Research Article MIMO Techniques for Jamming Threat ...Research Article MIMO Techniques for Jamming...

Research ArticleMIMO Techniques for Jamming Threat Suppression inVehicular Networks

Dimitrios Kosmanos1 Nikolas Prodromou1 Antonios Argyriou1

Leandros A Maglaras2 and Helge Janicke2

1Department of Electrical amp Computer Engineering University of Thessaly Volos Greece2School of Computer Science and Informatics De Montfort University Leicester UK

Correspondence should be addressed to Leandros A Maglaras leandrosmaglarasdmuacuk

Received 15 April 2016 Accepted 9 October 2016

Academic Editor Candelaria Hernandez-Goya

Copyright copy 2016 Dimitrios Kosmanos et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Vehicular ad hoc networks have emerged as a promising field of research and development since they will be able to accommodatea variety of applications ranging from infotainment to traffic management and road safety A specific security-related concernthat vehicular ad hoc networks face is how to keep communication alive in the presence of radio frequency jamming especiallyduring emergency situations Multiple Input Multiple Output techniques are proven to be able to improve some crucial parametersof vehicular communications such as communication range and throughput In this article we investigate how Multiple InputMultiple Output techniques can be used in vehicular ad hoc networks as active defense mechanisms in order to avoid jammingthreats For this reason a variation of spatial multiplexing is proposed namely vSP4 which achieves not only high throughput butalso a stable diversity gain upon the interference of a malicious jammer

1 Introduction

Vehicular ad hoc networks (VANETs) have emerged as apromising field of research [1 2] where advances in wirelessand mobile ad hoc networks can be applied to real-lifeproblems (traffic jams fuel consumption pollutant emis-sions and road accidents) Vehicles may utilize a variety ofwireless technologies to communicate with other devicesbut the dominant is Dedicated Short-Range Communication(DSRC) [3] which is designed to support a variety ofapplications based on vehicular communications VANETsare currently the center of attention for car manufacturerstechnology companies and transportation authorities Thebasic idea behind vehicular communications is to helpbroaden the range of perception of the driver and help withautonomous assistance applications

VANETs can be considered as mobile ad hoc networkswhich are utilized to enhance traffic safety and provide com-fort applications to drivers The unique features of VANETs

include fast-moving vehicles that follow predetermined paths(ie roads) though having high diversity of mobility patternsalong with messages that have different priority levels Forexamplemessages for comfort and infotainment applicationshave lowpriority whilemessages for traffic safety applicationsrequire timely and reliable message delivery [4] HybridVANETs can accommodate vehicle-to-vehicle (V2V) andvehicle-to-infrastructure (V2I) communicationThis enablesseveral other forms of communication such as vehicle-to-broadband cloud (V2B) where the vehicle communicateswith a monitoring data center and vehicle-to-human (V2H)to communicate with vulnerable road users for examplepedestrians or bicycles [5] Except from uninterrupted andreliable connectivity one of the major issues that VANETshave to face is security [6]

As cars become more interconnected one of the mainchallenges that manufacturers have to face is security Par-ticularly for safety applications where early warning of thedriver is crucial it is essential to ensure that life-critical

Hindawi Publishing CorporationMobile Information SystemsVolume 2016 Article ID 8141204 9 pageshttpdxdoiorg10115520168141204

2 Mobile Information Systems

information cannot be modified or dropped by an attackerVehicular security threats target all the three major compo-nents of security confidentiality integrity and availability(CIA) [7] A specific security-related concern that VANETsface is maintaining communication during an RF jammingattack As reported in [8] it is proven that constant periodicand reactive RF jamming has significant impact on vehicularcommunications through extensive measurements in ananechoic user Specifically in [8] the impact of the reactiondelay and interfering signal duration on the effectivenessof the reactive jammer is also quantified Hence jamming-aware communications protocols and applications as wellas effective jamming detection and reaction strategies are ofgreat need

Regarding jamming in VANETs the main purpose ofprevious works was to analyze threats and focus on the effectsof RF jamming [9 10] Most of previous works deal withthe early and correct detection of malicious nodes [11] ordevelop some techniques that use frequency hopping [12] inorder to find an interference-free channelThesemethods aretoo complicated to be implemented in a real environmentespecially when more sophisticated jamming attacks have tobe addressed (eg a reactive jammer) Also usually in RFjamming attacks all communication channels are blocked andtechniques like frequency hopping do not have any positiveeffect

2 Motivation

To combat RF jamming effectively in this paper we proposethe use of MIMO MIMO systems although thoroughlyinvestigated are mostly focused on how to improve someparameters of vehicular communications for example com-munication range and throughput Previous work mainlyfocuses on explaining the benefits of using MIMO inVANETs [13] and examining propagation models [14 15]and OFDM-based MIMO systems [16] Previous works didnot study MIMO systems as an active defense mechanismthat overcomes different types of RF jamming attacks Onlyrecently active antijamming MIMO-based techniques wereintroduced Authors in [17] present a MIMO-based anti-jamming technique that uses prerotation or beamformingof the jamming signal in order to improve sender signaldecodability The method is difficult to be implemented in aVANET scenario since the channel conditions are changingvery frequently and multiple pilots must be used by thesender and the jammer for real-time channel tracking Inanother work in [18] the authors proposed a cooperativeinterference mitigation scheme combined with MIMO forjamming suppression This method is based on the channelinformation ratio which is provided from the probing ofthe channel In VANETs the frequently changing channelwould generate a large number of probes overloading thechannelThe authors in [19] useMIMO and interference can-cellation in order to support communication in the presenceof strong interference However only random interferenceis considered and the proposed method is not tailored toreactive RF jammers Last in [20] an improved MIMOchannel estimation for interference cancellation is exploited

to combat reactive jamming However a quite complicatedmethod based on Kalman filter and basis expansion model(BEM) with a large number of iterations for convergence isused to track the channel of the jammer making the methoddifficult to be employed in real situations

This article investigates MIMO systems for improvingrobustness in RF continuous and reactive jamming threatsand simultaneously achieving higher throughput rates inVANETs In this paper the MIMO scheme with instanta-neous Channel State Information (CSI) per received packetat the receiver and without knowledge for the channel ofjammer is used We show that using MIMO the suppressingof the jamming signal can be successful without using ajamming detection phase and regardless of the type andstructure of the jamming signal Our proposed schemenamed vSP4 which combines the Alamouti scheme withspatial multiplexing (SM) [21] nearly doubles the throughputand also decreases the silence time almost by a factor of twowhen compared to the classic cSP4 scheme in the presenceof a malicious jammer Another contribution of this paper isa new framework for VANET simulations which combinesthree known simulators for obtaining more realistic results

