optimization subproblem becomes an integer least-squares problem, which we solve with a new algorithm using a sphere decoding (SD) approach. We show numerically that the proposed precoding technique vastly outperforms the baseline of optimizing an infinite-resolution precoder and then quantizing it. We also develop a heuristic quantization-aware precoding that outperforms the baseline while having comparable complexity., Comment: 6 pages, 3 figures, published in ICC 203. arXiv admin note: text overlap with arXiv:2209.01868

In this paper, we consider a downlink multi-user multiple-input multiple-output (MU-MIMO) communication assisted by a reconfigurable intelligent surface (RIS) and study the precoding and RIS configuration design under practical system constraints. These constraints include the limited-capacity fronthaul at the transmitter side and the finite resolution of RIS elements. We investigate the sum mean squared error (MSE) minimization problem and propose an algorithm based on the block coordinate descent method to optimize the precoding, RIS configuration, and receiver gains. We compute the precoding vectors and RIS configuration using the Schnorr-Euchner sphere decoding (SESD) method which delivers the optimal MSE-minimizing solution. We numerically evaluate the performance of the proposed SESD-based methods and corroborate their effectiveness in improving the system performance., Comment: Accepted for presentation at VTC-Spring 2024

The signal processing community is currently witnessing a growing interest in near-field signal processing, driven by the trend towards the use of large aperture arrays with high spatial resolution in the fields of communication, localization, sensing, imaging, etc. From the perspective of localization and sensing, this trend breaks the basic far-field assumptions that have dominated the array signal processing research in the past, presenting new challenges and promising opportunities., Comment: 25 pages, 10 figures

Extremely large aperture arrays (ELAAs) can offer massive spatial multiplexing gains in the radiative near-field region in beyond 5G systems. While near-field channel modeling for uniform linear arrays has been extensively explored in the literature, uniform planar arrays-despite their advantageous form factor-have been somewhat neglected due to their more complex nature. Spatial correlation is crucial for non-line-of-sight channel modeling. Unlike far-field scenarios, the spatial correlation properties of near-field channels have not been thoroughly investigated. In this paper, we start from the fundamentals and develop a near-field spatial correlation model for arbitrary spatial scattering functions. Furthermore, we derive the lower dimensional subspace where the channel vectors can exist. It is based on prior knowledge of the three-dimensional coverage region where scattering clusters exists and we derive a tractable one-dimensional integral expression. This subspace is subsequently employed in a reduced-subspace least squares (RS-LS) estimation method for near-field channels, thereby enhancing performance over the traditional least squares estimator without the need for having full spatial correlation matrix knowledge.

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the downlink communication between a base station (BS) and multiple user equipments (UEs) is aided by a reconfigurable intelligent surface (RIS). We study the sum rate maximization problem with the objective of finding the optimal precoding vectors and RIS configuration. Due to fronthaul limitation, each entry of the precoding vectors must be picked from a finite set of quantization labels. Furthermore, two scenarios for the RIS are investigated, one with continuous infinite-resolution reflection coefficients and another with discrete finite-resolution reflection coefficients. A novel framework is developed which, in contrast to the common literature that only offers sub-optimal solutions for optimization of discrete variables, is able to find the optimal solution to problems involving discrete constraints. Based on the classical weighted minimum mean square error (WMMSE), we transform the original problem into an equivalent weighted sum mean square error (MSE) minimization problem and solve it iteratively. We compute the optimal precoding vectors via an efficient algorithm inspired by sphere decoding (SD). For optimizing the discrete RIS configuration, two solutions based on the SD algorithm are developed: An optimal SD-based algorithm and a low-complexity heuristic method that can efficiently obtain RIS configuration without much loss in optimality. The effectiveness of the presented algorithms is corroborated via numerical simulations where it is shown that the proposed designs are remarkably superior to the commonly used benchmarks., Comment: Accepted for Publication in IEEE Transactions on Communications

Digital phased arrays have often been disregarded for millimeter-wave communications since the analog-to-digital converters (ADCs) are power-hungry. In this paper, we provide a different perspective on this matter by demonstrating analytically and numerically how the ADC resolution can be reduced when using digital phased arrays. We perform a theoretical analysis of the quantization noise characteristics for an OFDM signal received and processed by a digital phased array, using Gaussian approximation of the OFDM signal. In particular, we quantify the quantization noise suppression factor analytically and numerically. This factor describes how much the coherent combining reduces the quantization noise as a function of the number of antennas, which allows for reducing the ADC bit resolution. For instance in a 8-16 antenna digital phased array the ADC resolution can be reduced with 1-2 bits compared to the ADC required for an analog phased array., Comment: \copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

