Modeling Wireless Channel for Ad-Hoc Network Routing Protocol

Transcription

1 Modeling Wireless Channel for Ad-Ho Network Routing Protool Merlinda Drini, Tarek Saadawi Dept. ofeletrial Engineering, City College and Graduate Center ofcity University ofnew York New York, NY 10031, USA Abstrat. Enabling transmission over ad-ho networks is more hallenging than over onventional mobile networks beause a onnetion path in an ad-ho network is highly error-prone and the path an go down frequently. Although it is well-known that wireless hannels are time varying beause of user mobility and multi-path propagation effets, experiments for various types of hannels show that the basi hannel parameters an be stable for short time intervals. Therefore, a wireless hannel an be adequately represented by a set of stationary hannel models. There are number of wireless hannel models proposed in literature. For simpliity, we used a two state Markov model known as a Gilbert-Elliot model and then in order to model more appropriately a real ommuniation hannel, a three state Markov model is analyzed here. We demonstrate our onept by applying it to the Dynami Soure Routing protool (DSR). In our proposed modified DSR, both the route disovery and route seletion are based on physial layer parameter and the link monitoring funtion loated at eah node. Sine routing seletion based on the link quality is implemented, the minimum transfer delay from soure to destination and the maximal throughput may be obtained Simulation results show that aording to the hannel quality, the delay and throughput performane show remarkable performane when ompared to traditional DSR. Introdution The major problem with transmitting information over wireless hannels is the issue of link reliability. Wireless hannels have high hannel bit error rate and limited bandwidth.. The high bit error rate degrades the quality oftransmission and the network performane. The notion of a link in wireless ad ho networks is different from that in the wired network, where the links exists between two nodes only if they are onneted by a physial medium. On the other hand, in mobile wireless ad ho networks, the links exist theoretially between any pair ofnodes. Prepared through ollaborative partiipation in the Communiations and Networks Consortium sponsored by the U.S. Army Researh Laboratory under the Collaborative Tehnology Alliane Program, Cooperative Agreement DAADI The U.S. Government is authorized to reprodue and distribute reprints for Government purposes notwithstanding any opyright notation thereon /08/$ IEEE Eah wireless node an ommuniate with any other node within its transmission range, whih depends on the SNR at the reeiver and the oding sheme used by the transmitter. Routing in MANET (Mobile Ad Ho Network) is hallenging due to the dynami nature of network topology and the resoure onstraints. To maximize the hannel resoure utilization and minimize the network transfer delay along the path, the shortest path with minimum hops sheme is often adopted. Referene [I] shows that routing in multi-hop wireless networks using the shortest-path metri is not a suffiient ondition to onstrut good quality paths, beause minimum hop ount routing often hooses routes that have signifiantly less apaity than the best paths that exist in the network. However, the quality of wireless hannel among the mobile nodes is time varying due to fading, shadowing and pathloss. Given that the shortest-path metri does not take into aount the physial hannel variations of the wireless medium, it is desirable to selet the routes with minimum ost based on some other metris whih are aware of the wireless nature of the underlying physial hannel. In self-organized network, there are many other metris to be onsidered: Power, Paket Loss, Maximum available bandwidth et. These metris should ome from a ross-layer approah in order to make the routing layer aware ofthe loal issues ofthe underling layers. In this paper we perform the stohasti analysis of a Wireless ommuniation hannel. Our study is based on a Finite State Markov Model. The hannel swithes between different states. Eah state orresponds the probability that a paket sent by the transmitter will be reeived by reeiver or will be lost. The transition between the different states of the hannel is administrated by a Markov hain; this Markov hain is not observed diretly, but the reeived paket provides some probabilisti information about the urrent state of the hannel. Related work The idea of studying varying hannel onditions has been widely used in ommuniations. There are a number of wireless hannel models proposed in literature. But generally these models apture behavior ofthe network at MAC layer. Referene [2] has introdued an enhaned MAC protool for multi-hannel and multi-rate IEEE whih enables wireless ad ho networks to 549