3 System Model

31 Simulation Framework For evaluating the proposeddefense mechanisms the VEINS simulator is used [22]This open-source framework consists of two well-knownsimulators OMNET++ an event-based network simulatorand SUMO a road traffic simulator Furthermore insteadof using the existing PHY layer of OMNET++ the GEMV(a geometry-based efficient propagation model for V2V)[23] tool was integrated into the VEINS network simulatorGEMV calculates a propagation model that separates linksinto line-of-sight (LOS) and non-LOS (NLOSv and NLOSb)link types and calculates deterministically the large-scalesignal variation (ie path loss and shadowing) for eachlink type Furthermore GEMV employs a simple geometry-based small-scale signal variation model that calculates theadditional stochastic signal variation based on the informa-tion about the surrounding objects GEMV was configuredand modified to be portable to the VEINS simulator andincorporated into this Figure 1 illustrates the instantaneousSNR versus distance calculated by integration of GEMV-VEINS compared with this calculated by VEINS simulatorGEMV uses a more detailed propagation model to calculatethe SNR which takes into account the ldquoqualityrdquo of the linkstaking into account the physical obstacles (eg building andcars) compared to simple log-distancemodel which is used inVEINS The proposed VEINS-GEMV integrated simulationframework allows more realistic simulations since the SNRis affected not only from the distance among vehicles butalso from the small-scale and the large-scale variations of thewireless medium

32 Channel Model and PHY Modulation For simulationsthe 80211p MAC and PHY parameters at 59GHz (10MHz)are used Please refer to Table 2 for details of specific param-eter values Also Rayleigh fading channels with Additive

Mobile Information Systems 3

GEMV versus VEINS SNR estimation

minus5

0

5

10

15

20

25

30

35

SNR

(dB)

50 100 150 200 250 300 350 4000Distance (m)

GEMV-VEINS estimationVEINS estimation

Figure 1 GEMV-VEINS SNR versus distance estimation

White Gaussian Noise (AWGN) () being stable during thetransmission of 10 symbols are assumed In our scenario10 packets per second are transmitted The average SNR iscalculated for each second For the transmission of 5 lowast 103symbols the modulation that was used in simulations isQPSK16-QAM and the data rates that were used are 3Mbpsfor packet header and 6Mbps for packet payload whichare currently supported by VEINS project A modulationand coding scheme (MCS) with 119898 bitssymbol is used bythe transmitter and the jammer while its optimal value isdetermined by each node independently For the evaluationof MIMO for suppressing jamming effects we use threedifferent MIMO schemes

33 MIMO Model For all the MIMO schemes we assumethat 119870 is the number of symbols which are transmitted inthe duration of 119879 time slots 119875119879 is the power of transmittedsignal and 1205902119899 is the uncorrelated equal noise power at thereceiver We also use forward error correction (FEC) codingat the transmitter assuming perfect instantaneous channelknowledge at the receiver Moreover 119899119877 is the number ofreceived antennas and 119899119879 is the number of transmittedantennas while the variable 119899119860 describes howmany antennasare used for sending multiple copies of the same symbol withthe MIMO schemes for increasing the diversity gain Weassume that ℎTxRx is the channel between the transmitter (Tx)and receiver (Rx)The systemswe test are the following a 2times2Alamouti scheme in Section 41 a 2times2 SM in Section 42 and

an enhanced 4 times 4 combination of Alamouti scheme and SMscheme in Section 43

4 The Proposed Defense System

41 Classic Alamouti Algorithm One of the most populartechniques for improving reliability in MIMO systems is theAlamouti Space-Time Block Coding (STBC) technique [24]STBC is a technique used in wireless communications totransmit multiple copies of a data stream across a numberof antennas to improve reliability Alamouti requires at least2 transmit antennas [21] It does not improve throughputin terms of absolute numbers but achieves significantlylower Bit Error Rate (BER) With Alamouti 2 symbols aretransmitted orthogonally as illustrated in Table 1 We use a2 times 2 MIMO Alamouti scheme in which (119870 = 2) a numberof symbols are transmitted in the duration of 119879 time slots(119879 = 2)

Due to orthogonal transmission with Alamouti thetwo transmitted symbols do not interfere with each otherEach symbol is communicated over a different independentchannel realization improving the overall system reliabilityThe received signal can be written as

=[[[[[[

11991011119910lowast2111991012119910lowast22

]]]]]]=[[[[[[

ℎ11 ℎ21ℎlowast21 minusℎlowast11ℎ12 ℎ22ℎlowast22 minusℎlowast12

]]]]]][11990611199062] + (1)

In the above equation 11991011 and 11991012 denote the receivedsymbols at antenna element number 1 and number 2 atthe first time slot 1198791 (Table 1) and similarly 119910lowast21 and 119910lowast22represent the received symbols at antenna element number 1and number 2 at the second time slot 1198792 Using the diversity-multiplexing tradeoff (DMT) [25] we can see that the ratefor symbols sent with the 2 times 2 MIMO Alamouti scheme is119903 = 119870119879 = 1 symbolstime slots and the diversity gain is119889 = 1 So the DMT is (1 1)

Decoding with Maximum-Ratio Combining (MRC)combines signals using a weight factor in order to achievehigher average SNR [21] If we have

119867 =[[[[[[


]]]]]]

(2)

and the inverted matrix product is

(119867119867119867)minus1 = [[[[

11003816100381610038161003816ℎ1110038161003816100381610038162 + 1003816100381610038161003816ℎ2110038161003816100381610038162 + 1003816100381610038161003816ℎ1210038161003816100381610038162 + 1003816100381610038161003816ℎ2210038161003816100381610038162 00 11003816100381610038161003816ℎ1110038161003816100381610038162 + 1003816100381610038161003816ℎ2110038161003816100381610038162 + 1003816100381610038161003816ℎ1210038161003816100381610038162 + 1003816100381610038161003816ℎ2210038161003816100381610038162

]]]] (3)


Table 1 Alamouti scheme

Tx (antenna) Idtimeslot 1198791 1198792Tx1 1199061 1199062Tx2 minus119906lowast2 119906lowast1

