Random Access Protocol in Multipacket Reception for ...

11th International Conference on Science Engineering & Technology

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Random Access Protocol in Multipacket Reception for Throughput Maximization and Delay Reduction A. Ashwin, Dr.S.J.K. Jagadeesh Kumar and V.R. Azhaguramyaa Abstract--- With the rapid proliferation of wireless services, it is paramount importance of how fast the data can be sent through the wireless networks. The use of multiple-packet reception (MPR) in wireless networks is known to improve throughput especially in high-traffic conditions. This paper considers random access protocols with Multipacket reception (MPR), which include both persistent CSMA protocols. Slotted-Aloha and slotted For both protocols, each node makes a transmission attempt in a slot with a given probability. We first establish the dependency of delay on buffer size and packet size which is by using the concept of router buffer. The router buffer is a is a leaky bucket as packets are coming in and going out towards the destination through this buffer. To increase the efficiency of the router buffer, we optimize the buffer parameters such as number of inorder packets, number of out-of-order packets and available buffer value at that particular instant of time., and then present a delay optimization approach for traffic in WLAN. We use Knapsack algorithm for buffer management to maximize the in-order packets and minimize the out-of-order packets simultaneously. Our approach exploits the buffer internals and dynamically adjusts the buffer usage so that a node transmits the packets in the desired order to its successive nodes. Careful estimation of packet size and buffer size helps in minimizing the delay, improving the capability of receiving packets in the correct order and reducing outof-order packets in the buffer at intermediate nodes. Our approach also controls the loss of data packets during transmission. The goals of this paper is to derive the optimal transmission probability maximizing a system throughput and to reduce delay under Multipacket Reception Index Terms— Wireless Local Area Networks, CSMA, KNAPSACK Algorithm, Optimal Transmission Probability Delay Analysis, Single and Multipacket Reception.

I.

Wireless Networks (802.11) A wireless local-area network (LAN) uses radio waves to connect devices such as laptops gets connected to the networks. When it is connected to a Wi-Fi hotspot at a cafe, hotel, airport lounge, or other public place, it is connecting to the business's wireless network. Slotted Aloha Time is divided into “slots” of one packet duration – E.g., fixed size packets. When a node has a packet to send, it waits until the start of the next slot to send it – Requires synchronization. If no other nodes attempt transmission during that slot, the transmission is successful – Otherwise “collision” – Collided packet are retransmitted after a random delay.

Throughput Slotted aloha T = 0.368 = (1/e) Optimal Transmission Probability [1][7] Uplink of a cellular system where M mobiles transmit over a communication channel to a base station. DCF-Distributed Coordinated Function PCF-Point Coordinated Function Carrier sense multiple access (CSMA) is a probabilistic media access control (MAC) protocol in which a node verifies the absence of other traffic before transmitting on a shared transmission medium, such as an electrical bus, or a band of the electromagnetic spectrum. II.

A. Ashwin, PG Student, II ME CSE, Sri Krishna College of Engineering and Technology, Coimbatore Dr.S.J.K. Jagadeesh Kumar , Professor, Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore V.R. Azhaguramyaa , Assistant Professor, Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore

INTRODUCTION

• •

ISBN 978-93-85477-73-7

OBJECTIVES OF THIS PAPER:

Achieving a system throughput that is close to maximum. Reducing the network delay.

11th International Conference on Science Engineering & Technology

The rest of the paper is organized as follows. In Section II existing system protocols of wlan’s are briefly explained. Section III explains the concepts regarding the proposed system. Section IV deals with the simulation parameters in NS2 simulator. Section V concludes the paper. III.

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technologies like orthogonal CDMA, MIMO and space– time codes.

