**1.2. Open issues and chapter objectives**

Despite recent advances in RFID MAC layer design, several issues remain open today. Current RFID algorithms are designed under simplistic assumptions such as the collision-model. In such a collision-model, collisions are regarded as the loss of all the transmitted information. On the contrary, collision-free transmissions are always assumed to be correctly received. These assumptions are, however, highly inaccurate, particularly for wireless settings with rapidly changing channel conditions and assisted by modern signal processing tools. In wireless networks, packet transmissions can be lost due to random fading phenomena and not only due to collisions. On the other hand, a collision with multiple concurrent transmissions can be resolved by means of multiple antenna receivers. Therefore, a new approach for a more accurate design and modeling of random access protocols in modern wireless networks is required. In the literature of conventional random access protocols, considerable advances in these aspects have been recently made using the concept of cross-layer design [18–26]. The objective of this chapter is to use two of these recent cross-layer solutions and modeling approaches to improve the performance of RFID. In particular, we focus on those solutions that make use of signal processing tools that exploit diversity in the space (multi-packet reception) and time domains (retransmission diversity).

tag activation/detection processes (considering multi-packet reception and retransmission diversity) are then proposed. The proposed approach also allows for a novel joint design of reader and tag anti-collision schemes. Conventionally, these two algorithms were designed independently from each other. However, readers and tags operate in the same frequency band. Therefore, contention between their transmissions can potentially arise. Furthermore, reader anti-collision policies directly influence tag activation, and thus also the way in which tags collide when responding to readers' requests. Therefore, a complete model of RFID MAC layer should consider both processes together rather than independently. The proposed framework fills this gap by simultaneously modeling tag activation and the corresponding tag responses to readers, while also considering multi-packet reception and retransmission

An RFID Anti-Collision Algorithm Assisted by Multi-Packet Reception and Retransmission Diversity

http://dx.doi.org/10.5772/54069

51

To complement the framework for MAC/PHY cross-layer design, a Markov model is also presented, which allows for capacity and stability evaluation of asymmetrical RFID systems. The approach consists of defining the states (i.e., the set of active tags/readers) that describe the network at any given time, and then map them into a one-dimensional Markov model that can be solved by standard techniques such as eigenvalue analysis. The results show that the proposed algorithms as well as the joint cross-layer approach and the Markov model provide considerable benefits in terms of capacity and stability over conventional solutions.

The organization of this chapter is as follows. Section 2 describes the framework for cross-layer design, and gives details of the operation of the protocol with multi-packet reception and retransmission diversity. Section 3 describes the proposed metrics and the Markov model. Section 4 addresses the optimization of the system and displays the results

Consider the slotted RFID network depicted in Fig. 1 with a set R of *K* readers R = {1, . . . *K*} and a set T of *J* tags T = {1, . . . , *J*}. Each reader is provided with *M* antennas that will be used to recover, using source separation, the simultaneous transmissions of several tags. Two main processes can be distinguished in the RFID network in Fig. 1: Tag activation by the transmission of readers, also called the down-link transmission; and the backscattering response towards readers by previously activated tags, also called up-link transmission. In the down-link, the transmit power of reader *k* will be denoted by *Pr*,*<sup>k</sup>* while its probability of transmission will be denoted by *pr*,*k*. All the antennas will be assumed to transmit the same signal in the down-link. The subset of active readers at any given time will be denoted by R*t*. Tags are activated when the signal-to-interference-plus-noise ratio (SINR) given a reader transmission is above an activation threshold. The set of activated tags will be denoted by T*P*. In the up-link, the active tags proceed to transmit a backscatter signal using a randomized transmission scheme. The subset of tags that transmit a signal once they have been activated will be given by T*t*, where each tag *j* will transmit with a power level denoted by *Pt*,*j*. Details

using different scenarios. Finally, Section 5 presents the conclusions of the chapter.

of the down- and up-link models are given in the following subsections.

**2. System model and cross-layer framework**

diversity at the reader side.

**1.5. Organization**

### **1.3. MAC-PHY cross-layer design: Multi-packet reception and retransmission diversity**

Multi-packet reception is a concept that has revolutionized the design paradigm of random access protocols. Conventionally, collisions were always considered as the loss of all the transmitted information. However, modern multiuser detection and source separation tools allow for the simultaneous decoding of concurrent transmissions. Design of random access protocols with multi-packet reception has been addressed in [9] using a symmetrical and infinite user population model, and in [16] using an asymmetrical and finite user population model. A novel multi-packet reception scheme that exploits the time domain in order to achieve diversity has been proposed in [26], and it has been called network diversity multiple access (NDMA). In NDMA, a virtual MIMO (multiple-input multiple-output) system is induced by requesting as many retransmissions as needed to recover the contending packets using source separation. A hybrid algorithm with multi-packet reception and retransmission diversity has been proposed in [21].