Table 2 Simulation parameters

Parameter ValueTransmitter power 1748 dBmJammer power 1675 dBmPacket generation rate (packetss) 10Simulation symbols number 5000Data rates in experiments 6MbpsPacket payload 400 B

the proposed signal is = 119867119867 (119867119867119867)minus1 997888rarr119910 = [1 0

0 1] [11990611199062] + (4)

Because MRC decoding is used as the number ofreceived antennas is increased the overall performance isalso improved Finally after calculating the throughput of theAlamouti scheme we see that the instantaneous capacity is

119862Alamouti = 119870119879 log(det(119868 + 1198751198791199031205902119899119899119879 (119867119867119867))) (5)

From the above equation we can conclude that the capacityof the Alamouti scheme depends on the rate of the symbolsthat are transmitted in each time slot (ie 119903 = 119870119879)Consequently if the rate with Alamouti increases then thecapacity of this scheme is also increased Finally for the2 times 2 MIMO Alamouti scheme the capacity is 119862Alamouti ge119862SISO where SISO is a Single (antenna) Input Single (antenna)Output scheme

42 Classic SpatialMultiplexing Themethodwhich offers thehighest throughput is SM The reason is that each antennatransmits a different symbol during each time slot So in caseof 2 4 or119873 antennas in general the throughput is doubledquadrupled or increased by 119873 times respectively Howeverin poor channel conditions SM achieves low SNR and veryhigh BER The MIMO channel with SM is

= 119867 + (6)

By applying Least Squares Equalization to the channel matrix119867 we have to multiply with the pseudoinverse matrix

119867dagger = (119867119867119867)minus1119867119867 (7)

The sufficient statistic that is used for detection is then

119903 = (119867119867119867)minus1119867119867 = 119909 + (119867119867119867)minus1119867119867 (8)

which is also known as the zero-forcing method

For a 2times2MIMO SM scheme the received signals whichare reached at antenna 1 and antenna 2 can be written as

119910119895 =2sum119894=1

ℎ119894119895119909119894 + 119908119895 119895 = 1 2 (9)

From the above equation we notice that the received copiesof the symbols 1199091 and 1199092 are 2 So the multiplexing gain ofthe SM using the DMT is 2 while the diversity gain is 0 (2 0)

Calculating the capacity of SM scheme we have

119862SM = log(det(119868 + 1198751198791205902119899119899119879 (119867119867119867)))

= min(119899119877119899119879)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (10)

In the above equation 1205822119894 are the eigenvalues of (119867119867119867)matrix [21] For our 2 times 2 MIMO example compared withthe capacity of Alamouti scheme with SM we can concludethat 119862SM = 2 lowast 119862Alamouti in the high SNR regime

43 Enhanced Version of Spatial Multiplexing In this workthe classic version of SM is enhanced for our particularapplication with a combination of SM and Alamouti Morespecifically users may choose a slower but more reliabletransmission technique by selecting how many differentsymbols will be transmitted in each time slot The remainingantennas repeat these symbols achieving higher probabilityof successful decoding For example in a 4times4MIMO systemwith classic SM 4 symbolswould be transmitted per time slotIn our system 119903 = 2 symbols per time slot are transmittedin order not only to double the maximum throughput butalso to provide a more robust communication by increasingthe probability of successful decoding by a factor of 2 So theDMT for this (vSP4) scheme is (2 2) where the diversity gainis 119889 = 2 In our system in order for two symbols (1199091 1199092)to be transmitted each odd numbered antenna transmits1199091 symbol and all the even numbered antennas transmit 1199092symbol So the received signals for our 4times4MIMOenhancedversion of SM are

119910119895 =2sum119894=1

(ℎ119894119895119909119894) + ℎ31198951199091 + ℎ41198951199092 + 119908119895 119895 = 1 4 (11)

TheDMT for this 4times4MIMO SM variant (vSP4) is (2 2)while the DMT for the 4 times 4 classic SM scheme is (4 0) Thecomparison of the diversity gains and multiplexing gains forthe 4 times 4MIMO Alamouti and SM schemes is

Diversity(vSP4) = 2 lowast Diversity(Alamouti)Multiplex(SM) = 2 lowastMultiplex(vSP4)

= 4 lowastMultiplexAlamouti(12)

From the above equations it is obvious that using the vSP4scheme we increase the diversity gain by a factor of 2and decrease the multiplexing gain by a factor of two too


compared with the classic 4 times 4 SM MIMO scheme Thecalculations of the capacity of the proposed communicationscheme lead to

119862vSP4 =min(119899119879119899119860119899119877119899119860)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (13)

In the above equation the new 4 times 4 channel matrix 119867is used and 1205822119894 are the eigenvalues of (119867119867119867)matrix [21] Wealso use (119899119877 = 119899119879 = 4 119899119860 = 2) and min(119899119879119899119860119899119877119899119860) = 2Compare the capacities of the schemes vSP4 (119862vSP4) 2times2 SM(1198622times2SM) 4times4 SM (1198624times4SM) and a 2times2Alamouti (1198622times2Alamouti)scheme in which 2 symbols per 2 time slots are transmittedassuming ideal channel conditions between Tx and Rx for allthe schemes

119862vSP4 = 1198622times2SM = 1198624times4SM2 gt 1198622times2Alamouti (14)

Consequently vSP4 is a method that almost doubles thediversity increases the reliability compared with the classicSM scheme and also decreases the overall throughput of thesystem

Practical Considerations The proposed defense system isbased onMIMO signal processing techniques MIMO enjoyswidespread applicability in most wireless systems todayHence our proposed system is amenable to practical real-time implementation and operation without affecting otheraspects of the wireless transmission system Furthermorethere is no need for additional algorithms or processingbesides the MIMO receiver processing

5 Performance Evaluation

Methods Compared In order to evaluate the performanceof the proposed defense mechanism vSP4 we compare itwith the 2 times 2 MIMO classic version of SM (cSP2) and the4 times 4MIMO classic version of SM (cSP4) We also compare a2 times 2MIMO Alamouti (STBC) technique with a classic SISOsystem and with a 2 times 2 classic SM scheme

Performance Metrics As performance metrics we used thethroughput versus SNR the throughput versus time (silencetime) the throughput versus distance (silence range) thethroughput versus SNR and the PER (Packet Error Rate =PacketslostPacketssent) versus time Silence time is the timeduration of the complete disruption of communication dueto strong jamming while silence range is the range in metersin which the communication is impossible It is importantto note that in our throughput results we exclude of coursepacket losses in order to ensure that we measure the actualvolume of successfully communicated data per second in thepresence of a jammer In this paper we do not investigateadditional algorithms like packet retransmission (ARQ) orforward error correction (FEC) which can be employed at thePHY or the link layerThese schemes are well known andwellinvestigated and they are distracted from the main idea ofthe simulation which is the use of MIMO signal processingfor enjoying throughput improvements in the presence of awireless jammer

