A collision resolution algorithm for RFID using modified dynamic tree with Bayesian tag estimation

© 1997-2012 IEEE. A new tree-based anti-collision protocol for radio-frequency identification systems is proposed to achieve a very high tag identification efficiency. The proposed algorithm works in two phases. In the first phase, the number of competing tags is estimated through the proposed Bayesian estimation technique, while in the second phase, tags are identified using our modified dynamic tree algorithm. The system efficiency is mathematically derived and verified through simulation. Numerical results show that the proposed algorithm achieves a tag identification system efficiency of 45% and a time system efficiency of 78.5%, thus outperforming any existing collision resolution algorithms

A fast tag identification anti-collision algorithm for RFID systems

© 2019 John Wiley & Sons, Ltd. In this work, we propose a highly efficient binary tree-based anti-collision algorithm for radio frequency identification (RFID) tag identification. The proposed binary splitting modified dynamic tree (BS-MDT) algorithm employs a binary splitting tree to achieve accurate tag estimation and a modified dynamic tree algorithm for rapid tag identification. We mathematically evaluate the performance of the BS-MDT algorithm in terms of the system efficiency and the time system efficiency based on the ISO/IEC 18000-6 Type B standard. The derived mathematical model is validated using computer simulations. Numerical results show that the proposed BS-MDT algorithm can provide the system efficiency of 46% and time system efficiency of 74%, outperforming all other well-performed algorithms

FİZİKÖTESİNE AÇILAN PENCERE KUTLU SANAT

In this paper, three noise correlation-aided iterative decoding schemes are proposed for magnetic recording channels, where the correlation is imposed by the equalizer's spectral shaping effect. The first approach exploits the noise' correlation in the form of linear prediction-aided detection by increasing the number of detector trellis states invoked by the Bahl, Cocke, Jelinek, and Raviv (BCJR) detection algorithm. In the second approach, we have extended the first technique by employing both previous and future correlated noise samples in order to attain noise estimates. In order to achieve this objective, the classic BCJR algorithm has to be modified for the sake of additionally exploiting future noise samples. The third approach is designed for reducing the decoding complexity by applying the Viterbi Algorithm (VA) to assist the detector in finding the encoded sequences associated with the survivor paths in the detector's trellis, without increasing the number of trellis states. We will demonstrate that for the classic PR4-equalized Lorentzian channel, the proposed schemes are capable of offering a performance gain in the range of 1.1-3.7 dB over that of a benchmark turbo decoding system at the BER of 10-5 and at a recording density of 2.86. Keywords-magnetic recording, noise prediction, turbo codes

A GUI based Self-learning Tool for Polar Codes using Successive Cancellation and List Decoders

Recently, the importance of channel coding has become prominent for next-generation 5G wireless communication networks. Channel coding is becoming an active area of research and scholars are aiming to improve their knowledge about channel coding schemes which will be playing a crucial part in high-performance 5G networks. polar codes are the class of channel coding techniques that have been standardized in 5G. Moreover, it is a fact that activity tool-based learning raises the learning efficiency and generates intrinsic motivation for learning. Therefore, the main motivation for designing this paper is to develop a Graphical User Interface (GUI) based learning tool to help students easily learn channel coding techniques. In our teaching curriculum, we have used figure-based interactive representations to teach the basic concepts of polar codes efficiently. This learning tool provides users the option to learn about the encoding technique and two different decoding techniques named as Successive Cancellation (SC) and Successive Cancellation List (SCL) for different lengths of user-defined data bits. This complete model has been designed in Python