Recently, visible light communications (VLCs) based on the orthogonal frequency-division multiplexing (OFDM) technique play an important role in the field of wireless communications. Researches in that field found techniques to improve performance and reduce the system complexity. This paper proposes a system named asymmetric symmetric subcarriers enhanced optical (ASSEO)-OFDM applied to VLC based on OFDM technique. The proposed system enhances the transmitted data rate by a factor of 1/8 compared with other existing systems. Keeping the same transmitted data rate as the comparable techniques, simulation results show an enhancement of the Eb/No for the proposed system by at least 0.65 dB than asymmetrically and symmetrically clipping optical (ASCO)-OFDM, by at least 2 dB than asymmetrically clipped optical (ACO)-OFDM and FLIP-OFDM, and by at least 0.9 dB than DC-biased optical (DCO)-OFDM system at bit error rate (BER) of 10 −4 . An analytical description of the ASSEO proposed system is also introduced in this paper besides a comparative study with other existing systems.

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Massive Multiple Input Multiple Output (MIMO) is a key technique used in 5G mobile communication systems; it aims to efficiently increase the spectral efficiency of the communication systems. Massive MIMO is a MIMO system with a massive number of antennas in the base station, it uses its large number of antennas to efficiently transmit and receive the signals between the base stations and the user equipment and maximize the spectral efficiency of the system. Massive MIMO is mainly composed of three important processes: channel estimation, uplink transmission (receive beamforming), and downlink transmission (transmit beamforming). Based on the effective channel estimation methods, the base station can process the signal to make efficient transmit and receive beamforming and provide good transmission and reception quality, which is measured by the spectral efficiency of the system. Many references present the basics of massive MIMO processes, including channel estimation, transmit beamforming and receive beamforming. This paper aims to present a cleared and concluded study on these basics massive MIMO processes. It presents different channel estimation methods and evaluates its performance based on the normalized mean square error. It also presents different receive and transmit beamforming techniques and evaluates its performance based on spectral efficiency.

OFDM systems are sensitive to Carrier Frequency Offsets (CFO) that results in performance's degradation. Researchers have proposed various CFO compensation techniques. Some estimates the CFO's value then compensates for it using preamble methods. Others estimate the CFO value using the inherent construction of the time-domain OFDM symbol. Our proposed technique changes the conventional structure of the OFDM symbol to allow estimating the CFO values over a wide range. CFO estimation range, accuracy, transmitted data-rate, system complexity, and the compatibility of a specific estimator to be used in different applications are the most important parameters to be considered in researches. In this paper, a comparative analysis between preamble techniques (Moose, Training Symbol) and the OFDM internal construction-based estimators including blind and proposed schemes will be discussed. Simulations show the trade-offs between those techniques.

Massive MIMO is dominant in the current wireless communication systems. Massive MIMO system uses a massive number of antennas to serve multi-users simultaneously. The growth of served users in the system will increase the interference between them and affect the system's performance. To maintain the qualified service for the growing number of users, user selection techniques can be used to separate users into groups to be served well. This paper proposes three user selection methods named Mean Step User Selection (MSUS), Second Null User Selection (SNUS), and Interference Threshold User Selection (ITUS) methods. These three user selection methods aim to serve users with the best possible performance. The performance of these proposed user selection methods will be evaluated and compared with the performance of the system without selection and with the system using other user selection methods such as Random User Selection (RUS), Semi-orthogonal User Selection (SUS), and Inter-Channel Interference Based Selection (ICIBS) methods. The simulation shows that the proposed MSUS, SNUS, and ITUS methods provide an improvement in the spectral efficiency of 75.39%, 92.13%, and 153.71% when compared with the system without the selection method. The proposed user selection methods also improve performance compared to other user selection methods.