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.

Indoor visible light communication (VLC) uses light emitting diodes (LEDs) to provide both illumination and data communication. The deployment of LED plays an important role in maintaining a steady optical power distribution over the reference receiving plane. Typical ways of luminaire deployment in offices and homes are not optimized for VLC. This paper investigates various configurations of LEDs for deploying them on the ceilings of offices and homes. The existing square array deployment of LEDs does not provide a full coverage on the receiving plane leaving dead spaces, which in turn affects the performance of the whole system. An optimized circular deployment scheme is proposed that considers both the position of the LED transmitters on the ceiling and the first reflections at each wall to yield more accurate results. Rectangular deployment and circular deployment are analyzed through simulation of the received optical power distribution, average outage area rate, and energy consumption. An optimization technique is developed to analyze the LED deployment schemes. It is clear from the results that the circular LED deployment provides a better performance than the square array grid LED deployment.

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.

Hybrid pre-coding strategies are considered as a potential solution for combating path loss experienced by Massive MIMO systems operating at millimeter wave frequencies. The partially connected structure is preferred over the fully connected structure due to smaller computational complexity. In order to improve the spectral efficiency of a partially connected hybrid pre-coding architecture, which is one of the requirements of future 5G/B5G systems, this work proposes the application of evolutionary algorithms for joint computation of RF and the digital pre-coder. The evolutionary algorithm based scheme jointly evaluates the RF and digital pre-coder for a partially connected hybrid structure by taking into account the current RF chain for computations and therefore it is not based on interference cancellation from all other RF chains as in the case of successive interference cancellation (SIC). The evolutionary algorithm, i.e., Artificial Bee Colony (BEE) based pre-coding scheme outperforms other popular evolutionary algorithms as well as the SIC based pre-coding scheme in terms of spectral efficiency. In addition, the proposed algorithm is not overly sensitive to variations in channel conditions.

Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline, pharmaceutical, e-commerce, technology, and hospitality. We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory (LSTM) model to improve stock prediction. The LSTM has the advantage of analyzing relationship between time-series data through memory functions. The performance of the system is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

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.