Near-Field RSS-Based Localization Algorithms Using Reconfigurable Intelligent Surface

Huang, S.-Y.; Wang, Bo; Zhao, Yi; Luan, Mingan

doi:10.1109/jsen.2022.3141386

Cited by 37 publications

(15 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They further refined this work and proposed an integrated localization system in [ 31 ], realizing centimeter-level accuracy. Huang et al [ 33 ] also picked the best available RIS codeword, but simply for signal strength improvement, which indirectly enhances localization performance. However, the prerequisite for these methods is the RIS is deployed solely for localization, rather than more important tasks such as enlarging the signal coverage and increasing the network throughput [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the fast-growing complexity of these methods leads to long inference time [ 26 ], which severely limits their usability in real-world deployments. Considering these drawbacks, other solutions measure the RSSI from known BSs for data-driven fingerprint-based localization [ 27 , 28 , 29 , 30 ] in RIS-enhanced environments [ 31 , 32 , 33 ], as the fingerprints are much easier to collect than field measurements [ 34 ] and are far simpler than CV channel matrices [ 33 , 35 ]. However, these methods usually involve cooperation with optimized RIS codewords to provide high-accuracy localization, which unrealistically assumes that the RIS is solely deployed for localization rather than more pressing demands in modern high-frequency wireless networks, such as enlarging the signal coverage and increasing the overall network throughput [ 8 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Codeword-Independent Localization Technique for Reconfigurable Intelligent Surface Enhanced Environments Using Adversarial Learning

Luo

Meratnia

2023

Sensors

View full text Add to dashboard Cite

Reconfigurable Intelligent Surfaces (RISs) not only enable software-defined radio in modern wireless communication networks but also have the potential to be utilized for localization. Most previous works used channel matrices to calculate locations, requiring extensive field measurements, which leads to rapidly growing complexity. Although a few studies have designed fingerprint-based systems, they are only feasible under an unrealistic assumption that the RIS will be deployed only for localization purposes. Additionally, all these methods utilize RIS codewords for location inference, inducing considerable communication burdens. In this paper, we propose a new localization technique for RIS-enhanced environments that does not require RIS codewords for online location inference. Our proposed approach extracts codeword-independent representations of fingerprints using a domain adversarial neural network. We evaluated our solution using the DeepMIMO dataset. Due to the lack of results from other studies, for fair comparisons, we define oracle and baseline cases, which are the theoretical upper and lower bounds of our system, respectively. In all experiments, our proposed solution performed much more similarly to the oracle cases than the baseline cases, demonstrating the effectiveness and robustness of our method.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Codeword-Independent Localization Technique for Reconfigurable Intelligent Surface Enhanced Environments Using Adversarial Learning

Luo

Meratnia

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, the concept of Large Intelligent Surface (LIS) [2]- [4] arises as an evolution of massive multi-input multi-output (MIMO) system, which shows substantial gains in performances of signal-transmission and terminal-positioning. Later on, similar concepts such as reflection intelligent surface (RIS), intelligent reflection surface (IRS), holographic MIMO [5]- [8] and others have also been introduced and gained extensive interests from researchers in different areas.…”

Section: Introductionmentioning

confidence: 99%

Design of Near-Field Beamforming for Large Intelligent Surfaces

Hu¹,

Wang²,

ilter³

2023

Preprint

View full text Add to dashboard Cite

<p> In this paper, we propose a novel near-field beamforming (BF) design for a Large Intelligent Surface (LIS) implemented as a discretized two-dimensional (2D) antenna array. We firstly investigate the definitions of near-field and far-field regions of the LIS, and determine the Fraunhofer distance which scales up linearly in the surface-area. Then, we derive the Fresnel near-field region where variations of amplitudes and angles are both negligible, as long as the distance from a user-equipment (UE) to the LIS is larger than a threshold that scales up linearly in the diameter of the LIS. Hence, in the majority region of near-field, only phase variations worsen the quality of received signal and yield significant losses of array gains. Following this observation, we present a decomposition theorem proving that in the Fresnel region, the optimal three-dimensional (3D) near-field BF can be decomposed into a conventional 2D far-field BF and a one-dimensional (1D) near-field BF. The 2D far-field BF compensates phase variations caused by the azimuth and elevation angles, while the 1D near-field BF compensates phases variations caused by distance differences among the UE and antenna-elements on the LIS. Such a 2D+1D BF design is fully compatible with existing 2D far-field BF schemes, and the 1D near-field BF can be treated as an additional step. From this perspective, we further derive an optimal codebook design of the 1D near-field BF with Lloyd-Max method. Numerical results show that the proposed 2D+1D BF is effective to recover losses of array gains in the near-field, and also robust against distance mismatches. Moreover, with a few optimized quantization points in the codebook, the 1D near-field BF performs close to optimal. </p>

show abstract

“…Recently, the concept of Large Intelligent Surface (LIS) [2]- [4], as an evolution of massive multi-input multi-output (MIMO) system, arises in wireless system design, which introduces substantial gains in signal-transmission and terminal-positioning. Later on, similar concepts including reflection intelligent surface (RIS), intelligent reflection surface (IRS), and holographic MIMO [5]- [8] have also been introduced and gain extensive interests.…”

Section: Introductionmentioning

confidence: 99%

Near-Field Beamforming for Large Intelligent Surfaces

Ilter

Wang

2022

2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

View full text Add to dashboard Cite

In this paper, we propose a novel near-field beamforming (BF) design with a Large Intelligent Surface (LIS) that is implemented as a discretized 2D-array. We first investigate the definitions of the near-field and far-field regions, and determine the Fraunhofer distance of the LIS, which scales up linearly in the surface-area of the LIS. Hence, a user-equipment (UE) can enter the near-field of a LIS in practice. In addition to Fraunhofer distance, we further derive the Fresnel near-field region where both amplitude and angle variations are negligible, as long as the distance from the UE to the LIS is larger than a threshold, which only scales up linearly in the diameter of LIS. Therefore, in the majority region of near-field, only phase variations worsen the the quality of received signal and result in significant array-gain losses. Motivated by this observation, we further propose a two-step near-field BF design that can effectively recover the array-gain losses in Fresnel near-field, and is fully compatible with a conventional far-field BF.

show abstract

Near-Field RSS-Based Localization Algorithms Using Reconfigurable Intelligent Surface

Cited by 37 publications

References 51 publications

A Codeword-Independent Localization Technique for Reconfigurable Intelligent Surface Enhanced Environments Using Adversarial Learning

A Codeword-Independent Localization Technique for Reconfigurable Intelligent Surface Enhanced Environments Using Adversarial Learning

Design of Near-Field Beamforming for Large Intelligent Surfaces

Near-Field Beamforming for Large Intelligent Surfaces

Contact Info

Product

Resources

About