Visible light backscattering with applications to communication and localization in healthcare: A survey

Ullah, Muhammad Habib; Gelli, Giacinto; Verde, Francesco

doi:10.1016/j.procs.2022.07.111

Cited by 9 publications

(1 citation statement)

References 27 publications

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“…These research and implementation works use VLC for downlink communication, while infrared (IR) was used for uplink communication. Furthermore, in the literature, there are few works related to VLC applied to the localization of medical equipment in hospitals [25,26]. On the other hand, in [27], the authors focus mainly on simultaneous positioning and orientation using VLC.…”

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confidence: 99%

Performance Analysis of the Particle Swarm Optimization Algorithm in a VLC System for Localization in Hospital Environments

Candia,

Játiva,

Azurdia Meza

et al. 2024

Applied Sciences

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Localization in hospitals can be valuable in improving different services in medical environments. In this sense, an accurate location system in this environment requires adequately enabling communication technology. However, widely adopted technologies such as Wireless Fidelity (WiFi), Bluetooth, and Radio Frequency Identification (RFID) are considered poorly suited to enable hospital localization due to their inherent drawbacks, including high implementation costs, poor signal strength, imprecise estimates, and potential interference with medical devices. The increasing expenses associated with the implementation and maintenance of these technologies, along with their limited accuracy in dynamic hospital environments, underscore the pressing need for alternative solutions. In this context, it becomes imperative to explore and present novel approaches that not only avoid these challenges but also offer more cost effective, accurate, and interference-resistant connectivity to achieve precise localization within the complex and sensitive hospital environment. In the quest to achieve adequate localization accuracy, this article strategically focuses on leveraging Visible Light Communication (VLC) as a fundamental technology to address the specific demands of hospital environments to achieve the precise localization and tracking of life-saving equipment. The proposed system leverages existing lighting infrastructure and utilizes three transmitting LEDs with different wavelengths. The Received Signal Strength (RSS) is used at the receiver, and a trilateration algorithm is employed to determine the distances between the receiver and each LED to achieve precise localization. The accuracy of the localization is further enhanced by integrating a trilateration algorithm with the sophisticated Particle Swarm Optimization (PSO) algorithm. The proposed method improves the localization accuracy, for example, at a height of 1 m, from a 11.7 cm error without PSO to 0.5 cm with the PSO algorithm. This enhanced accuracy is very important to meet the need for precise equipment location in dynamic and challenging hospital environments to meet the demand for life-saving equipment. Furthermore, the performance of the proposed localization algorithm is compared with conventional positioning methods, which denotes improvements in terms of the localization error and position estimation.

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confidence: 99%

Performance Analysis of the Particle Swarm Optimization Algorithm in a VLC System for Localization in Hospital Environments

Candia,

Játiva,

Azurdia Meza

et al. 2024

Applied Sciences

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Underwater optical wireless communication system: Deep learning CNN with NOMA-based performance analysis

2023

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This research is looking forward improving the performance for underwater optical wireless communication (UOWC) by applying a Non-orthogonal multiple access (NOMA) technique. We also get the benefit of the advantage the transmission based on convolutional neural network hybrid with a long short-term memory cell. The relays selection and power optimization are two main parameters to enhance the UOWC system performance. In this work, we suppose a pairing method for NOMA nodes. By replacing the inner dense connections with convolution layers, this model is proposed to overcome high complexity and over fitting to improve the model performance. The obtained performance for sum rates show that NOMA outperforms the orthogonal multiple access system by ~ 6%. Applying a step-by-step sub-optimization algorithm (SSOPA) yields better results than using fixed power allocation (FPA), while using a global optimal power allocation algorithm (GOPA) increases the sum rates over both FPA and SSOPA. It is found that the improvement when using GOPA combined with CNN approach enhances the performance of sum rates by ~ 2.5% than using the independent-relay-aided NOMA (ICNOMA) for UOWC. The GOPA improvement is 1.2%, 2.5%, 8.7% over FPA and is 0.12%, 0.34%, 2.09% over SSOPA, for clear, pure, and coastal water, respectively. The ICNOMA outperforms both ordinary NOMA (ONOMA) and cooperative NOMA (CNOMA) without independent relay nodes. The ICNOMA achieves an improvement over ONOMA and CNOMA by 20.4% and 3.2%, respectively.

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Deep learning based BER improvement for NOMA-VLC systems with perfect and imperfect successive interference cancellation

Salama

Aly

Amer³

2023

Opt Quant Electron

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This paper focuses in the improvement of the BER performance of multiple-input multiple-output (MIMO) systems is investigated utilizing non-orthogonal multiple access-visible light communication (NOMA-VLC). Applying multi-user downlink MIMO-NOMA-VLC system within equal gain combiner at the receiver is used with two types of modulation; On–Off Keying (OOK) and L-Pulse Position Modulation, with L = 4 and 8. The perfect and imperfect successive interference cancellation scenario is used in this system, and the scenario is considered for two and three users. Our proposed framework is divided into two stages. First, data is collected using the MATLAB software. Second, two deep learning models (DLMs); ResNet50V2 and InceptionResNetV2 which are trained and tested. Python software is then used to develop and train the DLMs. The obtained results assures the superiority of ResNet50V2 over InceptionResNetV2, in different cases and for all users. The BER performance is also studied versus α for two and three users OOK modulation single-input single-output (SISO), (2 × 2) and (3 × 2) MIMO-NOMA-VLC systems based on the two DL techniques; ResNet50V2 and InceptionResNetV2. Again, ResNet50V2 achieves better results than InceptionResNetV2. The obtained results are compared with the previously published ones, showing that the proposed system and techniques achieve better results.

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Visible light backscattering with applications to communication and localization in healthcare: A survey

Cited by 9 publications

References 27 publications

Performance Analysis of the Particle Swarm Optimization Algorithm in a VLC System for Localization in Hospital Environments

Performance Analysis of the Particle Swarm Optimization Algorithm in a VLC System for Localization in Hospital Environments

Underwater optical wireless communication system: Deep learning CNN with NOMA-based performance analysis

Deep learning based BER improvement for NOMA-VLC systems with perfect and imperfect successive interference cancellation

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