We consider a cell-free massive MIMO system with multiple antennas on the users and access points. In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pilots. This approach works well when having single-antenna users. In this paper, we show that much higher SEs can be achieved if downlink pilots are sent since the effective channel matrix does not harden when having multi-antenna users. We propose a pilot-based downlink estimation scheme and derive a new SE expression that utilizes zero-forcing combining. We show numerically how the number of users and user antennas affects the SE., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing communication overhead. However, the increased number of FL users may hinder completing large-scale FL over wireless networks due to high imposed latency. Cell-free massive multiple-input multiple-output~(CFmMIMO) is a promising architecture for implementing FL because it serves many users on the same time/frequency resources. While CFmMIMO enhances energy efficiency through spatial multiplexing and collaborative beamforming, it remains crucial to meticulously allocate uplink transmission powers to the FL users. In this paper, we propose an uplink power allocation scheme in FL over CFmMIMO by considering the effect of each user's power on the energy and latency of other users to jointly minimize the users' uplink energy and the latency of FL training. The proposed solution algorithm is based on the coordinate gradient descent method. Numerical results show that our proposed method outperforms the well-known max-sum rate by increasing up to~$27$\% and max-min energy efficiency of the Dinkelbach method by increasing up to~$21$\% in terms of test accuracy while having limited uplink energy and latency budget for FL over CFmMIMO.

Co-channel interference poses a challenge in any wireless communication network where the time-frequency resources are reused over different geographical areas. The interference is particularly diverse in cell-free massive multiple-input multiple-output (MIMO) networks, where a large number of user equipments (UEs) are multiplexed by a multitude of access points (APs) on the same time-frequency resources. For realistic and scalable network operation, only the interference from UEs belonging to the same serving cluster of APs can be estimated in real-time and suppressed by precoding/combining. As a result, the unknown interference arising from scheduling variations in neighboring clusters makes the rate adaptation hard and can lead to outages. This paper aims to model the unknown interference power in the uplink of a cell-free massive MIMO network. The results show that the proposed method effectively describes the distribution of the unknown interference power and provides a tool for rate adaptation with guaranteed target outage., Comment: 6 pages, Accepted at IEEE Wireless Communications and Networking Conference (WCNC), 2024. arXiv admin note: text overlap with arXiv:2305.07344

Following the promising beamforming gains offered by reconfigurable intelligent surfaces (RISs), a new hardware architecture, known as \emph{beyond diagonal RIS (BD-RIS)}, has recently been proposed. This architecture enables controllable signal flows between the RIS elements, thereby providing greater design flexibility. However, the physics-imposed symmetry and orthogonality conditions on the non-diagonal reflection matrix make the design challenging. In this letter, we analyze how a BD-RIS can improve a wideband channel, starting from fundamental principles and deriving the capacity. Our analysis considers the effects of various channel taps and their frequency-domain characteristics. We introduce a new algorithm designed to optimize the configuration of the BD-RIS to maximize wideband capacity. The proposed algorithm has better performance than the benchmarks. A BD-RIS is beneficial compared to a conventional RIS in the absence of static path or when the Rician $\kappa$-factor is smaller than $10$., Comment: 5 pages, 3 figures, submitted as a possible publication

Source localization is the process of estimating the location of signal sources based on the signals received at different antennas of an antenna array. It has diverse applications, ranging from radar systems and underwater acoustics to wireless communication networks. Subspace-based approaches are among the most effective techniques for source localization due to their high accuracy, with Multiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) being two prominent methods in this category. These techniques leverage the fact that the space spanned by the eigenvectors of the covariance matrix of the received signals can be divided into signal and noise subspaces, which are mutually orthogonal. Originally designed for far-field source localization, these methods have undergone several modifications to accommodate near-field scenarios as well. This chapter aims to present the foundations of MUSIC and ESPRIT algorithms and introduce some of their variations for both far-field and near-field localization by a single array of antennas. We further provide numerical examples to demonstrate the performance of the presented methods., Comment: To appear as a chapter of the book "Massive MIMO for Future Wireless Communication Systems: Technology and Applications", to be published by Wiley

Extremely large aperture arrays can enable unprecedented spatial multiplexing in beyond 5G systems due to their extremely narrow beamfocusing capabilities. However, acquiring the spatial correlation matrix to enable efficient channel estimation is a complex task due to the vast number of antenna dimensions. Recently, a new estimation method called the "reduced-subspace least squares (RS-LS) estimator" has been proposed for densely packed arrays. This method relies solely on the geometry of the array to limit the estimation resources. In this paper, we address a gap in the existing literature by deriving the average spectral efficiency for a certain distribution of user equipments (UEs) and a lower bound on it when using the RS-LS estimator. This bound is determined by the channel gain and the statistics of the normalized spatial correlation matrices of potential UEs but, importantly, does not require knowledge of a specific UE's spatial correlation matrix. We establish that there exists a pilot length that maximizes this expression. Additionally, we derive an approximate expression for the optimal pilot length under low signal-to-noise ratio (SNR) conditions. Simulation results validate the tightness of the derived lower bound and the effectiveness of using the optimized pilot length., Comment: Accepted to be presented in IEEE WCNC 2024

