Achieving Beamfocusing via Two Separated Uniform Linear Arrays

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.

Approaching Maximum Likelihood Performance via End-to-End Learning in MU-MIMO Systems

Multi-user multiple-input multiple-output (MU-MIMO) systems allow multiple users to share the same wireless spectrum. Each user transmits one symbol drawn from an M-ary quadrature amplitude modulation (M-QAM) constellation set, and the resulting multi-user interference (MUI) is cancelled at the receiver. End-to-End (E2E) learning has recently been proposed to jointly design the constellation set for a modulator and symbol detector for a single user under an additive white Gaussian noise (AWGN) channel by using deep neural networks (DNN) with a symbol-error-rate (SER) performance, exceeding that of Maximum likelihood (ML) M-QAM detectors. In this paper, we extend the E2E concept to the MU-MIMO systems, where a DNN-based modulator that generates learned M constellation points and graph expectation propagation network (GEPNet) detector that cancels MUI are jointly optimised with respect to SER performance loss. Simulation results demonstrate that the proposed E2E with learned constellation outperforms GEPNet with 16-QAM by around 5 dB in terms of SER in a high MUI environment and even surpasses ML with 16-QAM in a low MUI condition, both with no additional computational complexity.

An Efficient Modified MUSIC Algorithm for RIS-Assisted Near-Field Localization

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.

Symbol-Level Precoding for Near-Field ISAC

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.

The Roles of Spatial Frequencies and Degrees-of-Freedom in Near-Field Communications

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.

Enabling 6G Performance in the Upper Mid-Band Through Gigantic MIMO

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. We also identify five open research challenges that must be overcome to efficiently use gigantic MIMO dimensions in 6G from hardware, cost, and algorithmic perspectives.

Spatial Correlation Modeling and RS-LS Estimation of Near-Field Channels with Uniform Planar Arrays

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.

Accurate Direct Positioning in Distributed MIMO Using Delay-Doppler Channel Measurements

Distributed multiple-input multiple-output (D-MIMO) is a promising technology for simultaneous communication and positioning. However, phase synchronization between multiple access points in D-MIMO is challenging, which requires methods that function without the need for phase synchronization. We therefore present a method for D-MIMO that performs direct positioning of a moving device based on the delay-Doppler characteristics of the channel state information (CSI). Our method relies on particle-filter-based Bayesian inference with a state-space model. We use recent measurements from a sub-6 GHz D-MIMO OFDM system in an industrial environment to demonstrate centimeter accuracy under partial line-of-sight (LoS) conditions and decimeter accuracy under full non-LoS.

Parametric Near-Field Channel Estimation for Extremely Large Aperture Arrays

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.

Nonlinear Distortion Radiated from Large Arrays and Active Reconfigurable Intelligent Surfaces

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.