ServiceTime-BasedRegionDivisioninOVSF-BasedWireless...

Research ArticleService Time-Based Region Division in OVSF-Based WirelessNetworks with Adaptive LTE-M Network for Machine toMachine Communications

Vipin Balyan 1 Davinder S Saini2 and Bhasker Gupta 2

1Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape TownSouth Africa2Department of Electronics and Communication Engineering Chandigarh College of Engineering and Technology Sec-26Chandigarh India

Correspondence should be addressed to Vipin Balyan vipinbalyanrediffmailcom

Received 4 February 2019 Accepted 6 May 2019 Published 30 May 2019

Academic Editor George S Tombras

Copyright copy 2019 Vipin Balyan et al +is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

+e 3GPP standards have presented the LTE-M as one of the main technologies to provide services to Internet of +ings (IoT)+e IoT applications are usually short-lived applications like smart sensing surveillance systems for home or businesses anddata uploading applications like metering In this paper the proposed architecture of the base station has a LTE interface whichassigns resource blocks (RBs) and another 3G interface which is equipped with orthogonal variable spreading factor (OVSF)codes +e IoT devices deployed ubiquitously leads to massive machine type communication which leads to burst traffic oncurrent cellular services +e IoTdevices when assigned large resources will reduce the radio efficiency +e work in this paperassigns OVSF codes available on 3G interface to the IoT devices +e LTE resources are used for IoT devices in case ofemergency or when resources of 3G interface are 100 utilized +is will solve the problem of both small data transfer andconnectivity requirement of IoTdevices +e IoTapplications are event-driven and time-bound also and the resources are alsoreserved for these applications in the proposed work +e simulations and results show that proposed work increases bothnetwork efficiency and capacity

1 Introduction

In Internet of $ings (IoT) devices are connected to eachother physically IoTprovides services like disaster detectione-health surveillance of places grids etc which makes lifeconvenient +e IoT is basically a machine-to-machine(M2M) communication this made machine to machine(M2M) communication an attractive technology for in-dustry and researchers It is a novel communication tech-nology in which large number of wirelessly connecteddevices can share information with each other By 2021 15billion devices are expected to be connected using M2Mcommunications as predicted by Ericsson [1] +e largenumber of devices and other connectivity issues challengesimposed on cellular networks will be addressed by 3GPPstandards Some of challenges are reducing latency reducing

device cost and increasing support to large number ofdevices extended with enhanced coverage [2ndash4]

Although small-size packets are transmitted by devicesusing M2M communication in time intervals their speci-fications and functions create synchronized storms ofpayload as compared to traditional cellular communications+is will lead to saturation of the limited bandwidth of theLTE-M without 3G interface with increased number ofdevices +is problem will become more prominent in theconditions of emergency (floods terrorist attacks fire etc)when all the devices connected to critical services want tosend their data simultaneously +is will basically lead toblocking of either cellular communication or M2M com-munication which degrades further when additional devicesjoin the network due to the random nature of devices joiningand leaving the network [5]

HindawiJournal of Electrical and Computer EngineeringVolume 2019 Article ID 3623712 8 pageshttpsdoiorg10115520193623712

+e LTE-M assigns a resource block (RB) which cantransmit hundreds of data bits at a time On the other handthe IoTapplications require small data to be transmitted at atime as they are mostly event based +e assignment of a RBto IoT applications will severely reduce efficiency of thenetwork as a whole and will lead to blocking of cellular calls+e M2M calls carry small data with required quality ofservice (QoS)