2 opportunistially exploit the presene of frequeny diversity in this way: if the signal to noise ratio on the urrent hannel is not favorable, mobile nodes an opportunistially skip to better quality frequeny hannels enabling data transmission at a higher rate. This maps the hannel onditions at the PRY layer to the MAC. [3] investigates the effet of different predition models under a given hannel model in the way that, at the time ofpaket sheduling, the sheduler has to predit the urrent hannel state at a given reeiver, often based on an N-state Markov model. It shows that better performane an be ahieved by inreasing N, however the performane evaluation of any routing protool was not presented. In [4], the authors developed a three-state disrete time Markov-hain based model for the performane evaluation of MAC protool and analyzed the arrier sense multiple aess (CSMA) MAC protool for its delay and throughput harateristis in the presene of transmitter power ontrol. Nevertheless, authors in [5] suggest taking into aount the physial layer harateristis in the design of routing protools, whih is similar to our approah. Our objetive is to propagate the information, partiularly, SNR (signal to noise ratio) from the physial layer up to the routing to improve the performane ofthe existing protool. Furthermore, we develop a framework to analyze a performane of a routing protool applying the Markov hain model for the hannel, thus ombining physial layer and network layer together. Moreover, we shift our fous on using three state Markov model, whih reveals signifiant improvement potential. Thus, a routing layer an adapt itself to varying hannel onditions and, therefore an be employed for more realisti hannel haraterization: node swithes to a new good quality route, before the former one breaks.!he sinalto-noise ratio (SNR) experiened by mobile terminal IS omplex mobility-dependent stohasti proess resulting in three fading omponents eah of whih signifiantly influene performane ofthe wireless hannel. If we onsider Rayleigh fading hannel, then the reeived signal is the sum of signals with different phases aused by different paths, whih an be modeled as a random variable. In a multipath propagation environment with additive ausia noie, reeived SNR has also the Rayleigh distribution with probability density funtion p(y)= ; Finite State Markov Chain Model exp(-;) (1) Where, r is the average SNR, whih is physial layer dependent and an be expressed as y=p-l.-p -L tin P (6) (2) Where Pt (dbm) is the transmitter output power, Li(dB) is the implementation loss due to onneting ables or antenna patterns, PN(dBm) is the reeiver hardware related noise power and Lp(dB) is the radio propagation path loss whih an be expressed as L p = G 1 + G 2 10g 10 Ie + G 3 log10 d (3) G1, G2, G3 are onstants orresponding to the appliation senarios, f is the arrier frequeny and d is the distane between the transmitter and reeiver. In other words, given the physial layer onditions, the average reeived SNR enables us to haraterize the hannel variation at the physial layer using the finite state Markov hain hannel mode (FSMC). The basi requirement for defining a Markov proess is to speify the probability of making the next transition state for eah state in the proess and for eah transition time. Thus..(4) must be speified for 1 i, j N where i,j denote the state index and n is the disrete time index. The N 2 transition probabilities that desribe the Markov proess are generally represented by NxN transition probability matrix generally denoted as P. The transition probability matrix of a Markov proess an be graphially represented with the transition diagram, where the nodes represent the states ofthe proess itself. PO,l Pl,2 P 2,i Pj,K-l " " """;:_1 p PI.. P2:Z PK-l,K-l 0,0 Figure 1. K-State Markov model ofa hannel Let S=SbS2,,S/U denote the state spae of a stationary Markov hain with K states. State's spae S is that of K different hannel states with orresponding SNR thresholds, Ik, in inreasing order [6]..,.., r:...(5) O =.1 0 <.<J;<.. :<4.<E=oo The hannel is in state k, if the reeived SNR is between rk and Ik+l. s;fe [Fk,D+.] k=o,l,2,...,k-1; Considering the mobility of the nodes, their motion ofa ertain speed auses the Doppler frequeny,fm, then the number of times that the reeived signal rosses the given threshold, fk, in the positive or negative diretion only, is known as the level rossing rate oflevel fk and is given with ' 550