LITERATURE SURVEY

In this section, we present the various qos parameters used in WLAN. This survey concentrates on the qos parameters that are limited to the Multipacket reception alone is taken. A) Single packet reception [3] 1) Finite users: We need to compute d success given K users transmit in N channels, P (d|K). Only one packet can be successfully received under collision. This occurs when the base station can estimate the channel of only the user whose SINR is maximum. In the general form we get the probability of success as

K users transmit. Similarly, Es (N, K) is the total number of ways in which all the N channels are successful given a total of K users transmit. Derivation of Ef (N, K) and Es (N, K). 2) Infinite users: In the infinite user case the new packet arrival is assumed to be Poisson distributed with mean

The conventional MAC (Medium Access Control) protocols assume that only one packet can be received at a given time. However, with the advent of sophisticated signal processing and antenna array techniques, it is possible to achieve Multipacket reception (MPR) in the physical layer (PHY). In this paper, we propose a PHY methodology and the corresponding MAC protocol for MPR in wireless local area networks (WLANs). The proposed MAC protocol closely follows the 802.11 DCF (Distributed Coordination Function) scheme and enables MPR in a distributed manner. For the proposed MPR system, a closed-form expression of the average throughput is derived. Based on the expression, an optimal transmission probability that maximizes the throughput can be attained. In addition, two enhancement schemes are presented to further improve the performance of the MPR protocol. Numerical results show that the proposed MPR system can considerably increase the spectrum efficiency compared to the WLANs with conventional collision models. System Model

arrival rate of λ. With the proposed capture model, the throughput of an infinite user S-ALOHA network is S=

---- (2)

Where p (n) is the Poisson distribution as given = with mean arrival rate and by is the success probability when n users transmit. For = 1/n Using (2), the value of for SISO/ICcase which the network is stable can be obtained. B) Multipacket reception [1] In networking, Multipacket reception [6] refers to the capability of networking nodes for decoding/demodulating signals from a number of source nodes concurrently. In wireless communications, Multipacket reception is achieved using physical layer

C) MPR Realization [4] i)

Transmitter Perspective The first class of techniques that enable MPR require a significant effort by the transmitter. Examples such as CDMA and OFDMA fall into this class. CDMA allows multiple users to be multiplexed over the same wireless channel by employing a coding scheme where each transmitter is assigned a code. The baseband signal is multiplexed with a spreading code running at a much higher rate. The spreading code is a pseudorandom code, and all codes used for one channel are orthogonal.

ISBN 978-93-85477-73-7

11th International Conference on Science Engineering & Technology

Therefore, on the receiver side, an unwanted signal will be eliminated by the cross-correlation decode, and only the relevant signals are conserved. This technique allows the receiver to decode multiple data streams with the different codes that are known a priori. The ability to decode multiple data packets depends on the selection of code. For example, the orthogonality is the key that allows the receiver to decode the set of simultaneous arrived signals, and this is done on the transmitter side. ii)

Transreceiver Perspective In this class of techniques, to enable MPR, transmitters and receivers should cooperate on some operations. Multiantenna MIMO system can achieve MPR by exploiting the spatial diversity of the transmissions. In such a system, each antenna corresponds to a different channel characteristic. A packet sent through one antenna can be easily distinguished from one sent with another antenna by channel estimators. In case of a cellular or AP-based wireless network, the communication between Mobile Station (MS) and Base Station (BS) is based on the MPR capability of the BS. The realization of MPR in a multi-antenna MIMO system requires both transmitters and receivers to implement specific functionalities. That is how it differs from pure transmitter-based techniques. iii)

Receiver Perspective Next, we present a class of techniques that involve only the receiver for decoding several packets simultaneously. Compared to the previous two classes, this class of solutions comes closer to the ideal of MPR, to shift the responsibility from transmitters to receivers. The Match Filter (MF) [1] approach is widely used for single user detection. Even though it is not optimal when multiple users are present, still a receiver can use a bank of Match Filters to decode packets coded with spreading codes that need not even be orthogonal. Techniques used to separate signals for Multiuser Detection (MUD) are more applicable for MPR. That is why many papers on network capacity with MPR cited it as the technique to realize MPR. It is a way to alleviate Multiple Access Interference (MAI) during the simultaneous transmissions on the same channel. An optimal MUD detector refers to maximum likelihood sequence estimation (MLSE) which requires knowledge of all transmitters’ spreading codes (e.g., base stations in a cellular CDMA system). This optimal detector is too complicated for practical application although it has excellent performance. One of the reasons, given in, is that cellular system is centrally controlled and always has synchronization among different users to some extent. However, in distributed wireless networks, it is quite difficult to apply signalprocessing techniques to separate the asynchronous