In order to address these problems the IoT calls aremostly handled using 3G interface and cellular calls usingLTE-M interface in this paper +e proposed solution will usethe available 3G interface in the base stations (BSs) which ismostly used for voice calls in cellular communications +ereis only topological difference in which M2M devices areconnected in this paper and they are connected to a gatewayand can also communicate directly with the BS +eremaining topology of the M2M network is similar to to-pology available in literature like transmission data can beaggregated on one device from multiple devices and thatdevice is a gateway for those devices +e proposed topologyuses OVSF codes as resources for handling IoT calls LTE-Mresources are used in case of emergency and during nonbusyhours of cellular communication In this paper a new servicetime-based region division of OVSF code tree is used which isbest suited for IoT applications like metering information ofgrid surveillance information periodic update etc the IoTcalls for which data is known in advance +is will furtherimprove the resource efficiency +e OVSF code tree is di-vided based on service time required which will reduce la-tency and provide better QoS +e overall network efficiencywill also increase

+e rest of the paper is organized as follows +e lit-erature survey of the related work is done in Section 2 InSection 3 the network architecture and problem is definedIn Section 4 the proposed scheme is discussed and used forhandling IoT calls Simulation and results are presented inSection 5 +e paper is concluded in Section 6

2 Related Work

In the literature the work done is focused on coveragecapacity and emergency services using LTE-M andornarrow band IoT +e work in [6] has examined the plat-forms for providingM2M service and explored the problemswhich might appear while handling M2M services+e workin [7] focuses on resource allocation in both spatial andtemporal manners+e network performance is improved in[8ndash11] using path selection strategies In [8] the path isselected on the basis of channel conditions this improvesthroughput as the path with best channel conditions reducesloss of information +e work in [9] imposed the devices toform connection with the nearest gateways in order toimprove transmission quality +e scheme in [10] decidesthe path depending upon the received channel quality whileaccommodating both parameters efficiency and resourceutilization +e devices in [11] choose their gateways basedupon transmission time which improves the transmissionefficiency All these suffer from wastage of radio resource asindividually connected devices lead to blocking of resources

+is problem is solved by sharing the physical resourceblocks between devices in [12]

+e work is also done for efficient utilization of RBs ineNodeB in [13] the number of devices that can be handledby eNodeB are increased using a cross-layer approach +eTCPIP overheads are reduced by clustering and bufferingUsing this approach eNodeB can handle up to 65K devicesat a time for a 10MHz bandwidth case However it affectsQoS of both M2M and cellular communication in con-gestion state A source modelling approach which is basedon Coupled Markov Modulated Poisson Processes(CMMPP) is proposed in [14] to handle massive devices andproblems associated with them It proposes the parallelimplementation of 30K M2M devices A mathematicalmodel was proposed in [15] for LTE downlink bandwidthassignment to improve QoS for the devices the bandwidthadaptation was not discussed while proposed with the aim ofproviding a good QoS for each UE the coexistence betweenLTE-M and LTE-A systems and the bandwidth adaptationare not spotted +e work in [16] uses a cognitive radio-based access approach which works on both the priority andqueuing +e M2M devices are differentiated on the basis oftheir QoS requirements

Most of the work in the literature uses LTE bandwidthfor M2M communication No work is available in the lit-erature which uses already available 3G interface at the BS+is interface can be used to provide faster access to theM2M devices without creating much interference +isinterface can also handle mission critical cellular calls in caseof emergency

3 Network Architecture

+e network architecture is introduced in this section forboth M2M devices and cellular communication +e eNo-deB has an LTE interface which assigns RB(s) and a 3Ginterface which assigns OVSF codes +e network is as-sumed to have one eNodeB a group of nests are connectedto it In this section the network architecture of LTE-M isfirst defined+en the traffic features and QoS requirementsof the M2M devices are described +e 3G interface isequipped with OVSF code tree of 9 layers

31 LTE-M Network Architecture In the LTE-M networkfor communication two types of machines are involvedM2M devices and M2M gateway +e M2M devices con-sidered in this paper are consumer devices which enablecommunication and can communicate directly witheNodeB or connect to gateways using two hop commu-nication +e gateways improve the transmission efficiencyas they help devices to transfer data to eNodeB +isformation of nesting and gateway is based on distance thegateways are the devices which are closer to eNodeB andprovide connection to the devices in the nest eNodeB +egateways can be dynamic or static depending upon theremobility +e device can join and leave a nest dynamically+e LTE-M network architecture with cellular users isshown in Figure 1

