paper-with-me

Papers

Resource allocation exploiting reflective surfaces to minimize the outage probability in VLC

2024-01-29 · Borja Genoves Guzman, Maximo Morales Cespedes, Victor P. Gil Jimenez, Ana Garcia Armada, Maite Brandt-Pearce

Visible light communication (VLC) is a technology that complements radio frequency (RF) to fulfill the ever-increasing demand for wireless data traffic. The ubiquity of light-emitting diodes (LEDs), exploited as transmitters, increases the VLC market penetration and positions it as one of the most promising technologies to alleviate the spectrum scarcity of RF. However, VLC deployment is hindered by blockage causing connectivity outages in the presence of obstacles. Recently, optical reconfigurable intelligent surfaces (ORISs) have been considered to mitigate this problem. While prior works exploit ORISs for data or secrecy rate maximization, this paper studies the optimal placement of mirrors and ORISs, and the LED power allocation, for jointly minimizing the outage probability while keeping the lighting standards. We describe an optimal outage minimization framework and present solvable heuristics. We provide extensive numerical results and show that the use of ORISs may reduce the outage probability by up to 67% with respect to a no-mirror scenario and provide a gain of hundreds of kbit/J in optical energy efficiency with respect to the presented benchmark.

📄 PDF Abstract BibTeX arXiv:2401.16627

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Unsupervised Learning based Element Resource Allocation for Reconfigurable Intelligent Surfaces in mmWave Network

2025-09-03 · Pujitha Mamillapalli, Yoghitha Ramamoorthi, Abhinav Kumar, Tomoki Murakami 외 arxiv

The increasing demand for high data rates and seamless connectivity in wireless systems has sparked significant interest in reconfigurable intelligent surfaces (RIS) and artificial intelligence-based wireless application…

3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces

2023-01-01 · ICCV 2023 1 · Xuepeng Shi, Georgi Dikov, Gerhard Reitmayr, Tae-Kyun Kim 외

Self-supervised monocular depth estimation (SSMDE) aims at predicting the dense depth maps of monocular images, by learning to minimize a photometric loss using spatially neighboring image pairs during training. Whil…

Depth EstimationMonocular Depth Estimation

UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections

2023-12-20 · Fangjinhua Wang, Marie-Julie Rakotosaona, Michael Niemeyer, Richard Szeliski 외

Neural 3D scene representations have shown great potential for 3D reconstruction from 2D images. However, reconstructing real-world captures of complex scenes still remains a challenge. Existing generic 3D reconstruction…

3D ReconstructionNeRF

Power-Aperture Resource Allocation for a MPAR with Communications Capabilities

2023-07-06 · Augusto Aubry, Antonio De Maio, Luca Pallotta

Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire contr…

QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach

2019-06-20 · Fatima Haouari, Emna Baccour, Aiman Erbad, Amr Mohamed 외

Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewer…

BIG-bench Machine Learning