Learning AP in wireless powered communication networks

In a wireless powered communication network (WPCN), sensor nodes harvest energy to transmit information. By a harvest-then-transmit (HT) protocol, nodes can be classified into either energy receiving (ER) or data transmitting (DT) nodes depending on the current level of the harvested energy. Since nodes may join or leave a network any time and energy levels vary, the distribution of ER and DT nodes changes over time. As the number of contending DT nodes is highly dynamic, a quick learning mechanism is required for an access point (AP). We propose a learning AP that learns from experience and adapts the frame size according to the changes in the number of DT nodes. The proposed learning AP is also shown to learn well and react to the situation. We compare the performances of the proposed learning mechanism with a WPCN and conventional HT FSA schemes. The proposed RL scheme outperforms the comparative schemes in terms of success rate and delay.