In this paper, we investigate how metasurfaces can be deployed to deliver high data rates in a millimeter-wave (mmWave) indoor dense space with many blocking objects. These surfaces can either be static metasurfaces (SMSs) that reflect with fixed phase-shifts or reconfigurable intelligent surfaces (RISs) that can reconfigure their phase-shifts to the currently served user. The latter comes with an increased power, cabling, and signaling cost. To see how reconfigurability affects the network performance, we propose an iterative algorithm based on the feasible point pursuit successive convex approximation method. We jointly optimize the types and phase-shifts of the surfaces and the time portion allocated to each user equipment to maximize the minimum data rate achieved by the network. Our numerical results demonstrate that the minimum data rate improves as more RISs are introduced but the gain diminishes after some point. Therefore, introducing more reconfigurability is not always necessary. Another result shows that to reach the same data rate achieved by using 22 SMSs, at least 18 RISs are needed. This suggests that when it is costly to deploy many RISs, as an inexpensive alternative solution, one can reach the same data rate just by densely deploying more SMSs., Comment: 6 pages, 5 figures, Accepted to be presented in IEEE WCNC 2024

Accurate channel estimation is critical to fully exploit the beamforming gains when communicating with extremely large aperture arrays. The propagation distances between the user and receiver, which potentially has thousands of antennas/elements, are such that they are located in the radiative near-field region of each other when considering the Fraunhofer distance of the entire array. Therefore, it is imperative to consider near-field effects to achieve proper channel estimation. This paper proposes a parametric multi-user near-field channel estimation algorithm based on MUltiple SIgnal Classification (MUSIC) method to obtain the essential parameters describing the users' locations. We derive the estimated channel by incorporating the estimated parameters into the near-field channel model. Additionally, we implement a least-squares-based estimation corrector, resulting in a precise near-field channel estimation. Simulation results demonstrate that our proposed scheme outperforms classical least-squares and minimum mean-square error channel estimation methods in terms of normalized beamforming gain and normalized mean-square error., Comment: Presented in IEEE Asilomar Conference 2023

Extremely large aperture arrays (ELAAs) and reconfigurable intelligent surfaces (RISs) are candidate enablers to realize connectivity goals for the sixth-generation (6G) wireless networks. For instance, ELAAs can provide orders-of-magnitude higher area throughput compared to what massive multiple-input multiple-output (MIMO) can deliver through spatial multiplexing, while RISs can improve the propagation conditions over wireless channels but a passively reflecting RIS must be large to be effective. Active RIS with amplifiers can deal with this issue. In this paper, we study the distortion created by nonlinear amplifiers in both ELAAs and active RIS. We analytically obtain the angular directions and depth of the nonlinear distortion in both near- and far-field channels. The results are demonstrated numerically and we conclude that non-linearities can both create in-band and out-of-band distortion that is beamformed in entirely new directions and distances from the transmitter.

The increasing demand for wireless data transfer has been the driving force behind the widespread adoption of Massive MIMO (multiple-input multiple-output) technology in 5G. The next-generation MIMO technology is now being developed to cater to the new data traffic and performance expectations generated by new user devices and services in the next decade. The evolution towards "ultra-massive MIMO (UM-MIMO)" is not only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights from the communication, electromagnetic (EM), and circuit theory areas. This article offers a comprehensive overview of the key benefits of the UM-MIMO technology and the associated challenges. It explores massive multiplexing facilitated by radiative near-field effects, characterizes the spatial degrees-of-freedom, and practical channel estimation schemes tailored for massive arrays. Moreover, we provide a tutorial on EM theory and circuit theory, and how it is used to obtain physically consistent antenna and channel models. Subsequently, the article describes different ways to implement massive and dense antenna arrays, and how to co-design antennas with signal processing. The main open research challenges are identified at the end., Comment: Submitted to Proceedings of the IEEE, 36 pages, 23 figures