51 Simulation Setup For our experiments we used theparameters of the real experiments that were conducted in[8] More specifically the same road in the outskirts of thecity of Aachen as shown in Figure 3 was used Several otherparameters that are illustrated in Table 2 are also tuned inorder to better represent the scenarios of the real experimentsconducted in [8] The side road in which the jammer (Jn)is located is also the same For our evaluation scenarios Rxfollows Tx keeping a constant distance The first time stepsand the last time steps of our simulation can bemapped to thedistances about 150m between Rx and Jn and Jn approachesthe pair Tx-Rx at about distance of 5m at the middle (70 sec)of the simulation increasing strongly the jamming effectAlso in Section 53 (Experiment 2) we evaluate the use ofa reactive jammer with 119879detection = 12 120583s and 119879duration = 84 120583sat the standard of [8]

52 MIMO Defense Mechanism (Experiment 1) To highlightthe negative effects that a jammer induces in vehicularcommunication and how the MIMO techniques effectivelysuppress these effects we compare the performances ofMIMO techniques for short and long distanceswithin the Tx-Rx pair Also Figures 2(c) and 2(d) demonstrate the silencetime of communication which is caused by the presence of ajammer in the side road

As expected while Tx-Rx distance increases from 20mto 100m the RF jamming impact also increases dramati-cally as seen in Figures 2(c) and 2(d) Also the improvedperformance and the benefits of the MIMO system whencompared to the SISO system are significant For shortdistances where there is the least impact in communicationthe Alamouti technique manages to suppress the silencerange of the RF jamming threat from the distance above 10m(see Figure 2(a)) As we can also see in Figure 2(c) the silencetime of communication is reduced to only a few secondsby using the Alamouti technique For intervehicle distancesof 100m the silence range is 20m which is almost doublecompared to the silence range for intervehicle distancesof 20m This situation is also graphically represented inFigure 3 Silence range extends considerably for the other twotechniques 35m for SISO and 75m for SM

In time domain communication with the Alamoutitechnique is affected for a duration of 20 s for intervehicledistance of 100m (see Figure 2(d)) while for intervehicledistance of 20m the disruption of communication is onlyabout 2 s (see Figure 2(c)) On the other hand using SMscheme the communication is affected for about 10 s fordistance of 20m and the corruption of communication isdramatically increased at 30 s for distance of 100m

The first main conclusion from these figures is the stableperformance of the Alamouti scheme for all the possibledistances of Rx-Jn and Tx-Rx and the elimination of thejamming effect for intervehicle distances lower than 20mwith the presence of a jammer 20m away at least from thereceiver Furthermore besides throughput we are also inter-ested in higher reliability of the system under the presenceof malicious jammers Notably for emergency situations itis very important for the silence range to be very low Forthis reason an interesting result of our simulation study is


50 100 1500Distance (m)

0

2

4

6

8

10

12Th

roug

hput

(Mbp

s)

SISO AlamoutiSM

(a)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 100 1500Distance (m)

SISO AlamoutiSM

(b)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 60 70 80 90 10040Time (s)

SISO AlamoutiSM

(c)

50 60 70 80 90 10040Time (s)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

SISO AlamoutiSM

(d)

Figure 2 Experiment 1 results Throughput of 2 times 2MIMO system (a) Throughput to Rx-Jn pair distance Tx-Rx pair distance = 20m (b)Throughput to Rx-Jn pair distance Tx-Rx pair distance = 100m (c)Throughput to time Tx-Rx pair distance = 20m (d)Throughput to timeTx-Rx pair distance = 100m Payload data rate = 6Mbps (4-PSK FEC = 12) packet payload = 400 B

Figure 3 Experiment 1 graphical representation Graphical repre-sentation of silence range blockage line

that SM achieves the best throughput in jamming-free areasbut the worst silence range (time) in areas where jammingexists

53 Reactive Jammer (Experiment 2) To evaluate the per-formance of a more intelligent jammer we implemented areactive algorithm The reactive jammer is designed to starttransmitting upon sensing energy above a certain thresholdWe set the latter to minus86 dBm as we empirically determined itto be a good tradeoff between jammer sensitivity and falsetransmission detection rate If the detected energy exceedsthe threshold during a certain time span (119879detection= 12 120583s)an ongoing 80211p transmission is assumed by the jammerand starts its transmission for a duration of 119879duration = 84 120583sThe reactive jammer is designed in order to achieve jammingthe header of 80211p frame from Tx to Rx

From Figures 4(a) and 4(b) we can see the PER of thetransmission between Tx and Rx with the presence of acontinuous jammer in Figure 4(a) and a reactive jammerin Figure 4(b) with the presence of an reactive jammer Fortime slots where the distance between Jn and Rx is quitelarge the performance of reactive jammer is lower thanthat of the continuous jammer mainly because the reactivejammer is not sensing the ongoing transmissions at thesetime slots At the small distances Jn-Rx it is obvious thatthe silence time for the MIMO Alamouti and SM is aboutthe same for the continuous and the reactive jammer ThePER of the continuous jammer is smaller than the PER


40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

PER

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(a)

PER

40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(b)

Figure 4 Experiment 2 results PER of continuous jammer and reactive jammer for 2 times 2 MIMO schemes of Experiment 1 Tx-Rx pairdistance = 100m Payload data rate = 6Mbps (4-PSK FEC = 12) packet payload = 400 B (a) PER to time (continuous jammer) (b) PER totime (reactive jammer)

0 5 10 15 20minus5SNR (dB)

0

5

10

15

20

Thro

ugpu

t (M

bps)

cSP2cSP4 vSP4

(a)

40 50 60 70 80 90 100Time (s)

cSP2

0

5

10

15

20

25Th

roug

hput

(Mbp

s)

cSP4 vSP4

(b)

