Josko Radic

Energy Efficient Tag Estimation Method for ALOHA-based RFID systems

Radio frequency identification (RFID) technology has become an important tool for items identification and tracking. Its rapid deployment in dynamic industry areas enables variety of new applications in behavioral analysis and processes automation. In such systems it is crucial to identify all RFID tags before they leave the interrogation area. To accomplish fast identification, it is necessary to estimate a number of tags which are in the reader interrogation area. In this paper, we revisit the problem of tag quantity estimate and adapting frame size of dynamic frame slotted ALOHA widely used as RFID medium access control mechanism. The main disadvantage in the implementation of the state-of-the-art estimation algorithms includes the number of required computations, along with the temporary storage of large numbers which appear before estimation. As a consequence, such algorithms are energy inefficient and may require specific computer architecture to support the calculus. In order to address stated issues, we present improved linearized combinatorial model algorithm for optimal frame size selection which, due to linear property of the estimator, allows significant reduction in the estimate computation with acceptable tradeoff in accuracy. Simulations analyze the required number of slots to identify tag population and the floating point operation costs required to compute estimate. In addition, to emphasize the importance of reducing computational cost, we give a case study which compares energy consumed by the mobile RFID reader processor to compute the estimate and its energy equivalent of the required radio front-end energy to identify tags.

Software defined radio based implementation of RFID tag in next generation mobiles

Radio Frequency identification (RFID) technology has become important tool for items identification and tracking. In those purposes different types of RFID technologies could be used, depending on its application. Limitations of passive RFID technology, related to tags reading range and confidence in harsh environments, puts restrictions on implementation in the real life scenarios. To overcome the issue, but staying within the standards, we have considered development and implementation of active backscattering tag technology, which significantly improves tag reading range and confidence. Software Defined Radio (SDR) technology is promising technology for building mobiles of multiple radio standards in 4G networks. Regarding stated RFID technologies limitations and SDR technology, we present development and implementation of the Software Defined Radio (SDR) active backscattering tag compatible with the EPCglobal UHF Class 1 Generation 2 (Gen2) RFID standard. Such technology can be used for wide spectra of applications and services. The system is developed and tested on SDR platform. Validity and performances of developed Gen2 SDR tag are shown through actual presented results.

Early Frame Break Policy for ALOHA-Based RFID Systems

Throughput of dynamic frame slotted ALOHA (DFSA) in radio frequency identification (RFID) systems depends on the tags quantity estimate. This paper shows how to apply the slot-by-slot (SbS) estimate approach, along with the policy for the early frame-break. Simulation results show noticeable throughput improvements.

Comparing Theoretical and Experimental Results in Gen2 RFID Throughput

Radio frequency identification (RFID) technology, based on wireless communication between a reader and tags, is the most popular technology for indoor item identification and tracking. Among competing RFID technologies, Gen2 has emerged as the most popular one, due to the best price/performance ratio. In order to communicate with multiple tags, Gen2 RFID systems use dynamic frame slotted ALOHA (DFSA) as a medium access control (MAC) mechanism. To maximize DFSA throughput simple calculations show that the number of tags should equal the DFSA frame size. However, the number of tags in an environment is usually unknown and has to be estimated. So far, significant effort has been invested in estimating the correct number of tags. However, to the best of our knowledge, no one has explored the estimator performance in a real RFID measuring scenario. In this paper, we evaluate the throughput of a real RFID system by using the Software Defined Radio (SDR) technology. Our results show that phenomena related to radio waves propagation and reception have a significantly bigger influence on Gen2 RFID throughput than traditional approaches take into account.

Linearized Combinatorial Model for optimal frame selection in Gen2 RFID system

Radio Frequency Identification (RFID) technology become an important tool for items identification and tracking. In this paper we observe RFID Gen2 communication protocol between the RFID reader and the low-cost battery free passive RFID tags. To establish communication between the reader and tags, Gen2 uses Dynamic Frame Slotted ALOHA (DFSA) Medium Access Control (MAC) layer protocol with Q-Selection algorithm for frame size adaptation. DFSA constraints of Gen2 RFID reader-tag communication may become an issue in the fast identification of all tags in the interrogation area. To identify all tags as soon as possible, DFSA frame size should be selected properly so its throughput is maximized, and that can be achieved only if one can estimate number of interrogated tags correctly. In this paper we present Linearized Combinatorial Model (LCM) algorithm for the optimal frame size adaptation. Developed scheme is implemented and tested on Universal Radio Serial Peripheral 1 (USRP1) Gen2 reader application. Results analysis shows that our scheme outperforms Q-Selection algorithm.

Distribution of the noise squared norm in OFDM systems interfered by class a impulsive noise

Recently there is a noticeable interest in investigation of the squared norm distribution of complex-valued Gaussian variables that have different variances. A typical example is consideration of the channel state information (CSI) in multi-antenna systems. A similar problem appears in communication systems interfered by Middleton Class A impulsive noise. In this paper we develop exact expressions for the squared norm distribution in the time and frequency domains in OFDM systems interfered by Class A impulsive noise. Accurate approximation by the chi-square distribution based on equating the first two moments has been also derived.

Improved Linearized Combinatorial Model (ILCM) for optimal frame size selection in ALOHA-based RFID systems

Radio Frequency Identification (RFID) technology became the most important tool for identification of items and tracking. Nowadays, the most popular in terms of best price-performance ratio is passive RFID technology, where tags are both powered-up and communicating using the same radio waves transmitted via reader antenna(s).

Soft decision PAPR reduction in OFDM

This work deals with problem of improving the peak-to-mean average power ratio (PAPR) of the orthogonal frequency division multiplexing (OFDM) signals. Clipping is simple and efficient way to overcome the large PAPR in OFDM systems; however this technique results in bit error rate (BER) increasing in the receiver. In this paper we propose improvement of the iterative algorithm that is usual approach for PAPR reduction. Proposed scheme is based on soft estimation of the samples that are estimated as clipped. The performance of the proposed technique is evaluated for additive white Gaussian noise (AWGN). Simulation results shows improvement in comparison with known method without increasing complexity.

RFID-based solution for galleries and museums visit modelling using Markov model, BBN's and MAP decisions

RFID technologies are becoming increasingly popular and widely used in many applications. Tags can be used for environment and habit monitoring, healthcare applications, home automation and pedestrian or vehicle traffic control. This paper describes the method of building a robust N-state Markov model that describes visitors behaviour in a gallery room. The built model can be used in planning of exhibitions, in modelling of visitors preferences, and/or in generation of predictions related with exhibition lasting, expected sales and pricing. Presented system performance improvements are realised through Bayesian belief network (BBN) and maximum a posterior probability (MAP) decision approximation algorithm.

Reconstruction of the Samples Corrupted with Impulse Noise in Multicarrier Systems

This paper addresses estimation of the signal samples corrupted with impulse noise in multicarrier systems. The main contribution in our work is application of the well known Bussgang theorem for developing the explicit expression for estimation of the signal samples that are corrupted with impulse noise. In the presented analysis, estimation measure based on minimizing mean square error (MMSE), has been considered. Simulation results show that MMSE measure obtained by the proposed method reduces symbol error rate (SER) for the given number of iterations in comparison with the standard approach without estimation. Simulation results show very good agreement with the results of the theoretic analysis.