3 N(r J= /21Cr. f ex1_r.) t r m lr. (7) Thus the transition probabilities from state Sk, to state Sk+i, Pk,k+]' an be expressed as a ratio ofthe level rossing rate at threshold Ik+i, and the average number of signal segments per seond staying in state Sf<- The transition Rrobabilities an be approximated as _ N(rk+I) kl-, _ k-l,2,...,k-l, +,k (8) P,. -I =N(;JT p, k =2,3,...,K...(9) t,t Consequently knowing the transition probabilities the steady state probabilities an be alulated as _{I=P',k+1 - P.,k-I',k - 1,1' 1- P K,K-l' P = [p.l C I,} K+l)x(K+l) if0< k< K if k= 0 ifk=k. (10) (11) where P is the transition matrix ofthe FSMC model. Now our attention will be given to the realization of wireless hannels, first onsidering the simplest ase of a hannel having only two states, and observing the errors generated by this hannel. The generation of an error depends upon a threshold level, whih is the probability of generating error. If the probability is less than the threshold, the error probability an be negleted. If the probability is higher than the threshold, then it is assumed that the error is generated. This is known as simplified two state Markov hain hannel model or Gilbert-Elliot model. In this model the soure has two states: G (for good or no errors) and B (for bad or burst errors). PGGD Good P GS Figure 2. Two State Markov model ofa hannel The hannel is assumed to either be in a "Good" state or in a "Bad" state during a paket transmission. We onsider the situation in whih the suess of a paket transmission in a given state is determined by omparing the reeived SNR, to a threshold; above the threshold the paket is deoded suessfully with probability 1, otherwise it is lost with probability 1. Gilbert-Elliot model is basi model and is not enough for modeling ofthe wireless hannel in most ases. Hene we onsider a more omplex ase where the hannel onsists of more than two states. Eah state an have a different threshold level, and depending on a given PSS threshold, we assoiate an error probability with that state. In our approah we use three state Markov hain model, where there are two good states, Gl and G2 and a single bad state, B. This gives us an insight for modeling wireless hannels more aurately. If we onsider that the probability of transition from state Gl to state B and vie versa is very low, we an represent this with the flow diagram given below. PH P21 P12 op22 P32 :G2 Jp33 Figure 3. Three-State Markov model ofa hannel Where the transition probabilities are given with the following matrix: PH P 12 P _ [PGIGI PGIGI p= P21 P 22 P 23 - PG2G1 PG2G2 P23 1- PG'G- PGlGl] P 31 P 32 P P RB P RR We onsider the situation in whih the suess of a paket transmission in a given state is determined by omparing the reeived SNR, to the thresholds in eah state, eah ofwhih has ertain paket error probability. In a physial layer model our point is to alulate the symbol error rate whih in a wireless network is a funtion ofsnr. Ifbinary FEC is used then it may not be pratial to determine the symbol error rate, sine the error orretion is performed at the bit level. Then the physial layer model alulates the bit error rate after FEC. Hene the bit error rate, BER, in eah state an be obtained from a seleted modulation sheme. Sine we have used BPSK modulation sheme, aordingly, the BER, the probability of a symbol error for a given SNR threshold, r, an be alulated from p(r) =1-<I>(-J2f), r <I>(r) =J e 2 dt -00\/2" (12) (13) Where 4>(I) is normal Gaussian funtion. Given that we know the average BER and assuming that the bits in a paket an be onsidered to be independent from one another, the paket error rate, PER, an be expressed as a funtion of equivalent BER and the number ofonsisting bits. L is the paket length in bits. PER =1-(I-BER)L (14) If a paket is reeived in error, it is being retransmitted in a link basis. Ifwe denote with PERthresh, the maximum tolerable PER for an appliation, then the probability of paket reeived orretly after k retransmissions an be determined with P = 1- PEresh PEI1 =(PER)k+l reesh (15) (16) 551