Figure 5 Experiment 3 results Comparison of SM variants and higher-order MIMO Tx-Rx pair distance = 100m Payload data rate =6Mbps (4PSK FEC = 12) packet payload = 400 B (a) Throughput to SNR (b) Throughput to time

of the reactive jammer only for the SISO scheme at about90 sec This behavior is justified because our MIMO defensescheme does not use a detection phase of the jammer but usesthe multiple antennas continuously in order to suppress thejamming effects The main characteristic of reactive jammeris to avoid detection from the Rxrsquos CCA mechanism of the80211p protocol PHY Since we observed the same behaviorbetween the reactive jammer and the continuous jammerfor our MIMO schemes we will use the continuous jammerfor the rest of our experiments So we can assume that ourMIMO defense scheme suppresses all types of jammingThe ineffectiveness of reactive jammer compared with acontinuous jammer can also be seen for a platoon of vehiclesat Figures 19(a) and 19(b) of [8]

54 SMVariants (Experiment 3) Theresults of Experiment 1allow us to introduce the last set of experiments and morespecifically the use of a 4 times 4 MIMO system Alamoutirsquosperformance as described above can almost eliminate thesilence range for intervehicle distances about 20m for a

2 times 2 MIMO system On the other hand the SM schemeachieves significant throughput in jamming-free areas buthigher silence rangewhen used in areaswhere jamming existsfor the majority of the simulations So these final simulationsfocus on trying to identify the optimal tradeoff betweendiversity and spatial multiplexing gain by comparing SMvariants which were described in Sections 42 and 43 InFigures 5(a) and 5(b) the schemes are the following

(i) 2 times 2 MIMO SM (cSP2) transmitting 2 sym-bolstimeslot

(ii) 4 times 4 MIMO SM (cSP4) transmitting 4 sym-bolstimeslot

(iii) 4 times 4 MIMO SM variant (vSP4) transmitting 2symbolstimeslot

The first conclusion based on the simulation that weconducted is that the SNR gain of vSP4 method is significantcompared to the other two Figure 5(a) demonstrates howcSP4 provides better throughput compared to cSP2 and vSP4only for large SNR values


On the other hand using vSP4 the throughput is almostdoubled compared to cSP4 at the middle SNR values inFigure 5(a) In Figure 5(b) the throughput of the SM variantsversus time is presented It can be seen that as the distancefrom a jammer remains relatively short the optimal scheme isvSP4 achieving a throughput of 10Mbps When the jammeris removed from the effective zone of communication thebest solution is the cSP4 which achieves the best throughputfor 20Mbps when compared to the other schemes

The most interesting result in these figures is that vSP4doubles the throughput and significantly reduces the RFjamming silence range Our goal is to illustrate the needfor more complex advanced and full adaptive algorithmsthat will select dynamically the optimal version of SMdepending on the operating regime for example diversity orthroughput

Summarizing the results of Experiment 3 it is obviousthat as the distance from a jammer remains relatively shortthe best solution that combines better throughput and diver-sity is vSP4 presenting a stable throughput value at about10Mbps vSP4 also reduces the silence time at about 12 swhile for cSP4 and cSP2 the silence time is 30 s and 20 srespectively So while a higher-order SM system is used thethroughput is increasedwith good channel conditions but thenegative implication is that the silence range is also increasedin the presence of RF jammingThese results confirm the factthat the classic version of SM is not suitable for suppressingthe jamming effects

6 Conclusions

In this paper we proposed the use of MIMO to increasethe throughput and reliability in VANETs which experienceRF jamming attacks The first novelty of this paper is theintroduction of a new simulation framework that combinesthree different well-known simulators The first one is thetraffic simulator SUMO [26] the second is the networksimulator OMNET++ [27] and the third is the GEMV [23]a geometry-based propagationmodel that is integrated in theVEINS simulator [22]

The second contribution is a set of extensive simulationsthat represent real conditions We showed that the Alamoutischeme retains a stable performance despite the intervehicledistance Tx-Rx and the presence of a malicious jammerin very close distances Moreover we showed that it caneliminate completely the silence range for small intervehicledistances Last by conducting experiments using a reactivejammer in addition to a continuous jammer we showedthat the Alamouti scheme can suppress the jamming effectregardless of the type of jamming signal and that SM achievesthe best throughput in jamming-free areas but the worstsilence range (time) in areas where jamming exists

The third contribution of this paper is a new techniquewhich is a combination of the SM scheme and the Alamoutischeme namely vSP4 which not only achieves the through-put to be sustainable but also doubles the reliability from theclassic SM decreasing the silence time at the same time withthe presence of a malicious jammer

Our future work will focus on designing a dynamicfully adaptive scheme that will select the optimal MIMOtransmission mode depending on the total interference levelAlso we plan to use our novel simulation model [28] whichis able to handle secured messages in order to simulate morerealistic situations

Competing Interests

The authors declare that they have no competing interests

References

[1] H Hartenstein and K P Laberteaux ldquoA tutorial survey onvehicular ad hoc networksrdquo IEEE Communications Magazinevol 46 no 6 pp 164ndash171 2008

[2] M L Sichitiu and M Kihl ldquoInter-vehicle communicationsystems a surveyrdquo IEEE Communications Surveys amp Tutorialsvol 10 no 2 pp 88ndash105 2008

[3] Y L Morgan ldquoNotes on DSRC amp WAVE standards suite itsarchitecture design and characteristicsrdquo IEEECommunicationsSurveys and Tutorials vol 12 no 4 pp 504ndash518 2010

[4] S Al-Sultan M M Al-Doori A H Al-Bayatti and H ZedanldquoA comprehensive survey on vehicular ad hoc networkrdquo Journalof Network and Computer Applications vol 37 no 1 pp 380ndash392 2014

[5] S Mitra and A Mondal ldquoSecure inter-vehicle communicationa need for evolution of vanet towards the internet of vehiclesrdquo inConnectivity Frameworks for SmartDevices pp 63ndash96 SpringerBerlin Germany 2016

[6] L A Maglaras ldquoA novel distributed intrusion detection sys-tem for vehicular ad hoc networksrdquo International Journal ofAdvanced Computer Science and Applications vol 6 no 4 2015

[7] S Zeadally R Hunt Y-S Chen A Irwin and A HassanldquoVehicular ad hoc networks (VANETS) status results andchallengesrdquo Telecommunication Systems vol 50 no 4 pp 217ndash241 2012

[8] O Punal C Pereira A Aguiar and J Gross ldquoExperimentalcharacterization and modeling of RF jamming attacks onVANETsrdquo IEEE Transactions on Vehicular Technology vol 64no 2 pp 524ndash540 2015