2 Journal of Electrical and Computer Engineering

32 TracType andQoSRequirements e IoTapplicationsgenerate broadly three types of tracs depending upon thetype of the QoS and delay requirements the delay-toleranttrac with constant bandwidth requirement delay-toleranttrac with exible bandwidth requirement and delay-sensitive trac with xed bandwidth requirements ecellular communication is divided into two categoriesdelay-tolerant with exible bandwidth and delay-sensitivewith constant bandwidth requirements (voice calls) eLTE resources will mainly be assigned to cellular commu-nication for calls requesting delay tolerant with exiblebandwidth e OVSF codes in two dimensions are used forassignment to handle any type of calls when LTE resourcesare busy A minimum of two time consecutive RBs areassigned to a device (cellular or M2M) and contingent onutilized modulation and coding scheme (MCS) the devicetransmits between 32 and 616 bitssubframe when using LTEinterface

e M2M gateway collects payloads from all the devicesconnected to it when LTE resources are busy and transmitswhen they become available e M2M gateways are mostly

used when M2M devices cannot communicate directly witheNodeB or when no vacant code is available for assignmentin OVSF code tree is leads to reduction in the wastage ofRBs especially when devices have less bits to send

4 Network Parameters and Properties

In LTE resources are divided into time and frequency Intime domain the radio frame used is of 10ms which isfurther divided into 10 subframes of 1ms as shown inFigure 2 [17] A subframe is divided into two slots of 05msEach slot is of 6 or 7 orthogonal frequency division multipleaccess (OFDMA) symbols In frequency domain thesmallest unit is of 15KHz which is termed as resource el-ement (RE) A resource block constitutes 12 subcarrierswhich is a duration of one slot and total bandwidth 180KHz

In this paper the uplink transmission for an eNodeB isconsidered for di M2M devices where 1le ilem and gjM2M gateways where 1le jle n ese M2M devices cancommunicate directly with eNodeB and can communicatethrough M2M in a two-hop manner e M2M devices

Medicalcommunication

systemsTraffic lights

controller

Smartgrid

applications

Cars with smartdevices

eNodeB

Mobilenetwork

M2M network

M2Mgateway Using

sim card

Devices

Figure 1 Smart communication system with M2M network smart grids and possible cellular communications

Journal of Electrical and Computer Engineering 3

decide the communication they will use depending uponstatus of eNodeB and their distance from the eNodeBUsually the M2M devices at the boundaries of coverage areause two-hop communication or when amount of in-formation is less When eNodeB capacity is fully utilizedthen also the two-hop communication is utilized by M2Mdevices through M2M gateways eM2M gateways are alsoequipped with storage capacity ey can store the in-formation received from M2M devices e M2M gatewaystransfer their data depending on the location from eNodeBwhile using 3G interface and using a suitable AMC schemegiven in Table 1

e 3G interface uses 2-dimensional OVSF codes tohandle cellular voice calls and M2M calls e system modelin this section describes the notations used identication ofcodes and formulae used to nd Cl of a time domain code

A binary tree is used to generate an OVSF code tree of llayers where l isin [1 L] L is the root layer [9] A channeli-zation code used for assignment on 3G interface is repre-sented by Clnl where nl is its position in layer l andnl isin [1 2Lminusl] A code in any layer l has spreading factor(SF) 2Lminusl e codes in the lowest layer 1 has a maximumspreading factor of 2Lminus1 e higher the SF of a code thelower the rate supported by it and vice versa e OVSFcodes support quantized rates of the form 2lminus1 e or-thogonal nature of the codes leads to the problem of codeblocking which prevents assignment of codes in layersbelow and above an assigned code is leads to fragmen-tation of available radio resources in the code tree and can bereduced by using careful assignment schemes e OVSFcodes in this paper spreads in both time and frequencydomain e code in time domain is denoted as C2lt minus 1 nlt

andas C2lf minus 1 nlf

in frequency domain [18] e code spreads in 2dimensions retains the orthogonal nature e channeliza-tion code time domain code and frequency spreading codeSF are related to each other by

