Reconfigurable Intelligent Surface-Assisted Bluetooth Low Energy Link in Metal Enclosure

Lodro, Mir; Gradoni, Gabriele; Gros, Jean-Baptiste; Greedy, Steve; Lerosey, Geoffroy

doi:10.3389/frcmn.2021.733637

Cited by 6 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PlutoSDR is a cost-effective SDR based on AD9363 RFIC and can perform transmit and receive operations with a minimum sampling rate of 65.1 ksps. We have used PlutoSDR in our previous works for testing the wireless communication systems in metal enclosures [13] and for the reconfigurable intelligent surface-assisted modulation classification in the indoor environment [14]. PlutoSDRs were used to measure the real-time data from the set of trusted WLAN routers on the same floor and across the floor.…”

Section: Methodsmentioning

confidence: 99%

Experimental Evaluation of a Wireless Local Area Network (WLAN) Impersonator Detection System Using Deep Learning

2023

RSL

View full text Add to dashboard Cite

We present an experimental evaluation of a radio impersonator detection system using deep learning in an indoor environment. RF fingerprinting is used to detect a wireless local area network (WLAN) impersonator that transmits beacon frames to WLAN users with a known medium access control address. The RF impersonator detection was performed by training a neural network with real-time beacon frame data captured from the trusted routers in a multifloor building in a campus environment. In addition to the beacon frames from the trusted routers, the beacon frames were captured from an unknown router at five different locations. The neural network was trained using RF fingerprinting from the trusted routers and the unknown router. The confusion matrices about the classification of the trusted routers and the unknown router are presented. Finally, a software-defined radio (SDR) over-the-air (OTA) test was performed in the same multifloor indoor environment to detect router impersonator. Our SDR-based approach shows that the trained neural network can classify the WLAN router impersonator with high accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Experimental Evaluation of a Wireless Local Area Network (WLAN) Impersonator Detection System Using Deep Learning

2023

RSL

View full text Add to dashboard Cite

show abstract

“…Unlike the usual wireless interference, which is additive, this can be seen as a multiplicative interference that needs to be handled carefully. Examples of interference management via a Software Defined Radio (SDR) setup with two RISs, targeting channel hardening of the received signals at UE terminals have been lately presented [15], [16], [17]. The characterization of the capacity limits in multi-RISempowered wireless communications, and the practical schemes to achieve it, constitutes also an active area of research [18], [19].…”

Section: A Connectivity and Reliabilitymentioning

confidence: 99%

RIS-Enabled Smart Wireless Environments: Deployment Scenarios, Network Architecture, Bandwidth and Area of Influence

Alexandropoulos¹,

Phan-Huy²,

Katsanos³

et al. 2023

Preprint

View full text Add to dashboard Cite

Reconfigurable Intelligent Surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments, and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localization, and sustainability requirements of next generation wireless networks. In this paper, we first present the deployment scenarios for RISenabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardized interfaces. We identify various RIS deployment strategies and sketch the core architectural

show abstract

“…The aforementioned state-of-the-art works provided only numerical simulations but not experimental evaluations. Some works made efforts on experimental evaluations of the RIS optimization in wireless communications, e.g., multiple links in indoor environment [9] and millimeter wave beamforming [10].…”

Section: Introductionmentioning

confidence: 99%

Model-Free Optimization and Experimental Validation of RIS-Assisted Wireless Communications Under Rich Multipath Fading

Chen,

You,

Zhang

et al. 2024

IEEE Wireless Commun. Lett.

View full text Add to dashboard Cite

Reconfigurable intelligent surface (RIS) devices have emerged as an effective way to control the propagation channels for enhancing the end-users' performance. However, RIS optimization involves configuring the radio frequency response of a large number of radiating elements, which is challenging in realworld applications due to high computational complexity. In this paper, a model-free cross-entropy (CE) algorithm is proposed to optimize the binary RIS configuration for improving the signalto-noise ratio (SNR) at the receiver. One key advantage of the proposed method is that it only requires system performance indicators, e.g., the received SNR, without the need for channel models or channel state information. Both simulations and experiments are conducted to evaluate the performance of the proposed CE algorithm. This study provides an experimental demonstration of the channel hardening effect in a multi-antenna RIS-assisted wireless system under rich multipath fading.

show abstract

Reconfigurable Intelligent Surface-Assisted Bluetooth Low Energy Link in Metal Enclosure

Cited by 6 publications

References 26 publications

Experimental Evaluation of a Wireless Local Area Network (WLAN) Impersonator Detection System Using Deep Learning

Experimental Evaluation of a Wireless Local Area Network (WLAN) Impersonator Detection System Using Deep Learning

RIS-Enabled Smart Wireless Environments: Deployment Scenarios, Network Architecture, Bandwidth and Area of Influence

Model-Free Optimization and Experimental Validation of RIS-Assisted Wireless Communications Under Rich Multipath Fading

Contact Info

Product

Resources

About