A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks

Miranda, Rodrigo Fuchs; Barriquello, Carlos Henrique; Reguera, Vitalio Alfonso; Denardin, Gustavo Weber; Thomas, Djeisson Hoffmann; Loose, Felipe; Amaral, Leonardo Saldanha

doi:10.3390/s23187815

Cited by 7 publications

(8 citation statements)

References 103 publications

(143 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In essence, this contribution represents an advance because it offers a satisfying and rare combination of desirable characteristics. For example, a large bandwidth (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), gain greater than 30 dB, common-mode rejection ratio (CMRR) approximately equal to 40 dB, and noise levels approximately equal to 1.0 dB. Here, the noise figure in differential mode measured "on jig" and using the OMMIC foundry is less than 1.4 dB.…”

Section: Introductionmentioning

confidence: 91%

“…In addition, this high sensitivity is essential in electromedicine and, to a lesser extent, in certain applications in the industrial sector. It is notable how wireless sensor networks (WSN) have promoted great attention due to their versatility and uses in various sectors, such as healthcare, military, industrial automation, and urban intelligence [1][2][3][4][5][6]. In all these cases, receivers must present extremely low figures of merit, which implies using the most innovative technologies to achieve that objective [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Differential BroadBand (1–16 GHz) MMIC GaAs mHEMT Low-Noise Amplifier for Radio Astronomy Applications and Sensing

Jimenez-Martin,

Gonzalez-Posadas,

Parra-Cerrada

et al. 2024

Sensors

View full text Add to dashboard Cite

A broadband differential-MMIC low-noise amplifier (DLNA) using metamorphic high-electron-mobility transistors of 70 nm in Gallium Arsenide (70 nm GaAs mHEMT technology) is presented. The design and results of the performance measurements of the DLNA in the frequency band from 1 to 16 GHz are shown, with a high dynamic range, and a noise figure (NF) below 1.3 dB is obtained. In this work, two low-noise amplifiers (LNAs) were designed and manufactured in the OMMIC foundry: a dual LNA, which we call balanced, and a differential LNA, which we call DLNA. However, the paper focuses primarily on DLNA because of its differential architecture. Both use a 70 nm GaAs mHEMT space-qualified technology with a cutoff frequency of 300 GHz. With a low power bias Vbias/Ibias (5 V/40.5 mA), NF < 1.07 dB “on wafer” was achieved, from 2 to 16 GHz; while with the measurements made “on jig”, NF = 1.1 dB, from 1 to 10 GHz. Furthermore, it was obtained that NF < 1.5 dB, from 1 to 16 GHz, with a figure of merit equal to 145.5 GHz/mW. Finally, with the proposed topology, several LNAs were designed and manufactured, both in the OMMIC process and in other foundries with other processes, such as UMS. The experimental results showed that the NF of the DLNA MMIC with multioctave bandwidth that was built in the frequency range of the L-, S-, C-, and X-bands was satisfactory.

show abstract

Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Differential BroadBand (1–16 GHz) MMIC GaAs mHEMT Low-Noise Amplifier for Radio Astronomy Applications and Sensing

Jimenez-Martin,

Gonzalez-Posadas,

Parra-Cerrada

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…This approach offers a framework for femtocells to dynamically adjust power levels, considering the potential actions and reactions of neighboring cells, thereby minimizing interference and maximizing SINR [26]. Additionally, there are several works from [27], [28] propose a dynamic algorithm based on stochastic optimization principles, adeptly adjusting power levels in response to variations in network traffic and interference. The integration of game theory into power control strategies represents a paradigm shift, enabling more sophisticated and effective management of femtocell power to improve network efficiency and user satisfaction.…”

Section: Literature Surveymentioning

confidence: 99%

Optimizing Femtocell Networks: A Novel Game Theory Based Power Management Model for Enhanced SINR and Energy Efficiency

Airlangga,

Sihombing,

Bata

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This research presents a novel game theory based model for femtocell power management, engineered to significantly enhance the Signal-to-Interference-plus-Noise Ratio (SINR) while optimizing energy consumption across wireless communication networks. Femtocells, as a solution to the increasing demand for high-quality indoor network coverage, face challenges in power management and interference mitigation. Our proposed model addresses these issues, providing a sophisticated algorithmic approach that ensures high SINR levels without a proportional increase in power usage. Through a series of simulations, the model's performance was evaluated against existing power management techniques. The results, delineated across several tables, revealed that the proposed model consistently achieved and often surpassed targeted SINR levels with modest power consumption increments, even at high targets. Notably, at a SINR target of 20, the model sustained a high SINR of 23.62 while maintaining a reasonable energy profile. Additionally, the model exhibited exceptional operational efficiency, characterized by low execution times and rapid convergence rates, across varying network conditions. This responsiveness is essential for adapting to user mobility and varying traffic patterns, particularly in dense urban settings and during peak usage periods.

show abstract

“…In recent years, with the rapid development of wireless communication technology and mobile internet, wireless spectrum resources have been demanded increasingly, and the available frequency bands have been allocated. Therefore, it is particularly important to improve the efficiency of spectrum dynamic utilization [ 1 ]. Cognitive Radio (CR) is considered one of the solutions to solve the contradiction between spectrum supply and demand [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…As previously mentioned, traditional methods have difficulty handling wireless communication networks from the perspective of graph structure modeling. However, graph representation learning methods based on GNN can effectively mine the structural and feature information in graph data [ 1 ], to capture the structural and dependency relationships between nodes. This approach enables a more precise modeling of the topology of wireless networks more accurately.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression

Wen,

Fang,

Fan

2024

Sensors

View full text Add to dashboard Cite

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model’s ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.

show abstract

A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks

Cited by 7 publications

References 103 publications

Differential BroadBand (1–16 GHz) MMIC GaAs mHEMT Low-Noise Amplifier for Radio Astronomy Applications and Sensing

Differential BroadBand (1–16 GHz) MMIC GaAs mHEMT Low-Noise Amplifier for Radio Astronomy Applications and Sensing

Optimizing Femtocell Networks: A Novel Game Theory Based Power Management Model for Enhanced SINR and Energy Efficiency

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression

Contact Info

Product

Resources

About