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transmissions. Therefore, MLSE is not a candidate for MPR. Protocols in MPR [1] i)

MAC with MPR Mergen et al proposed a multiple-access protocol based on receiver-controlled transmission (RCT). RCT is a hybrid scheduled and random access MAC, which applies scheduling to determine the receiving nodes and then the transmitters for each receiving node. RCT aims to maximize local throughput by granting an appropriate subset of users so that the varying levels of MPR capability are exploited. The simulation results show that using RCT with MPR achieves almost double the local throughput of the slotted Aloha protocol. We took the adaptation on the MAC layer is necessary to fully exploit MPR capability, the proposed scheduling works with predefined network topologies and is hard to extend to general ad hoc networks. Zhao and et al designed a multiqueue service room (MQSR) protocol, which exploits MPR capability to handle users with different quality of service constraints. For each slot, the number of users is computed to maximize the expected number of successfully received packets. The performance of MQSR [5] is compared with slotted Aloha and URN [3] with 2MPR. The URN protocol manages to optimally adapt to the network load. In both MQSR and URN can extend the network capacity. But MQSR approaches the maximum throughput upon the transmission probability=0.5 while URN and slotted Aloha achieve their maximum throughput when P close to 1. IV.

PROPOSED MODEL

The existing approaches either reduce the delay or streamline the packet transmission by optimizing the transport layer application or network layer application (e.g., changes the size of packet and uses a compression technique). To the best of our knowledge, there is no approach that determines the optimized value for effective buffer size utilization that increases the throughput capability for packet transmission from source to destination and decreases the out-of-order capability of the packets in the buffer. In this paper, we develop a model for delay optimization by adopting Knapsack algorithm. Our approach differs from traditional approaches by considering the internal properties of the buffer such as filling speed of the buffer with out-of-order packets and number of packets loss dynamically with respect to time. Also, we estimate the number packets which are coming in-order to buffer and goes directly towards destination. Then we consider a FIFO (First-in First-out) buffer The FIFO buffer with capacity B bytes can store up to n packets (say, P1, P2, . . ., Pn) at any point of time. The

ISBN 978-93-85477-73-7

11th International Conference on Science Engineering & Technology

input and output data rates of the buffer are Di and Do respectively. Here, the packets arrive the buffer at time Ta and transmit them at time Tt with a delay of Td seconds. The synchronization constant K can be defined as the ration of input date rate and output data rate. That is, K= ---- (3) For faithful transmission of multimedia data and avoiding loss of (i) Maximum delay to avoid data pattern (text, audio, and video) loss. (ii) Minimum delay to avoid collision. At any instant, we assume the following: (1) There are n numbers of packets in the buffer, with packet Size p. Therefore, the buffer capacity B is, B=n*p--- (4) (2) The buffer is full for each cycle Tt. So the total time taken by a Packet to come out of the buffer is n* Tt Synchronization constant K varies between 0 and 1. Note That if K > 1, then there is no significant transmission constraint. That is, K = 1 represents perfect synchronization, and K – 1 represents imperfect synchronization. We know that the output data rate is the ratio of buffer capacity to the total time taken for the last packet to transmit out, that is, D0=