2Lminusl 2ktminus1 times 2kfminus1 (1)

where kt and kf denotes the index levels for time and fre-quency domains codes which are related to each other as

Lminus l kt + kf minus 2( ) (2)

and the code indexes nl nltand nlf are related by

nl nlf + nlt minus 1( ) times 2kfminus1 (3)

5 The Proposed Scheme

e proposed scheme is explained in this section eeNodeB is equipped with two gateways LTE and 3Ge RBsare assigned when LTE interface is used and OVSF codes areassigned when the 3G interface is used e main idea of thework is to maximize the utilization of RBs and existingresources in the 3G interface without acurrenecting QoS

e calls which can be arrived or generated in thenetwork are as follows

(a) Cellular calls for cellular devices Type-I(b) Cellular data calls Type-II(c) M2M gateway to eNodeB Type-III(d) M2M device to M2M gateway Type-IV

Generate a call of rate qR e algorithm for all types ofcalls works as follows

Case 1 qR isin Typeminus I ese calls are voice calls which arehandled using 3G interface e service time of these calls oraverage call durations are usually less than 2minutes Assignthe OVSF codes from the 3G interface using service time-based division (STBD) assignment scheme explained inSection 51

Case 2 qR isin Typeminus II ese calls are data calls and arehandled using LTE interface Assign required number of RBsusing LTE assignment scheme proposed in Section 52

10msFrame

TTI 2TTI 11ms

05ms 05ms

Subframe 0 Subframe 1

TTI 10TTI 9


Slot 2Slot 1Slot 0

OFDM symbol

Slot 3 Slot 18Slot 17Slot 16 Slot 9

Figure 2 An LTE PHY frame structure

Table 1 Relation betweenMCS index modulation and TBS index

MCS index Modulation TBS index0ndash9 QPSK 0ndash910ndash16 16QAM 9ndash1517ndash28 64QAM 15ndash2629 QPSK

Reserved30 16QAM31 64QAMTBS transport block size


Case 3 qR isin Typeminus III +e algorithm will check theavailable resources of LTE interface 1st and if LTE interfaceavailable resources are not enough 3G interface will be usedto handle the call

Case 4 qR isin Typeminus IV +e algorithm will check theamount of data required to transfer For small data transferrequest the 3G interface will be used and for large datatransfer request the LTE interface will be used Alsoavailability of LTE interface will be checked in case whereLTE resources are busy and 3G interface will handle the callat a slower speed

51 Service Time-Based Division For a call of rate qR theimproved service time-based division (STBD) scheme willdetermine the value of u log2 q + 1 [18] +e STBDscheme considers the U number of user classes with rates2uminus1R u isin [1 U] +e STBD scheme divides the code treeinto U regions and available capacity of each region islimited by channel load of that region

+e capacity of the tree and number of classes can havethese possibilities

(i) For i 0(ii) If (2Lminus1U) isin I and 2Uminus1 le (2Lminus1U) +e allocated

capacity to each class will be (2Lminus1U)R(iii) If (2Lminus1U) isin I and 2Uminus1 gt (2Lminus1U) Increment

i i + 1 the capacity allocated for class U is 2UminusiR+e total unused capacity is (2Lminus1 minus 2Uminusi)R

(iv) If (2Lminus1U) notin I Increment i i + 1 and set(2Lminus1U)minus (2Lminus1U) A If Agt (12) set B

(2Lminus1U) else B (2Lminus1U) Find ni max[niprime]

niprime ge 0 exist ni times 2Uminus1 leB +ere are three possibilities asfollows

(a) If ni is zero the capacity assigned to U class is2Uminus1R For finding the capacity of classes(Uminus 1) to 1 replacing (2Lminus1U)with (2Lminus1 minus2Uminusi)(Uminus 1) and Uwith (Uminus 1) respectivelyin step (i) and proceed

