Radio Localization and Sensing—Part I: Fundamentals

Wymeersch, Henk

doi:10.1109/lcomm.2022.3206821

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…3. The BS is at a fixed position, namely at p TX = [30,15,2] m, and it has a uniform rectangular array (URA) with 8 × 2 antennas in the 11 The first assumption is reasonable because in the near-field region, the distance between RIS and UE is small and the LOS probability is close to 1 [51]. As for the second assumption, uncorrelated scattering is a reasonable model without well-established channel models to characterize the RIS-UE channel.…”

Section: Resultsmentioning

confidence: 99%

“…For the motion of the UE, to emulate a movement pattern compatible with a typical random pedestrian motion model, we set σ 2 a,x = σ 2 a,y = 0.5 m 2 /s 3 , σ 2 a,z = 0 m 2 /s 3 . In all simulations, it is assumed that the initial state of the node (i.e., its position and velocity) is known, i.e., m 0 = s 0 and Σ 0 = P. There are three RISs in the environment located at [0, 15,3] m, [5,0,3] m, [10,30,3] m, if not otherwise indicated. They consist of a URA of 80 × 5 elements lying on the Y Z-plane, i.e., they are mainly used in the horizontal direction as long strips.…”

Section: Resultsmentioning

confidence: 99%

“…Two-step approaches are generally suboptimal compared to direct methods that start from the received signal and avoid intermediate processing steps [13]. In addition, modeling the measurement noise in the two-step approach could be a difficult task due to the nonlinear operations, which is avoided when starting directly from the received signals whose statistics are known [15], [16]. Therefore, in our narrowband method, we opt for a direct approach, where the range and angle information is implicitly accounted for by processing the received signals.…”

Section: Bayesian Localization: User Trackingmentioning

confidence: 99%

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Two-Timescale Joint Precoding Design and RIS Optimization for User Tracking in Near-Field MIMO Systems

Palmucci

Guerra

Abrardo

et al. 2023

IEEE Trans. Signal Process.

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In this paper, we propose a novel framework that aims to jointly design the reflection coefficients of multiple reconfigurable intelligent surfaces (RISs) and the precoding strategy of a single base station (BS) to optimize the selftracking of the position and the velocity of a single multi-antenna user equipment (UE) that moves either in the far-or nearfield region. Differently from the literature, and to keep the overall complexity affordable, we assume that RIS optimization is performed less frequently than localization and precoding adaptation. The proposed procedure leads to minimize the inverse of the received power in the UE position uncertainty area between two subsequent optimization steps.The optimal RIS and precoder strategy is compared with the classic beam focusing strategy and with a scheme that maximizes the communication rate. It is shown that if the RISs are optimized for communications, their configuration is suboptimal when used for tracking purposes. Numerical results show that in typical indoor environments with only one BS and a few RISs operating on millimeter waves, high location accuracy in the range of less than half a meter can be achieved.Index terms-Reconfigurable Intelligent Surfaces, Bayesian tracking, MIMO, Optimization.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Bayesian Localization: User Trackingmentioning

confidence: 99%

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Two-Timescale Joint Precoding Design and RIS Optimization for User Tracking in Near-Field MIMO Systems

Palmucci

Guerra

Abrardo

et al. 2023

IEEE Trans. Signal Process.

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“…The subcarrier spacing is denoted by ∆f . Although it is challenging to argue the resolvability of multiple paths in lower frequencies, and hence their relationship to the geometry of the environment, in mmWave and beyond, there is a stronger relation between the individual received paths and the geometric information of the channel [7], [37]. Finally, assuming a uniform planar array with antenna elements along x− and z−axis, the expression for the array response vector for the elaborated example at the receiver can be written as a (ϕ az , ϕ el ) = a z (ϕ el ) ⊗ a x (ϕ az , ϕ el ).…”

Section: A Channel Modelmentioning

confidence: 99%

“…Current deployment of massive multiple-input multiple-output (MIMO) technology, a foreseen adoption of reconfigurable intelligent surfaces (RIS), and tenacious exploration of higher bandwidths in millimeter waves and beyond aid to improve angular-and time-granularity of signal information [7]. This facilitates closing the gap between the aforementioned QoS requirements of different use cases and the performance provided by wireless communication and localization.…”

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confidence: 99%

Self-Supervised and Invariant Representations for Wireless Localization

Salihu¹,

Schwarz²,

Rupp³

2023

Preprint

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In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned representations are easily transferable to new environments and ready to use for other wireless downstream tasks. To the best of our knowledge, the proposed method is the first joint-embedding self-supervised approach to forsake the dependency on contrastive channel estimates. Our approach outperforms fully-supervised techniques in small data regimes under fine-tuning and, in some cases, linear evaluation. We assess the performance in centralized and distributed massive MIMO systems for multiple datasets. Moreover, our method works indoors and outdoors without additional assumptions or design changes.

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