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sequence, (b) directly reach to destination through intermediate nodes or (c) becomes in-order within a specified time of transmission. So, we follow a dynamic approach of Knapsack, where we maximize the in-order capability of packets and at the same time minimize the haphazard packet transmission in the buffer. This approach not only helps to reduce and optimize the delay but also suitable to control the loss of packets in the network, which is the main criterion of transmission of nodes. Knapsack Algorithm [2] Knapsack (n, m, nbf [], IB []) // Number of iterations n; buffer size m; Unfilled Buffer nbf; Packets becoming in-order inside Buffer IB; Output subset of optimal packets X. Begin 1. For (i = 0; i < n; i++) For (j = 0; j < m; j++) IBq [i][j] = 0; If (j - nbf[i] < 0) IBq [i][j] = IBq [i-1] [j]; else IBq [i][j] = Max (IBq [i-1] [j], IBq [i -1] [j - nbf[i] + IB[i]); 2. For (i = 0; i 0 && j > 0)

Using the eq (3) we get,

If (IBq[i][j]! = IBq[i-1] [j]) x[i] = 1;

=>K+

j = j - nbf[i];

B(Ta+Td) =n*K*P*Tt

i = i -1;

Therefore,Td=

6 Return X;

-Ta

To optimize the delay, we used Knapsack algorithm. Pisinger (1994) used the Knapsack algorithm to fill the packets in the memory by optimizing the size of packets within the buffer. However, in our case, packets are coming in, getting stored and going out through an intermediate node that acts as a router. That is, we can say that the buffer acts as a leaky bucket where some packets can (a) remain within the buffer due to out-of-order

End V.

SIMULATION

The simulation we have used is NS2 Simulator. Our network is modeled as collection of wireless nodes in an area of 300x300 m2.

ISBN 978-93-85477-73-7

11th International Conference on Science Engineering & Technology

Table 1. Simulation Parameters Parameters

Values

Number of Nodes

100

Active Nodes

20

Model Used

Random Way Point Model

Speed of Node(Km/hr)

10

Transmission Range (m) Message Size (KB)

150 5-20

VI.

CONCLUSION

In this paper, we propose a system that achieves system throughput close to maximum and reduction in delay under Multipacket Reception REFERENCES [1]

Yun Han Bae, Bong Dae Choi, Member, IEEE, and Attahiru S. Alfa, Member, IEEE “Achieving Maximum Throughput in Random Access Protocols with Multipacket Reception”, IEEE Vol. 13, No.3, March 2014. [2] Syed Jalal Ahmad, V.S.K. Reddy, A. Damodaran, P. Radha Krishna “Delay optimization using Knapsack algorithm for multimedia traffic over [3] MANETs”, Elsevier Ltd, May 2015. [4] Hemabh Shekhar and Mary Ann Ingram” On the Use of LMMSE Receiver for Single and Multiple Packet Reception”, IEEE Vol. 13, No.3, March 2010. [5] Jia-Liang Lu, Wei Shu, and Min-You Wu [6] “A Survey onMultipacket Reception for Wireless Random Access Networks”, Journal of Computer Networks and Communications, Volume 2012. [7] Qing Zhao, and Lang Tong,” A Multiqueue Service Room MAC Protocol for Wireless Networks with Multipacket Reception”, IEEE, Vol 1, FEBRUARY 2003. [8] B.J. Kwak, N.O. Song, and L.E. Miller, “Performance Analysis of Exponential Backoff,” IEEE/ACM Trans. Networking, vol. 13, no. 2, pp. 343-355, Apr. 2005 [9] R.H. Gau and K.-M. Chen, “Probability Models for the Splitting Algorithm in Wireless Access Networks with Multi-Packet Reception and Finite Nodes,” IEEE Trans. Mobile Computing, vol. 7, no. 12, pp. 1519-1535, Dec. 2008. [10] B.J. Kwak, N.O. Song, and L.E. Miller, “Performance Analysis of Exponential Backoff,” IEEE/ACM Trans. Networking, vol. 13, no. 2, pp. 343-355, Apr. 2005 [11] R.H. Gau and K.-M. Chen, “Probability Models for the Splitting Algorithm in Wireless Access Networks with Multi-Packet Reception and Finite Nodes,” IEEE Trans. Mobile Computing, vol. 7, no. 12, pp. 1519-1535, Dec. 2008.

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