Cramér-Rao Bounds for Holographic Positioning

D’Amico, Antonio A.; Torres, Andrea de Jesus; Sanguinetti, Luca; Win, Moe Z.

doi:10.1109/tsp.2022.3222102

Cited by 14 publications

(20 citation statements)

References 29 publications

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“…The propagation characteristics of the channel differ in the near-field and farfield regions, whose boundary is determined by the Rayleigh distance d Rayleigh ≜ 2[(N −1)da] 2 λc , where N refers to the number of antennas. Due to the joint effect of the array aperture and the carrier wavelength, the near-field region is non-negligible in HMIMO systems, which, compared to the traditional far-field setup, is a paradigm shift that brings new degrees-of-freedom (DoFs) for both communications and sensing [10].…”

Section: Hmimo Channel and System Models A Channel Model For Near-fie...mentioning

confidence: 99%

“…In this subsection, we first focus on the fully-digital system model, i.e., (10). Extension to hybrid analog-digital HMIMO systems will be presented in Section IV-B.…”

Section: A Bridging Mmse Estimation With the Score Functionmentioning

confidence: 99%

“…For σe (t) , we propose two possible methods to estimate it. First, since r (t) = h+e (t) is similar to the fullydigital system model, i.e., (10), we could apply the PCA-based noise level estimator to r (t) to get an estimate of σe (t) in each iteration. However, this method requires running Algorithm 2 once per iteration, and incurs a high computational complexity.…”

Section: B Score-based Estimator In Hybrid Analog-digital Systemsmentioning

confidence: 99%

“…3) Designing the NLE: Based on (22), r (t) in each iteration is the ground-truth channel corrupted by white Gaussian noise e (t) ∼ N (0, σ 2 e (t) I), which is similar to the formulation in fully-digital systems, i.e., (10). After treating r (t) as y and σe (t) as σn in Algorithm 2, the NLE is the same as the scorebased estimator in IV-A, given by…”

Section: B Score-based Estimator In Hybrid Analog-digital Systemsmentioning

confidence: 99%

“…With an ultra-massive number of antennas closely packed in a compact space, holographic MIMO (HMIMO) is envisioned as a promising next-generation multi-antenna technology to enable an unprecedentedly high degree of spectral and energy efficiency [2]. Extensive research efforts have been dedicated to many different aspects of HMIMO systems, such as channel measurement and modeling [2], [3], [4], [5], [6], beamforming methods [7], [8], [9], positioning and sensing [10], [11], and integrated sensing and communications [12], etc. Nevertheless, one fundamental problem is that, the promised gains brought A preliminary version of this work was submitted for possible publication at the IEEE International Conference on Communications (ICC), Denver, CO, USA, Jun.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks

Shen

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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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-) Bayesoptimality 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.

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Section: Hmimo Channel and System Models A Channel Model For Near-fie...mentioning

confidence: 99%

“…In this subsection, we first focus on the fully-digital system model, i.e., (10). Extension to hybrid analog-digital HMIMO systems will be presented in Section IV-B.…”

Section: A Bridging Mmse Estimation With the Score Functionmentioning

confidence: 99%

Section: B Score-based Estimator In Hybrid Analog-digital Systemsmentioning

confidence: 99%

Section: B Score-based Estimator In Hybrid Analog-digital Systemsmentioning

confidence: 99%