Ultra-massive multiple-input multiple-output (UM-MIMO) is the enabler of Terahertz (THz) communications in next-generation wireless networks. In THz UM-MIMO systems, a new paradigm of cross-field communications spanning from near-field to far-field is emerging, since the near-field range expands with higher frequencies and larger array apertures. Precise beam alignment in cross-field is critical but challenging. Specifically, unlike far-field beams that rely only on the angle domain, the incorporation of dual-domain (angle and distance) training significantly increases overhead. A natural question arises of whether far-field beam training can be deployed for cross-field beam alignment. In this paper, this question is answered, by demonstrating that the far-field training enables sufficient signal-to-noise ratio (SNR) in both far- and near-field scenarios, while exciting all channel dimensions. Based on that, we propose a subarray-coordinated hierarchical (SCH) training with greatly reduced overhead. To further obtain high-precision beam designs, we propose a two-phase angle and distance beam estimator (TPBE). Extensive simulations demonstrate the effectiveness of the proposed methods. Compared to near-field exhaustive search, the SCH possesses 0.2\% training overhead. The TPBE achieves 0.01~degrees and 0.02~m estimation root-mean-squared errors for angle and distance. Furthermore, with the estimated beam directions, a near-optimal SNR with 0.11~dB deviation is attained after beam alignment.

Reconfigurable intelligent surface (RIS) is a newly-emerged technology that might fundamentally change how wireless networks are operated. Though extensively studied in recent years, the practical limitations of RIS are often neglected when assessing the performance of RIS-assisted communication networks. One of these limitations is that each RIS element is restricted to incur a controllable phase shift to the reflected signal from a predefined discrete set. This paper studies an RIS-assisted multi-user multiple-input multiple-output (MIMO) system, where an RIS with discrete phase shifts assists in simultaneous uplink data transmission from multiple user equipments (UEs) to a base station (BS). We aim to maximize the sum rate by optimizing the receive beamforming vectors and RIS phase shift configuration. To this end, we transform the original sum-rate maximization problem into a minimum mean square error (MMSE) minimization problem and employ the block coordinate descent (BCD) technique for iterative optimization of the variables until convergence. We formulate the discrete RIS phase shift optimization problem as a mixed-integer least squares problem and propose a novel method based on sphere decoding (SD) to solve it. Through numerical evaluation, we show that the proposed discrete phase shift design outperforms the conventional nearest point mapping method, which is prevalently used in previous works., Comment: To appear in the Proceedings of IEEE CAMSAP 2023

Future wireless networks must provide ever higher data rates. The available bandwidth increases roughly linearly as we increase the carrier frequency, but the range shrinks drastically. This paper explores if we can instead reach massive capacities using spatial multiplexing over multiple-input multiple-output (MIMO) channels. In line-of-sight (LOS) scenarios, therank of the MIMO channel matrix depends on the polarization and antenna arrangement. We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation. The result is sparely spaced antenna arrays that exploit radiative near-field properties. We further optimize the array geometry for minimum aperture length and aperture area, which leads to different configurations. Moreover, we prove analytically that for fixed-sized arrays, the MIMO rank grows quadratically with the carrier frequency in LOS scenarios, if the antennas are appropriately designed. Hence, MIMO technology contributes more to the capacity growth than the bandwidth. The numerical results show that massive data rates, far beyond 1 Tbps, can be reached both over fixed point-to-point links. It is also possible for a large base station to serve a practically-sized mobile device., Comment: in IEEE Transactions on Wireless Communications

While reconfigurable intelligent surface (RIS)-aided user-specific beamforming has been vastly investigated, the aspect of utilizing RISs for assisting cell-specific transmission has been largely unattended. Aiming to fill this gap, we study a downlink broadcasting scenario where a base station (BS) sends a cell-specific signal to all the users located in a wide angular area with the assistance of a dual-polarized RIS. We utilize the polarization degree of freedom offered by this type of RIS and design the phase configurations in the two polarizations in such a way that the RIS can radiate a broad beam, thereby uniformly covering all azimuth and elevation angles where the users might reside. Specifically, the per-polarization configuration matrices are designed in such a way that the total power-domain array factor becomes spatially flat over all observation angles implying that the RIS can preserve the broad radiation pattern of a single element while boosting its gain proportionally to its aperture size. We validate the mathematical analyses via numerical simulations., Comment: To appear in the Proceedings of Asilomar 2023

We propose a novel resource efficient analog over-the-air (OTA) computation framework to address the demanding requirements of the uplink (UL) fronthaul between the access points (APs) and the central processing unit (CPU) in cell-free massive multiple-input multiple-output (MIMO) systems. We discuss the drawbacks of the wired and wireless fronthaul solutions, and show that our proposed mechanism is efficient and scalable as the number of APs increases. We present the transmit precoding and two-phase power assignment strategies at the APs to coherently combine the signals OTA in a spectrally efficient manner. We derive the statistics of the APs locally available signals which enable us to to obtain the analytical expressions for the Bayesian and classical estimators of the OTA combined signals. We empirically evaluate the normalized mean square error (NMSE), symbol error rate (SER), and the coded bit error rate (BER) of our developed solution and benchmark against the state-of-the-art wired fronthaul based system, Comment: 6 pages, 4 figures, Submitted to IEEE International Conference on Communications (ICC), 2024