Abstracts:Holographic MIMO (HMIMO) is being increasingly recognized as a key enabling technology for 6G wireless systems through the deployment of an extremely large number of antennas within a compact space to fully exploit the potentials of the electromagnetic (EM) channel. Nevertheless, the benefits of HMIMO systems cannot be fully unleashed without an efficient means to estimate the high-dimensional channel, whose distribution becomes increasingly complicated due to the accessibility of the near-field region. In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments. The core idea is to estimate the HMIMO channels solely based on the Stein' s score function of the received pilot signals and an estimated noise level, without relying on priors or supervision that is not feasible in practical deployment. A neural network is trained with the unsupervised denoising score matching objective to learn the parameterized score function. Meanwhile, a principal component analysis (PCA)-based algorithm is proposed to estimate the noise level leveraging the low-rank near-field spatial correlation. Building upon these techniques, we develop a Bayes-optimal score-based channel estimator for fully-digital HMIMO transceivers in a closed form. The optimal score-based estimator is also extended to hybrid analog-digital HMIMO systems by incorporating it into a low-complexity message passing algorithm. The (quasi-) Bayes-optimality of the proposed estimators is validated both in theory and by extensive simulation results. In addition to optimality, it is shown that our proposal is robust to various mismatches and can quickly adapt to dynamic EM environments in an online manner thanks to its unsupervised nature, demonstrating its potential in real-world deployment.

Abstracts:Snapshot Compressive Spectral Imaging Systems (SCSI) compress the scenes by capturing 2D projections of the encoded underlying signals. A decoder, trained with pre-acquired datasets, reconstructs the spectral images. SCSI systems based on diffractive optical elements (DOE) provide a small form factor and the single DOE can be optimized in an end-to-end manner. Since the spectral image is highly compressed in a SCSI system based on a single DOE, the quality of image reconstruction can be insufficient for diverse spectral imaging applications. This work proposes a multishot spectral imaging system employing a double-phase encoding with a double DOE architecture (DoDo), to improve the spectral reconstruction performance. The first DOE is fixed and provides the benefits of the diffractive optical systems. The second DOE provides the variable encoding of the multishot architectures. The work presents a differentiable mathematical model for the multishot DoDo system and optimizes the parameters of the DoDo architecture in an end-to-end manner. The proposed system was tested using simulations and a hardware prototype. To obtain a low-cost system, the implementation uses a deformable mirror for the variable DOE. The proposed DoDo system shows an improvement of up to 4 dB in PSNR in the reconstructed spectral images compared with the single DOE system.

Abstracts:With the deployment of extremely large-scale array (XL-array) operating at the high frequency bands in future wireless systems, integrated sensing and communication (ISAC) is expected to function in the electromagnetic near-field region with a potential distance of hundreds of meters. Also, a wide signal bandwidth is employed to benefit both communication capacity and sensing resolution. However, most existing works assume a far-field narrowband model, which has prohibited their practical applications in future ISAC systems. In this article, we propose a near-field wideband ISAC framework for concurrent multi-user downlink communications and multi-target localization. In particular, the expression of Cramer Rao Bound (CRB) of direction-of-arrival (DOA) and distance estimations for sensing multiple wideband sources is derived, which is minimized subject to the guaranteed communication quality of service (QoS) for each user. Based on the proposed ISAC framework, sparse transceiver array and the precoding matrix are jointly optimized to reduce mutual coupling and system overhead. The problem is relaxed to a convex optimization and solved iteratively. Simulation results demonstrate that the proposed wideband near-field ISAC framework can well support both modalities and that the sparse transceiver improves the sensing accuracy without sacrificing the communication performance.

Abstracts:Phase retrieval (PR) consists of recovering the phase information from captured intensity measurements, known as coded diffraction patterns (CDPs). Non-convex algorithms for addressing the PR problem require a proper initialization that is refined through a gradient descent approach. These PR algorithms have proven to be robust for different scenarios. Despite deep models showing surprising results in this area, these approaches lack interpretability in their neural architectures. This work proposes unrolling the initialization and iterative reconstruction algorithm for the PR problem using the near-field model based on a non-convex formulation; resulting in an interpretable deep neural network (DNN) that can be trained in an end-to-end (E2E) manner. Furthermore, the proposed method can jointly optimize the phase mask for the CDP acquisition and the DNN parameters. Simulation results demonstrate that the proposed E2E method provides high-quality reconstruction using a learned phase mask from a single projection. Also, the proposed method is tested over an experimental optical setup that incorporates the learned phase mask via an only-phase spatial light modulator.