[9] C Pereira and A Aguiar ldquoA realistic rf jamming model forvehicular networks design and validationrdquo in Proceedings ofthe IEEE 24th International Symposium on Personal IndoorandMobile Radio Communications (PIMRC rsquo13) pp 1868ndash1872London UK September 2013

[10] A M Malla and R K Sahu ldquoSecurity attacks with an effectivesolution for dos attacks in vanetrdquo International Journal ofComputer Applications vol 66 no 22 pp 45ndash49 2013

[11] K Verma H Hasbullah and A Kumar ldquoPrevention of DoSattacks in VANETrdquo Wireless Personal Communications vol 73no 1 pp 95ndash126 2013

[12] X Liu Z Fang and L Shi ldquoSecuring vehicular ad hocnetworksrdquo in Proceedings of the 2nd International Conferenceon Pervasive Computing and Applications (ICPCA rsquo07) pp 424ndash429 IEEE Birmingham UK July 2007

[13] A El-Keyi T ElBatt F Bai and C Saraydar ldquoMIMO VANETsresearch challenges and opportunitiesrdquo in Proceedings of the2012 International Conference on Computing Networking andCommunications (ICNC rsquo12) pp 670ndash676 IEEE Kauai HawaiiUSA February 2012


[14] ATheodorakopoulos P Papaioannou T Abbas and F Tufves-son ldquoA geometry based stochastic model for MIMO V2Vchannel simulation in cross-junction scenariordquo in Proceedingsof the 13th International Conference on ITS Telecommunications(ITST rsquo13) pp 290ndash295 Tampere Finland November 2013

[15] W Viriyasitavat M Boban H-M Tsai and A VasilakosldquoVehicular communications survey and challenges of channeland propagationmodelsrdquo IEEE Vehicular TechnologyMagazinevol 10 no 2 pp 55ndash66 2015

[16] A B Al-Khalil A Al-Sherbaz and S Turner ldquoEnhancing thephysical layer in V2V communication using OFDMmdashMIMOtechniquesrdquo Architecture vol 1 article 10 2013

[17] Q Yan H Zeng T Jiang M Li W Lou and Y T HouldquoMIMO-based jamming resilient communication in wirelessnetworksrdquo in Proceedings of the 33rd IEEE Conference onComputer Communications (IEEE INFOCOM rsquo14) pp 2697ndash2706 Toronto Canada May 2014

[18] Y Hou M Li X Yuan Y T Hou and W Lou ldquoCooperativecross-technology interference mitigation for heterogeneousmulti-hop networksrdquo in Proceedings of the 33rd IEEEConferenceon Computer Communications (IEEE INFOCOM rsquo14) pp 880ndash888 IEEE Toronto Canada May 2014

[19] S Gollakota F Adib D Katabi and S Seshan ldquoClearing the rfsmog making 80211 n robust to cross-technology interferencerdquoACMSIGCOMMComputer Communication Review vol 41 no4 pp 170ndash181 2011

[20] M C Mah H S Lim and A W C Tan ldquoImproved channelestimation for mimo interference cancellationrdquo IEEE Commu-nications Letters vol 19 no 8 pp 1355ndash1357 2015

[21] D Tse and P Viswanath Pervasive Computing and ApplicationsCambridge University Press Cambridge UK 2005

[22] C Sommer R German and F Dressler ldquoBidirectionally cou-pled network and road traffic simulation for improved IVCanalysisrdquo IEEE Transactions on Mobile Computing vol 10 no1 pp 3ndash15 2011

[23] M Boban J Barros and O K Tonguz ldquoGeometry-basedvehicle-to-vehicle channelmodeling for large-scale simulationrdquoIEEE Transactions on Vehicular Technology vol 63 no 9 pp4146ndash4164 2014

[24] S M Alamouti ldquoA simple transmit diversity technique forwireless communicationsrdquo IEEE Journal on Selected Areas inCommunications vol 16 no 8 pp 1451ndash1458 1998

[25] A Lozano and N Jindal ldquoTransmit diversity vs spatial mul-tiplexing in modern MIMO systemsrdquo IEEE Transactions onWireless Communications vol 9 no 1 pp 186ndash197 2010

[26] D Krajzewicz J Erdmann M Behrisch and L Bieker ldquoRecentdevelopment and applications of SUMOmdashsimulation of urbanmobilityrdquo International Journal on Advances in Systems andMeasurements vol 5 no 3-4 pp 128ndash138 2012

[27] A Varga ldquoThe omnet++ descrete event simulation systemrdquo inProceedings of the European Simulation Multiconference (ESMrsquo01) Prague Czech Republic June 2001

[28] R Riebl M Monz S Varga et al ldquoImproved security perfor-mance for vanet simulationsrdquo in Proceedings of the 4th IFACSymposium on Telematics Applications (TA rsquo16) Porto AlegreBrazil 2016

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

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Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

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Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia


Biomedical Imaging


ArtificialNeural Systems

Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in



information cannot be modified or dropped by an attackerVehicular security threats target all the three major compo-nents of security confidentiality integrity and availability(CIA) [7] A specific security-related concern that VANETsface is maintaining communication during an RF jammingattack As reported in [8] it is proven that constant periodicand reactive RF jamming has significant impact on vehicularcommunications through extensive measurements in ananechoic user Specifically in [8] the impact of the reactiondelay and interfering signal duration on the effectivenessof the reactive jammer is also quantified Hence jamming-aware communications protocols and applications as wellas effective jamming detection and reaction strategies are ofgreat need

Regarding jamming in VANETs the main purpose ofprevious works was to analyze threats and focus on the effectsof RF jamming [9 10] Most of previous works deal withthe early and correct detection of malicious nodes [11] ordevelop some techniques that use frequency hopping [12] inorder to find an interference-free channelThesemethods aretoo complicated to be implemented in a real environmentespecially when more sophisticated jamming attacks have tobe addressed (eg a reactive jammer) Also usually in RFjamming attacks all communication channels are blocked andtechniques like frequency hopping do not have any positiveeffect

