Visible light communication (VLC) is a relatively new wireless communication technology that allows for high data rate transfer. Because of its capability to enable high-speed transmission and eliminate inter-symbol interference, orthogonal frequency division multiplexing (OFDM) is widely employed in VLC. Peak to average power ratio (PAPR) is an issue that impacts the effectiveness of OFDM systems, particularly in VLC systems, because the signal is distorted by the nonlinearity of light-emitting diodes (LEDs). The proposed method Long Short Term Memory-Autoencoder (LSTM-AE) uses an autoencoder as well as an LSTM to learn a compact representation of an input, allowing the model to handle variable length input sequences as well as predict or produce variable length output sequences. This study compares the suggested model with various PAPR reduction strategies to demonstrate that it offers a superior improvement in PAPR reduction of the transmitted signal while maintaining BER. Also, this model provides a flexible compromisation between PAPR and BER.
The population is sharply growing in the last decade, resulting in non-potential power requests in dense urban areas, especially with the traditional power grid where the system is not compatible with the infrequent changes. Smart grids have shown strong potential to effectively mitigate and smooth power consumption curves to avoid shortages by adjusting and forecasting the cost function in real-time in response to consumption fluctuations to achieve the desired objectives. The main challenge for the smart grid designers is to reduce the cost and Peak to Average Ratio (PAR) while maintaining the desired satisfaction level. This paper presents the development and evaluation of a Multi-Agent Reinforcement Learning Algorithm for efficient demand response in Smart Grid (MARLA-SG). Also, it shows a simple and flexible way of choosing state elements to reduce the possible number of states, regardless of the device type, range of operation, and maximum allowable delay. It also produces a simple way to represent the reward function regardless of the used cost function. SARSA (State-Action-Reward-State-Action) and Qlearning schemes are used and attained PAR reduction of 9.6%, 12.16%, and an average cost reduction of 10.2%, 7.8%, respectively.
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