Bayesian Tag Estimate and Optimal Frame Length for Anti-Collision Aloha RFID System

In a dynamic framed slotted aloha RFID system, the key technique can be divided into two parts: precisely estimating tag quantity and determining an optimal frame length. For estimating tag quantity, this paper uses three risk functions to propose three Bayesian estimates, and improves the estimates to reduce computational complexity. For determining an optimal frame length, this paper derives an optimal frame length, which can make the system achieve maximum channel usage efficiency under the condition that the durations of an idle, a collision and a successful slot are not identical. In our simulations, comparison with several conventional tag estimates shows that the proposed Bayesian tag estimates have less error. In addition, the improved estimates have lower computational complexity and their estimate performance is not reduced. The simulations results also indicate that the derived optimal frame length guarantees the maximum channel usage efficiency.