2 Motivation

To combat RF jamming effectively in this paper we proposethe use of MIMO MIMO systems although thoroughlyinvestigated are mostly focused on how to improve someparameters of vehicular communications for example com-munication range and throughput Previous work mainlyfocuses on explaining the benefits of using MIMO inVANETs [13] and examining propagation models [14 15]and OFDM-based MIMO systems [16] Previous works didnot study MIMO systems as an active defense mechanismthat overcomes different types of RF jamming attacks Onlyrecently active antijamming MIMO-based techniques wereintroduced Authors in [17] present a MIMO-based anti-jamming technique that uses prerotation or beamformingof the jamming signal in order to improve sender signaldecodability The method is difficult to be implemented in aVANET scenario since the channel conditions are changingvery frequently and multiple pilots must be used by thesender and the jammer for real-time channel tracking Inanother work in [18] the authors proposed a cooperativeinterference mitigation scheme combined with MIMO forjamming suppression This method is based on the channelinformation ratio which is provided from the probing ofthe channel In VANETs the frequently changing channelwould generate a large number of probes overloading thechannelThe authors in [19] useMIMO and interference can-cellation in order to support communication in the presenceof strong interference However only random interferenceis considered and the proposed method is not tailored toreactive RF jammers Last in [20] an improved MIMOchannel estimation for interference cancellation is exploited

to combat reactive jamming However a quite complicatedmethod based on Kalman filter and basis expansion model(BEM) with a large number of iterations for convergence isused to track the channel of the jammer making the methoddifficult to be employed in real situations

This article investigates MIMO systems for improvingrobustness in RF continuous and reactive jamming threatsand simultaneously achieving higher throughput rates inVANETs In this paper the MIMO scheme with instanta-neous Channel State Information (CSI) per received packetat the receiver and without knowledge for the channel ofjammer is used We show that using MIMO the suppressingof the jamming signal can be successful without using ajamming detection phase and regardless of the type andstructure of the jamming signal Our proposed schemenamed vSP4 which combines the Alamouti scheme withspatial multiplexing (SM) [21] nearly doubles the throughputand also decreases the silence time almost by a factor of twowhen compared to the classic cSP4 scheme in the presenceof a malicious jammer Another contribution of this paper isa new framework for VANET simulations which combinesthree known simulators for obtaining more realistic results

3 System Model

31 Simulation Framework For evaluating the proposeddefense mechanisms the VEINS simulator is used [22]This open-source framework consists of two well-knownsimulators OMNET++ an event-based network simulatorand SUMO a road traffic simulator Furthermore insteadof using the existing PHY layer of OMNET++ the GEMV(a geometry-based efficient propagation model for V2V)[23] tool was integrated into the VEINS network simulatorGEMV calculates a propagation model that separates linksinto line-of-sight (LOS) and non-LOS (NLOSv and NLOSb)link types and calculates deterministically the large-scalesignal variation (ie path loss and shadowing) for eachlink type Furthermore GEMV employs a simple geometry-based small-scale signal variation model that calculates theadditional stochastic signal variation based on the informa-tion about the surrounding objects GEMV was configuredand modified to be portable to the VEINS simulator andincorporated into this Figure 1 illustrates the instantaneousSNR versus distance calculated by integration of GEMV-VEINS compared with this calculated by VEINS simulatorGEMV uses a more detailed propagation model to calculatethe SNR which takes into account the ldquoqualityrdquo of the linkstaking into account the physical obstacles (eg building andcars) compared to simple log-distancemodel which is used inVEINS The proposed VEINS-GEMV integrated simulationframework allows more realistic simulations since the SNRis affected not only from the distance among vehicles butalso from the small-scale and the large-scale variations of thewireless medium

32 Channel Model and PHY Modulation For simulationsthe 80211p MAC and PHY parameters at 59GHz (10MHz)are used Please refer to Table 2 for details of specific param-eter values Also Rayleigh fading channels with Additive



minus5

0

5

10

15

20

25

30

35

SNR

(dB)

50 100 150 200 250 300 350 4000Distance (m)









=[[[[[[

11991011119910lowast2111991012119910lowast22

]]]]]]=[[[[[[


]]]]]][11990611199062] + (1)



119867 =[[[[[[


]]]]]]

(2)


(119867119867119867)minus1 = [[[[

11003816100381610038161003816ℎ1110038161003816100381610038162 + 1003816100381610038161003816ℎ2110038161003816100381610038162 + 1003816100381610038161003816ℎ1210038161003816100381610038162 + 1003816100381610038161003816ℎ2210038161003816100381610038162 00 11003816100381610038161003816ℎ1110038161003816100381610038162 + 1003816100381610038161003816ℎ2110038161003816100381610038162 + 1003816100381610038161003816ℎ1210038161003816100381610038162 + 1003816100381610038161003816ℎ2210038161003816100381610038162

]]]] (3)







0 1] [11990611199062] + (4)


119862Alamouti = 119870119879 log(det(119868 + 1198751198791199031205902119899119899119879 (119867119867119867))) (5)



= 119867 + (6)


119867dagger = (119867119867119867)minus1119867119867 (7)


119903 = (119867119867119867)minus1119867119867 = 119909 + (119867119867119867)minus1119867119867 (8)



119910119895 =2sum119894=1

ℎ119894119895119909119894 + 119908119895 119895 = 1 2 (9)



119862SM = log(det(119868 + 1198751198791205902119899119899119879 (119867119867119867)))

= min(119899119877119899119879)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (10)



119910119895 =2sum119894=1

(ℎ119894119895119909119894) + ℎ31198951199091 + ℎ41198951199092 + 119908119895 119895 = 1 4 (11)







119862vSP4 =min(119899119879119899119860119899119877119899119860)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (13)














50 100 1500Distance (m)

0

2

4

6

8

10

12Th

roug

hput

(Mbp

s)

SISO AlamoutiSM

(a)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 100 1500Distance (m)

SISO AlamoutiSM

(b)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 60 70 80 90 10040Time (s)

SISO AlamoutiSM

(c)

50 60 70 80 90 10040Time (s)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

SISO AlamoutiSM

(d)







40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

PER

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(a)

PER

40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(b)


0 5 10 15 20minus5SNR (dB)

0

5

10

15

20

Thro

ugpu

t (M

bps)

cSP2cSP4 vSP4

(a)

40 50 60 70 80 90 100Time (s)

cSP2

0

5

10

15

20

25Th

roug

hput

(Mbp

s)

cSP4 vSP4

(b)













6 Conclusions





Competing Interests


References





































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





minus5

0

5

10

15

20

25

30

35

SNR

(dB)

50 100 150 200 250 300 350 4000Distance (m)









=[[[[[[

11991011119910lowast2111991012119910lowast22

]]]]]]=[[[[[[


]]]]]][11990611199062] + (1)



119867 =[[[[[[


]]]]]]

(2)


