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A novel current source multilevel inverter is introduced in this paper which is an appropriate alternative to be employed for low/medium power applications. the proposed converter is formed basic modules which paralleling these modules increse output current levels and improve quality of injected current to load or grid. in order to validate advantages of proposed converter versus the several multilevel current source inverters, a full comparison is provided. the simulation results shows the good performance of the proposed converter in off grid and grid-connected applications. Also experimental results for single-phase load confirm the practicablity of the proposed converter.

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In spectrum sharing among different users, it is very essential to deal with the jammers in the network. This work is more important when the jammer is intelligent and capable of learning the communication patterns between users. In this paper, a spectrum sharing method has been suggested to deal with the jammers of the network. The proposed method is based on multi agent reinforcement learning. In the first stage, the interfered sub-bands are obtained by the base station. In the next stage, deep reinforcement learning is designed to offer each user a safe and appropriate spectrum. For this purpose, the effect of the jammer in the sub-band is obtained from the correlation between the transmitted pilot and the received feedback in each frequency interval. Then, the discount factor of the reinforcement learning algorithm is adjusted adaptively based on the correlation value. In this way, the sub-bands that are less affected by the jammer are assigned to the users. The proposed method has been evaluated in different scenarios and the results indicate that the sum rate of network converges to the desired level faster and the proposed method shows resistance against the jammer.