4 The value of SNR threshold is not hosen arbitrarily, it is a funtion of the maximum symbol error probability of the hannel that our appliation and protool an assume. It was shown that the SNR is related to the paket length [7]. But SNR threshold using the multipath fading hannel is onsiderably different from the additive white Gaussian noise (A WGN) hannel [8]. Given the parameters in our senario, first we alulate average reeived SNR and then selet appropriate SNR threshold for the speified paket error rate. DSR with Cross Layer Design (CLD) We implement the physial parameter aware routing metri by modifying the dynami soure routing (DSR) [9] protool. We demonstrate the benefits of inter layer interations in low mobility senarios, applying the routing metri to route disovery and the route maintenane. Route Disovery. To perform a route disovery, if the soure node doesn't have the valid route in its ahe, it starts with the broadasting of a route request (RRE paket that ontains the address of the initiator, the address of the target, the request identifier, and the route reord. Besides the information required for DSR suh as the destination address and the sequene number, the node inludes a physial layer parameter whih indiates the quality of the path. Here, we are using SNR as the physial layer parameter. In the route reord field, eah node appends its address as a hop sequene only if: it did not detet a dupliate RREQ and the value of the SNR is higher than the required threshold. So whenever a node reeives a RREQ paket, the physial parameter -SNR is determined and ifthe value of SNR is less than the required threshold, then the RREQ paket is disarded. If the above riterion is satisfied and the node is not the target it propagates the route request again. When the destination node reeives the RREQ message, it initiates the route reply (RREP) paket bak to the soure node. This route reply ontains the list of nodes along the path from soure to destination. Sine SNR is used as riteria in the seletion of the routes, we have to ensure that during the route maintenane phase, all existing routes meet the riteria of exeeding SNR threshold, otherwise suh route is invalid. In this ase, when a RREQ arrives at a node that has a ahe, we have to propagate the RREQ paket along the ahed route to verify that the SNR threshold is met. When the RREP reahes its destination the soure node begins using this route for delivery ofth paket, whih at the same time meets the QoS riteria set by the SNR-threshold. s Figure 4. RREQ messages during the route disovery with Cross-Layer parameter (SNR«link with SNR lower than SNR thresholdl) Route maintenane. Route maintenane is DSR's standard operation mode. While in route maintenane, DSR routes data pakets using the soure route. On reeiving a data paket, a node uniasts the paket to the node listed as the next hop in the soure route. Ifthe link to the next node is broken, the node deteting the failure sends a route error paket bak to the sender. Nodes overhearing the route error paket invalidate entries in their routing ahes as needed. Upon reeiving the route error paket, the sender attempts to find a new route to the destination node in its ahe, and if none is found, swithes to the route disovery mode. Even though DSR has the maintenane mehanism, it is applied only during the paket transmission. To maintain the route "freshness", in this approah during the route reply, eah node will keep trak of its first neighbor from whih it has reeived the RREP message for the first time.. Periodially this node will send some kind of "Hello messages" to its first neighbor along the path. This node replies with ACK but only when the link status is in state "Good!", whih is determined by the SNR threshold!. On the other hand if SNR value is less than SNR threshold!, but greater than SNR threshold2, the node replies with WARN message, whih determines the state "Good2" ofthe link. When a node reeives a WARN message, implies that the link is going down, therefore the soure within a ertain period of time ould swith to an alternate route, ifit exists or to start a new route disovery proess. The absene of the ontrol message within a ertain period of time, whih is determined with TIME EXPIRY means that it has to inform the nodes in its upstream link sending the RRER message. When RRER message reahes the soure node, it updates its ahe and deletes the existing path with the broken link. TIME_EXPIRY is determined as the mean sojourn time that the hannel remains in the "Bad" state. T =...(17) B 1-1JB Where, T p is paket transmission time. D 552

