Frame Size Optimization for Dynamic Framed Slotted ALOHA in RFID Systems

In recent years, the State Grid has actively promoted the construction of ubiquitous power Internet of things, so as to realize the interconnection and optimized management of things in the power system. Specifically, radio frequency identification (RFID) is the core technology for the construction of ubiquitous power Internet of things. Due to the advantages such as low-cost, battery-less, non-line-of-sight communication and long-distance communi-cation, RFID has been widely used in the power equipment management, the power inspection, and other applications. To inventory the items in the power equipment warehouse, the ID collection requires the fast tag identification. However, there are usually a large number of tags in the warehouse, and the signals from different tags will easily conflict with each other. Considering the dynamic framed ALOHA protocol conforming to EPC C1G2 standards in commodity RFID systems, this paper proposes a frame size adjustment algorithm based on Q-learning and neural network (denoted as QN-learning). The problem of adjusting the frame size can be modeled as the Markov decision process (MDP), the observed states are the number of different kinds of slots, i.e., empty slot, single slot and collision slot, and the actions correspond to the selected frame sizes. Therefore, the neural network-based Q-learning, named as QN-learning, is preferred to learn how to adjust the frame size adaptively. Referring to the learned strategy, the agent is able to select the global-optimal frame size with the latest observation. Simulation results show that the proposed QN-learning-based method performs well in terms of the frame size adjustment. Particularly, the QN-learning-based method can identify tags fast with high throughput and few query rounds, and it reduces the data transmission as well