Abstracts:In the context of single-base station (BS) non-line-of-sight (NLoS) single-epoch localization with the aid of a reflective reconfigurable intelligent surface (RIS), this paper introduces a novel three-step algorithm that jointly estimates the position and velocity of a mobile user equipment (UE), while compensating for the Doppler effects observed in near-field (NF) at the RIS elements over the short transmission duration of a sequence of downlink (DL) pilot symbols. First, a low-complexity initialization procedure is proposed, relying in part on far-field (FF) approximation and a static user assumption. Then, an alternating optimization procedure is designed to iteratively refine the velocity and position estimates, as well as the channel gain. The refinement routines leverage small angle approximations and the linearization of the RIS response, accounting for both NF and mobility effects. We evaluate the performance of the proposed algorithm through extensive simulations under diverse operating conditions with regard to signal-to-noise ratio (SNR), UE mobility, uncontrolled multipath and RIS-UE distance. Our results reveal remarkable performance improvements over the state-of-the-art (SoTA) mobility-agnostic benchmark algorithm, while indicating convergence of the proposed algorithm to respective theoretical bounds on position and velocity estimation.

Abstracts:As technologies envisioned for next-generation wireless networks significantly extend the near-field region, it is of interest to reevaluate integrated sensing and communications (ISAC) with an appropriate channel model to account for the effects introduced by the near field. In this article, a near-field ISAC framework is proposed for both downlink and uplink scenarios based on such a channel model. We consider a base station equipped with a uniform planar array, and the impacts of the effective aperture and polarization of antennas are considered. For the downlink case, three distinct designs are studied: a communications-centric (C-C) design, a sensing-centric (S-C) design, and a Pareto optimal design. Regarding the uplink case, the C-C design, the S-C design and a time-sharing strategy are considered. Within each design, sensing rates (SRs) and communication rates (CRs) are derived. To gain further insights, high signal-to-noise ratio slopes and rate scaling laws concerning the number of antennas are examined. The attainable near-field SR-CR regions of ISAC and the baseline frequency-division S&C are also characterized. Numerical results reveal that, as the number of antennas in the array grows, the SRs and CRs under our model converge to finite values, while those under conventional far- and near-field models exhibit unbounded growth, highlighting the importance of precise channel modeling for near-field ISAC.

Abstracts:This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth. Specifically, we model spatial-frequency domain channels in the beam-delay domain to explore inter-antenna and inter-subcarrier correlations. Within this model, the frequency-dependent hybrid-field beam domain steering vectors are introduced to describe the near-field and beam-squint effects. The independent and non-identically distributed Bernoulli-Gaussian models with unknown prior hyperparameters are employed to capture the sparsity in the beam-delay domain, posing a challenge for channel estimation. Under the constrained Bethe free energy minimization framework, we design different structures and constraints on trial beliefs to develop hybrid message passing (HMP) algorithms, thus achieving efficient joint estimation of beam-delay domain channel and prior hyperparameters. To further improve the model accuracy, the multidimensional grid point perturbation (MDGPP)-based representation is presented, which assigns individual perturbation parameters to each multidimensional discrete grid. By treating the MDGPP parameters as unknown hyperparameters, we propose the two-stage HMP algorithm for MDGPP-based channel estimation, where the output of the initial stage is pruned for the refinement stage to reduce the computational complexity. Numerical simulations demonstrate the significant superiority of the proposed algorithm over benchmarks with both near-field and beam-squint effects.

Abstracts:This paper considers near-field multiuser communications based on sparse arrays (SAs). First, for the uniform SAs (USAs), we analyze the beam gains of channel steering vectors, which shows that increasing the antenna spacings can effectively improve the spatial resolution of the antenna arrays to enhance the sum rate of multiuser communications. Then, we investigate nonuniform SAs (NSAs) to mitigate the high multiuser interference from the grating lobes of the USAs. To maximize the sum rate of near-field multiuser communications, we optimize the antenna positions of the NSAs, where a successive convex approximation-based antenna position optimization algorithm is proposed. Moreover, we find that the channels of both the USAs and the NSAs show uniform sparsity in the defined surrogate distance-angle (SD-A) domain. Based on the channel sparsity, an on-grid SD-A-domain orthogonal matching pursuit (SDA-OMP) algorithm is developed to estimate multiuser channels. To further improve the resolution of the SDA-OMP, we also design an off-grid SD-A-domain iterative super-resolution channel estimation algorithm. Simulation results demonstrate the superior performance of the proposed methods.

Abstracts:Reconfigurable intelligent surfaces (RISs) are proposed to control complex wireless environments in next-generation networks. In particular, wideband RISs can play a key role in high-accuracy location awareness, which calls for models that consider the frequency-selectivity of metasurfaces. This paper presents a general signal model for wideband systems with RISs and establishes a Fisher information analysis to determine the theoretical limits of wideband localization with RISs. In addition, synthetic RISs are proposed to mitigate the multiplicative fading effect caused by the scattering property of RISs. Special scenarios including complete coupling and complete decoupling are further investigated. Results show that with the proposed models, a wideband RIS with a polynomial phase response per element provides more position information than those with more degrees of freedom (DOFs) in piecewise-constant phase response per element. Furthermore, velocity-induced information allows a dynamic RIS to provide more position information than a static RIS. Additionally, a dynamic RIS can be synthesized through multiple measurements to outperform a large one.