### **1.4. Chapter contributions**

This chapter aims to use the concepts of multi-packet reception and retransmission diversity in the MAC layer design of passive RFID systems. To investigate these two cross-layer random access algorithms in the context of RFID, a novel framework which includes PHY (physical) and MAC (medium access control) layer parameters of RFID is here employed. The framework consists of the co-modeling of both the down-link (reader-to-tag) and up-link (tag-to-reader) signal-to-interference-plus-noise ratio (SINR) experienced in a multi-tag and multi-reader environment. This framework was first proposed in our previous work in [22], and it has been modified here to be used in the context of multi-packet reception and retransmission diversity. Based on this updated framework, stochastic models for tag activation/detection processes (considering multi-packet reception and retransmission diversity) are then proposed. The proposed approach also allows for a novel joint design of reader and tag anti-collision schemes. Conventionally, these two algorithms were designed independently from each other. However, readers and tags operate in the same frequency band. Therefore, contention between their transmissions can potentially arise. Furthermore, reader anti-collision policies directly influence tag activation, and thus also the way in which tags collide when responding to readers' requests. Therefore, a complete model of RFID MAC layer should consider both processes together rather than independently. The proposed framework fills this gap by simultaneously modeling tag activation and the corresponding tag responses to readers, while also considering multi-packet reception and retransmission diversity at the reader side.

To complement the framework for MAC/PHY cross-layer design, a Markov model is also presented, which allows for capacity and stability evaluation of asymmetrical RFID systems. The approach consists of defining the states (i.e., the set of active tags/readers) that describe the network at any given time, and then map them into a one-dimensional Markov model that can be solved by standard techniques such as eigenvalue analysis. The results show that the proposed algorithms as well as the joint cross-layer approach and the Markov model provide considerable benefits in terms of capacity and stability over conventional solutions.

### **1.5. Organization**

2 Radio Frequency Identification

**diversity**

diversity has been proposed in [21].

**1.4. Chapter contributions**

**1.2. Open issues and chapter objectives**

Despite recent advances in RFID MAC layer design, several issues remain open today. Current RFID algorithms are designed under simplistic assumptions such as the collision-model. In such a collision-model, collisions are regarded as the loss of all the transmitted information. On the contrary, collision-free transmissions are always assumed to be correctly received. These assumptions are, however, highly inaccurate, particularly for wireless settings with rapidly changing channel conditions and assisted by modern signal processing tools. In wireless networks, packet transmissions can be lost due to random fading phenomena and not only due to collisions. On the other hand, a collision with multiple concurrent transmissions can be resolved by means of multiple antenna receivers. Therefore, a new approach for a more accurate design and modeling of random access protocols in modern wireless networks is required. In the literature of conventional random access protocols, considerable advances in these aspects have been recently made using the concept of cross-layer design [18–26]. The objective of this chapter is to use two of these recent cross-layer solutions and modeling approaches to improve the performance of RFID. In particular, we focus on those solutions that make use of signal processing tools that exploit diversity in the space (multi-packet reception) and time domains (retransmission diversity).

**1.3. MAC-PHY cross-layer design: Multi-packet reception and retransmission**

Multi-packet reception is a concept that has revolutionized the design paradigm of random access protocols. Conventionally, collisions were always considered as the loss of all the transmitted information. However, modern multiuser detection and source separation tools allow for the simultaneous decoding of concurrent transmissions. Design of random access protocols with multi-packet reception has been addressed in [9] using a symmetrical and infinite user population model, and in [16] using an asymmetrical and finite user population model. A novel multi-packet reception scheme that exploits the time domain in order to achieve diversity has been proposed in [26], and it has been called network diversity multiple access (NDMA). In NDMA, a virtual MIMO (multiple-input multiple-output) system is induced by requesting as many retransmissions as needed to recover the contending packets using source separation. A hybrid algorithm with multi-packet reception and retransmission

This chapter aims to use the concepts of multi-packet reception and retransmission diversity in the MAC layer design of passive RFID systems. To investigate these two cross-layer random access algorithms in the context of RFID, a novel framework which includes PHY (physical) and MAC (medium access control) layer parameters of RFID is here employed. The framework consists of the co-modeling of both the down-link (reader-to-tag) and up-link (tag-to-reader) signal-to-interference-plus-noise ratio (SINR) experienced in a multi-tag and multi-reader environment. This framework was first proposed in our previous work in [22], and it has been modified here to be used in the context of multi-packet reception and retransmission diversity. Based on this updated framework, stochastic models for The organization of this chapter is as follows. Section 2 describes the framework for cross-layer design, and gives details of the operation of the protocol with multi-packet reception and retransmission diversity. Section 3 describes the proposed metrics and the Markov model. Section 4 addresses the optimization of the system and displays the results using different scenarios. Finally, Section 5 presents the conclusions of the chapter.
