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The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) improves spectrum efficiency by increasing the capacity of the wireless structure. Therefore, massive MIMO is promising for fifth generation (5G) wireless communications. In massive MIMO, channel estimation is a crucial part that should achieve reliable performance. Pilots are sent from the end-users to be used for estimating the channel. However, the problem of interference in pilot contamination affects the performance for cell-edge users. Specifically, pilot contamination appears when the same pilot sequence is utilized at the same time by more than one terminal. This lead to an inaccurate estimation of the channel. Consequently, the decoded data will not be reliable. For mitigating these pilot contamination effects, an enhanced fractional frequency reuse (eFFR) scheme is proposed that uses an algorithm in the allocation of pilot sequences to end users' devices based on the locations of the users from the target base station (BS). The simulation results exhibit that the proposed scenario outweighs the traditional FFR within both signal to interference, and noise ratio (SINR), and capacity. Consequently, the suggested scenario enhances the performance of more than 80% of the cell terminals and the other 20% of the terminals have a slightly lower performance compared to the FFR. [ABSTRACT FROM AUTHOR]