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Visible light communication (VLC) has recently gained significant academic and industrial attention. VLC has great potential to supplement the functioning of the upcoming radio-frequency (RF)-based 5G networks. It is best suited for home, office, and commercial indoor environments as it provides a high bandwidth and high data rate, and the visible light spectrum is free to use. This paper proposes a multi-user full-duplex VLC system using red-green-blue (RGB), and white emitting diodes (LEDs) for smart home technologies. It utilizes red, green, and blue LEDs for downlink transmission and a simple phosphor white LED for uplink transmission. The red and green color bands are used for user data and smart devices, respectively, while the blue color band is used with the white LED for uplink transmission. The simulation was carried out to verify the performance of the proposed multi-user full-duplex VLC system. In addition to the performance evaluation, a cost-power consumption analysis was performed by comparing the power consumption and the resulting cost of the proposed VLC system to the power consumed and resulting cost of traditional Wi-Fi based systems and hybrid systems that utilized both VLC and Wi-Fi. Our findings showed that the proposed system improved the data rate and bit-error rate performance, while minimizing the power consumption and the associated costs. These results have demonstrated that a full-duplex VLC system is a feasible solution suitable for indoor environments as it provides greater cost savings and energy efficiency when compared to traditional Wi-Fi-based systems and hybrid systems that utilize both VLC and Wi-Fi.

In this paper, we propose a compressive sensing (CS)-based channel estimation technique for asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) visible light communications (VLC) in 5G systems. We proposed a modified version of sparsity adaptive matching pursuit (SAMP) algorithm which is named as self-aware step size sparsity adaptive matching pursuit (SS-SAMP) algorithm. It utilizes the built-in features of SAMP and with additional ability to select step size according to the present situation, hence term self-aware, can provide better accuracy and low computational cost. It also does not require any prior knowledge of the sparsity of the signal which makes it self-sufficient. CS-based algorithms such as orthogonal matching pursuit (OMP), SAMP, and our proposed SS-SAMP were implemented on ACO-OFDM VLC. The paper is supported by simulation results which demonstrate performance of proposed scheme in terms of bit error rate (BER), mean square error (MSE), computational complexity, and key VLC parameter (LED nonlinearity, shot noise, thermal noise, channel response, and peak-to-average power ratio (PAPR). It is shown that the SS-SAMP is a good candidate for ACO-OFDM-based VLC that are mobile and have limited processing power, based on its performance and computational complexity.

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Bluetooth Low Energy (BLE) has become ubiquitous in the majority of mobile devices that connect wirelessly. With the increase in the number of devices, the probability of congestion also increases in a network. Data channels of the BLE use frequency hopping, but it is not available for advertising channels. The capability of the BLE for providing a wide range of parameters settings ensures the impressive potential for BLE devices to customize their discovery latency. But communication before connection setup is not synchronous and both the scanning devices and the advertising devices are unaware of the timing parameters of each other. This can lead to inefficient advertiser device discovery. To resolve this issue, an algorithm is proposed to reduce the average latency per advertiser experienced due to the increase in the number of BLE devices in a vicinity. It is observed that the average latency has shown improvement in the range of 35% to 55%, depending on different simulated scenarios. Due to this improvement the overall energy consumption is also reduced.