5 Table 1. The parameters values and the simulation senarios used in our simulations Parameters Value Modulation Sheme DSSS, BPSK Traffi rate 11 Mbps Radio Tx Power W Mobility model Random-Waypoint Propagation -Path loss Two-Ray Propagation fading model Rayleigh, Riian MAC protool Paket size 512 bytes Routing protool DSR Carrier Frequeny 2.4 GHz Terrain dimensions 4000X4000m Simulation time 3600 s Nodes number 80 Traffi FTP SNR Thresholds 8.5[dB] 5.5[dB] Transmission Range 1000m Speed 3 mls Simulation results The ross-layer algorithm desribed above was implemented and evaluated in OPNET v 11 [10]. We present results using the senario investigating the performane benefits of DSR with CLD in wireless mobile environment. All the nodes in the network are onfigured to work under ad ho mode. We used the IEEE Wireless LAN model with the ad ho network onfiguration. A network of size (4000 x 4000) m2 was hosen, but the size ofthe network is not restrited. The nodes in our senario are mobile but the position ofwireless nodes is arbitrarily hosen. The mobility assigned to eah node during simulation within OPNET is an important fator in the performane of the protool. Eah node is assigned a trajetory, whih is generated from the traffi simulator. This will provide realisti node movement. Mobility of a mobile node generates a doppler shift, whih is a key parameter offading hannel. A three-state Markov model was used for the links on a path. The SNR thresholds are hosen from the paket loss probabilities ofthe states. We simulated the traditional DSR routing protool and DSR with CLD sheme in mobile environment. We ompared their performane observing the average endto-end delay, the routing overhead, and the average throughput vs simulation time. One ofthe primary objetives of an ad-ho routing protool is to maximize energy effiieny whih an be measured by parameters suh as routing overhead. Figure 5 shows the omparison ofrouting overhead for two state and three state hannel modeling when using DSR and modified version ofthis protool. Clearly, DSR with CLD outperforms DSR allowing routes to be attained with signifiantly lower overhead sine it is looking for more reliable paths. The nodes disard the RREQ messages if the SNR value is less than the requirement. Although the maintenane mehanism of the modified DSR needs periodial broadast of HELLO messages, this ontrol pakets do not inrease the routing overhead ompared to the retransmitted data pakets, whih have signifiant larger paket size, sine they arry the entire route in the header. Errors introdued by a wireless medium are more frequent and profound than ontemporary wired media. Some of these errors, whih are not orreted by the physial layer, result in Medium Aess Control (MAC) layer bit errors and paket losses. Design of wireless protools and appliations an benefit substantially from a thorough understanding of these MAC layer destrutions. In Figure 6, the MAC layer data dropped is presented. We show that when using the ross layer optimization and three state Markov model, there is a visible improvement with respet to MAC layer data loss. Average end-to-end delay of data pakets inludes all possible delays in the nodes and the links. As it is illustrated in Figure 7, DSR has inreased delay due to the buffering delays of route reoveries and the retransmission delays at the link layer. DSR with CLD leads to better end-to-end delay performane beause the nodes aquire suffiient information from physial layer to onstrut more reliable paths. The paths built from these links redue the probability of failure, espeially in the mobility senario, thus improving the end-to-end delay. The mobile ad ho networks were used to ollet data and transfer pakets. The throughput of pakets transmitted is one of the parameters to evaluate the networks effiieny. Observing from Figure 8 ross layer optimization of DSR made visible improvement of average throughput, whih outperforms traditional DSR. When the hannel onditions are poor retransmissions result in higher delays and this leads to redued throughput. Exhanging the information between physial layer and network layer, the network layer may adapt itself, seleting the most reliable paths, and thus ahieving higher throughput. Conlusion In this paper we applied a ross-layer design onept using the physial layer parameters as one ofthe metris to the routng algorithm. We have applied this tehnique to the DSR protool and showed the improvements in the various routing protool. performane parameters. Based on our observations, we onlude the following: The two state Markov model, known as Gilbert-Elliot model whih was widely used in the researh to evaluate the ommuniation system is not enough for modeling a real hannel. By applying the 553

