Many features of 5G are definitely important to the broadcasting service, including diverse content services such as follow-me TV, video-on-demand, but also gaming, Virtual reality (VR) and Augmented reality (AR) and many others. Meanwhile, those services depend more and more on the user's position accuracy, especially in indoor environment. With the increase of broadcasting data traffic indoors, to obtain a highly accurate position is becoming a challenge because of the impact of radio interference. In order to support a high-quality indoor broadcasting service, a high-accuracy positioning, radiation-free, and high-capacity communication system is urgently needed. In this paper, a 5G indoor positioning system is proposed for museums. It utilizes unlicensed visible light of the electromagnetic spectrum to provide museum visitors with high-accuracy positioning, multiple forms of interaction services, and highresolution multimedia delivery on a mobile device. The geographic data and the location-related data integrated into the 5G New Radio (NR) waveform are detailed. A general-purpose system architecture is provided and some basic techniques to enhance system performance are also investigated. A prelimi

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In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%.

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This paper proposes a scheme of joint Backscatter communication (BAC) and Visible Light communication (VLC) based on Non-orthogonal Multiple Access (NOMA) for a Beyond-fifth-Generation(B5G)/Sixth-Generation (6G) ultramassive Machine-Type Communication (umMTC) system. In this proposal, a hybrid RF-VLC channel combined with NOMA supports multi-users gain data from server and sensor information from local IoT items simultaneously. Moreover, benefits from noninterference between RF-VLC channel and more time-frequency resource allocated to each user, the communication performance is improved. A simulation was implemented to verify and estimate our proposal. Results indicated that our proposal gained higher channel capacity and reach lower bit error rate (BER).

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Visible Light Communication (VLC) is one of technology for the sixth generation (6G) wireless communication and also broadcast system. VLC systems are more resistant against Radio Frequency interference and unsusceptible to security like most RF wireless networks. Since VLC is one of suitable candidate for enforcing data security in future wireless networks. This paper considers improving the security of the next generation of wireless communications by using wireless device fingerprints in visible light communication, which could be used potentially for ATSC broadcasting applications. In particular, we aim to provide a detailed proposal for developing novel wireless security solutions using Visible light communication device fingerprinting techniques. The objectives are two-fold: (1) to provide a systematic review of AI-based wireless device fingerprint identification method and (2) to identify VLC transmitter, with respect to the ATSC physical layer modulation scheme, by analysing the differences in the modulated constellations signaled received by photo-diode, which will be proved by laboratory experimentation.

This paper presents a joint illumination and communication model for Gallium Nitride (GaN) Multiple Quantum Well (MQW) Light Emitting Diodes (LEDs) used in Visible Light Communication (VLC) systems. Based on device physics, the proposed model characterizes the intrinsic nonlinearity of the LED's electro-optic conversion by incorporating the LED's material, physical structure, and bias voltage. The modeling methodology and the model's accuracy are demonstrated through experimental measurements on a commercial sample LED by a VLC system performance testbed. The validation results indicate the proposed GaN MQW LED model is consistent with physical principles and accurately predicts the LED's nonlinear impact on the VLC system under varying signal frequency and illumination intensity of the LED, underscoring its significance for analyzing and optimizing VLC systems. Overall, the proposed model offers a valuable tool for the design and optimization of VLC systems using GaN-based LEDs.