(119867119867119867)minus1 = [[[[

11003816100381610038161003816ℎ1110038161003816100381610038162 + 1003816100381610038161003816ℎ2110038161003816100381610038162 + 1003816100381610038161003816ℎ1210038161003816100381610038162 + 1003816100381610038161003816ℎ2210038161003816100381610038162 00 11003816100381610038161003816ℎ1110038161003816100381610038162 + 1003816100381610038161003816ℎ2110038161003816100381610038162 + 1003816100381610038161003816ℎ1210038161003816100381610038162 + 1003816100381610038161003816ℎ2210038161003816100381610038162

]]]] (3)







0 1] [11990611199062] + (4)


119862Alamouti = 119870119879 log(det(119868 + 1198751198791199031205902119899119899119879 (119867119867119867))) (5)



= 119867 + (6)


119867dagger = (119867119867119867)minus1119867119867 (7)


119903 = (119867119867119867)minus1119867119867 = 119909 + (119867119867119867)minus1119867119867 (8)



119910119895 =2sum119894=1

ℎ119894119895119909119894 + 119908119895 119895 = 1 2 (9)



119862SM = log(det(119868 + 1198751198791205902119899119899119879 (119867119867119867)))

= min(119899119877119899119879)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (10)



119910119895 =2sum119894=1

(ℎ119894119895119909119894) + ℎ31198951199091 + ℎ41198951199092 + 119908119895 119895 = 1 4 (11)







119862vSP4 =min(119899119879119899119860119899119877119899119860)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (13)














50 100 1500Distance (m)

0

2

4

6

8

10

12Th

roug

hput

(Mbp

s)

SISO AlamoutiSM

(a)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 100 1500Distance (m)

SISO AlamoutiSM

(b)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 60 70 80 90 10040Time (s)

SISO AlamoutiSM

(c)

50 60 70 80 90 10040Time (s)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

SISO AlamoutiSM

(d)







40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

PER

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(a)

PER

40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(b)


0 5 10 15 20minus5SNR (dB)

0

5

10

15

20

Thro

ugpu

t (M

bps)

cSP2cSP4 vSP4

(a)

40 50 60 70 80 90 100Time (s)

cSP2

0

5

10

15

20

25Th

roug

hput

(Mbp

s)

cSP4 vSP4

(b)













6 Conclusions





Competing Interests


References





































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in









0 1] [11990611199062] + (4)


119862Alamouti = 119870119879 log(det(119868 + 1198751198791199031205902119899119899119879 (119867119867119867))) (5)



= 119867 + (6)


119867dagger = (119867119867119867)minus1119867119867 (7)


119903 = (119867119867119867)minus1119867119867 = 119909 + (119867119867119867)minus1119867119867 (8)



119910119895 =2sum119894=1

ℎ119894119895119909119894 + 119908119895 119895 = 1 2 (9)



119862SM = log(det(119868 + 1198751198791205902119899119899119879 (119867119867119867)))

= min(119899119877119899119879)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (10)



119910119895 =2sum119894=1

(ℎ119894119895119909119894) + ℎ31198951199091 + ℎ41198951199092 + 119908119895 119895 = 1 4 (11)







119862vSP4 =min(119899119879119899119860119899119877119899119860)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (13)














50 100 1500Distance (m)

0

2

4

6

8

10

12Th

roug

hput

(Mbp

s)

SISO AlamoutiSM

(a)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 100 1500Distance (m)

SISO AlamoutiSM

(b)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 60 70 80 90 10040Time (s)

SISO AlamoutiSM

(c)

50 60 70 80 90 10040Time (s)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

SISO AlamoutiSM

(d)







40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

PER

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(a)

PER

40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(b)


0 5 10 15 20minus5SNR (dB)

0

5

10

15

20

Thro

ugpu

t (M

bps)

cSP2cSP4 vSP4

(a)

40 50 60 70 80 90 100Time (s)

cSP2

0

5

10

15

20

25Th

roug

hput

(Mbp

s)

cSP4 vSP4

(b)













6 Conclusions





Competing Interests


References





































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





119862vSP4 =min(119899119879119899119860119899119877119899119860)sum119894=1

log(1 + 11987511987912058221198941205902119899119899119879) (13)














50 100 1500Distance (m)

0

2

4

6

8

10

12Th

roug

hput

(Mbp

s)

SISO AlamoutiSM

(a)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 100 1500Distance (m)

SISO AlamoutiSM

(b)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 60 70 80 90 10040Time (s)

SISO AlamoutiSM

(c)

50 60 70 80 90 10040Time (s)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

SISO AlamoutiSM

(d)







40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

PER

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(a)

PER

40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(b)


0 5 10 15 20minus5SNR (dB)

0

5

10

15

20

Thro

ugpu

t (M

bps)

cSP2cSP4 vSP4

(a)

40 50 60 70 80 90 100Time (s)

cSP2

0

5

10

15

20

25Th

roug

hput

(Mbp

s)

cSP4 vSP4

(b)













6 Conclusions





Competing Interests


References





































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




50 100 1500Distance (m)

0

2

4

6

8

10

12Th

roug

hput

(Mbp

s)

SISO AlamoutiSM

(a)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 100 1500Distance (m)

SISO AlamoutiSM

(b)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

50 60 70 80 90 10040Time (s)

SISO AlamoutiSM

(c)

50 60 70 80 90 10040Time (s)

0

2

4

6

8

10

12

Thro

ughp

ut (M

bps)

SISO AlamoutiSM

(d)







40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

PER

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(a)

PER

40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(b)


0 5 10 15 20minus5SNR (dB)

0

5

10

15

20

Thro

ugpu

t (M

bps)

cSP2cSP4 vSP4

(a)

40 50 60 70 80 90 100Time (s)

cSP2

0

5

10

15

20

25Th

roug

hput

(Mbp

s)

cSP4 vSP4

(b)













6 Conclusions





Competing Interests


References





































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

PER

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(a)

PER

40 50 60 70 80 90 100 11030Time (s)

SISO AlamoutiSM

10minus14

10minus12

10minus10

10minus8

10minus6

10minus4

10minus2

100

(b)


0 5 10 15 20minus5SNR (dB)

0

5

10

15

20

Thro

ugpu

t (M

bps)

cSP2cSP4 vSP4

(a)

40 50 60 70 80 90 100Time (s)

cSP2

0

5

10

15

20

25Th

roug

hput

(Mbp

s)

cSP4 vSP4

(b)













6 Conclusions





Competing Interests


References





































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in







6 Conclusions





Competing Interests


References





































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in


























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



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