6 higher order of Markov hains, it is possible to implement a more realisti hannel. However, inreasing the number of states inreases the omputational omplexity. Consequently we used a three state Markov model of the wireless hannel as a trade off between omplexity and the performane. Our results indiate that the ross-layer design using three state Marov hain provides a better improvement in the routing protool performane when ompared to the traditional DSR routing protool or the two state Markov hain model U e "C u 600 S ns.. oc "'C e 400 > CL 0) ::s ;; J! C'G 5000 D:: 200 i UJ Simulation Time (s e) Figure 5. Routing Overhead vs Simulation Time Simulation Time (se) Figure 6. Wireless LAN Data Dropped vs Simulation Time u 2500 CD 2000 ;.. :s :s 0) 1500 ::::I e t= 1000 CD 0) l! 2 o n'b'b ro o.. '".v "oj v f\ b? ro 0/ 9>'" 9:J ro'b ro 500 o Simulation Time (se) Figure 7. Average End-to-End Delay vs Simulation Time Simulation Time (se) Figure 8. Average Throughput vs Simulation Time 554

7 Referenes 1. Wing Ho Yuen Heung-no Lee Timothy D. Andersen "A Simple an Effetive Cross Layer Networking System for Mobile Ad Ho Networks", in: PIMRC 2002, vol. 4, September 2002,pp Kanodia, V.; Sabharwal, A.; Knightly, E. "MOAR: a multi-hannel opportunisti auto-rate media aess protool for ad ho networks", Broadband Networks, BroadNets Proeedings Page(s): Ayta Azgin and Marwan Krunz "Impat of Channel Modeling on the Performane of Wireless Sheduling Shemes", VTC 2003 Fall.lEEE 58th Volume 3, Issue, 6-9 Ot Page(s): i-iii 4. Muhammad Tahir, Sudip K. Mazumder, "Markov Chain Model for Performane Analysis of Transmitter Power Control in Wireless MAC Protool: Towards Delay Minimization in Powernetwork Control", AINA apos;07. 21st International Conferene on Volume, Issue, May 2007 Page(s): Wisitpongphan, N., Ferrari, G., Panihpapiboon, S., Parikh, J. S. and Tonguz, o. K. (2005), "QoS provisioning using BER-based routing for ad ho wireless networks", Pro."-'IEEE Vehiular Teh."-'Conf. (VTC Spring 05): Qinqing Zhang, Saleem A. Kassam, "Finite-State Markov Model for Rayleigh Fading Channels", IEEE transations on ommuniations, vol. 47, no. 11, november Wei Liu and Yuguang Fang, "Courtesy Piggybaking: Supporting Differentiated Servies in Multihop Mobile Ad Ho Networks" INFOCOM 2004, (Vol. 3, No.4) pp ' 8. Sayantan Choudhury and Jerry D. Gibson "Joint PHY/MAC Based Link Adaptation for Wireless LANs with multipath fading", IEEE Wireless Communiations and Networking Conferene, Las Vegas, USA, 3-6 April D. B. Johnson, D. A. Maltz, and Y-C Hu., "The Dynami Soure Routing Protool for Mobile Ad Ho Networks (DSR)",. IETF Mobile Ad Ho Networks Working Group, Internet Draft, work in progress, 24 February The OPNET simulator Sanhez-Salas, D.A Cuevas-Ruiz, J.L. "N-states Channel Model using Markov Chains" DOl /CERMA Muhammad Tahir, Sudip K. Mazumder, "Markov Chain Model for Performane Analysis of Transmitter Power Control in Wireless MAC Protool: Towards Delay Minimization in Powernetwork Control" AINA Pages: Year of Publiation: Sumantra R. Kundu Kalyan Basu Sajal K. Das "Finite State Markov Model for Effetive Bandwidth Calulation in Wireless Paket Networks" Proeedings of the Third International Symposium on Modeling and Optimization in Mobile, Ad Ho, and Wireless Networks table of ontents Pages: Year of Publiation: Shunan Lin, Shiwen Mao, Yao Wang, Shivendra Panwar "A Referene Piture Seletion Sheme For Video Transmission Over Ad-Ho Networks Using Multiple Paths" Proeedings of ICME, August Xinsheng Xia, Qilian Liang And Qinghun Ren "Bottom-Up Cross-Layer Optimization For Mobile Ad Ho Networks" MILCOM 2005 IEEE (2005), pp Vol Ranurel D. Roviras, J. Conan, And F. Castanikl "Effet OfInterleaving On A Markov Channel T". ICASSP 2000 Volume 5, Issue 2000 Page(s): Syed A. Khayam And Hayder Radha "Markov Based Modeling Of Wireless Loal Area Networks" MSWiM 2003, SESSION: Emerging tehnologies: WLANs and WPANs Pages: The views and onlusions in this doument are those of the authors and should not be interpreted as representing the offiial poliies, either expressed or implied, of the Army Researh laboratory or the U.S. Government. 555