(b) If ni ne 0 and (ni + 1) times 2Uminusi minusBle 2Uminus(i+1) thecapacity allocated to U class is

CLprimemax ni + 1( 1113857 times 2Uminusi

(4)

CUminusi+1max ni times 2Uminusi

R (5)

(c) If ni ne 0 and (ni + 1) times (2Uminusi minusB)gt 2Uminus(i+1) thecapacity allocated to class (Uminus i + 1) in the firstround (Uminus i + 1 U) is given by

+e capacity to be distributed in remaining (Uminus i)

classes is given by

1113944

Uminus(i+1)

p1C

pmax 2Lminus1

RminusCUminusimax (6)

For the capacity of remaining classes steps (i)ndash(iii) arerepeated and replace (2Lminus1U) by (2Lminus1 minus 2Uminusi)(Lminus i) andU with (Uminus 1) For example for the code tree with capacity32R and number of user classes 4 +e capacity allocated toclass 4 users is 16R and for the classes 1 to 3 the remainingcapacity 16R will be distributed +e capacity distributionalgorithm will start from Step (i)

Let the average service time for class u user is tsu where1le uleU +e U regions are arranged in the ascending orderof the average service time Let the service time required bythe ith call is ts

i and elapsed time of ith call at a particulartime is denoted by te

i Let a new call (say ith call) with rate2lminus1R wants a vacant code +e call is given the vacant codefrom the region l that is dedicated to 2lminus1R calls +e totalservice time for ith call can have the following possiblevalues

(i) If the service time for ith call with rate 2lminus1R has avalue less than or equal to tsu the call will stay withcode in region u till completion

(ii) If the service time for ith call with rate 2lminus1R has valuegreater than tsu when the elapsed time te

i of the callexceeds the average service time of the uth regionthe call is shifted to region (u-1) if the vacant code isavailable If the total elapsed time for this ith callexceed tsu + tsuminus1 the call is again shifted to region(u-2) Shifting is done to place ith call to the region inwhich early call completion chances are more Doingthis it becomes certain that the existing vacant codesin the region can handle next calls that are assignedto the codes which becomes free for almost similartimes

+e codes in each region are spread in 2D to improveQoS however it limits the number of users a region canhandle +e different AMC scheme increases the number ofbits transmitted

+e channel load (CL) of the region u is calculatedbefore a new call is assigned to a region [19] if theCLu ltCLThreshold the call will be assigned in region uOtherwise check remaining regions with CLltCLThreshold+e call will be assigned in the available region +e Type-Icalls are always handled using 3G interface

52 LTE Scheme +e LTE resources are assigned based onthe priority Type-IIgtType-IIIgtType-IV calls +e Type-IIcalls are further categorized as emergency M2M calls andcellular data calls+e former has a higher priority+ese aretop priority calls and are handled using LTE resources thelegacy 3G network will lead to latency+e LTE-M devices inthis paper are without carrier aggregation (CA) capability+ey can only use one component carrier (CC) at a time

Consider a LTE-M network system channel bandwidth20MHz at 2GHz frequency +e used AMC schemes areQPSK 16QAM and 64QAM +e total number of RBs theused RBs and the required RBs for new call are denoted asRBT RBu and RBr respectively For a call of rate qR de-termine the required number of RBs


For a Type-II call with a flag of emergency the algorithmworks as follows

(a) If RBr le (RBT minusRBu)

Assign required RBs to the M2M device and updateRBu RBu + RBr

(b) Else ifFind the M2M device using the number ofRBsRBr minus (RBT minusRBu) switch the M2M devicecall to 3G interface and handle Type-II call with a flagusing (RBT minusRBu) and RBs of this call

(c) ElseFind the M2M device using the number ofRBsRBr switch the M2M device call to 3G in-terface and handle Type-II call using RBs of this call

(d) End

For all other calls Type-II-IV +e priority of calls isType-IIgtType-IIIgtType-IV For a new call with requirednumber of RBs as RBr +e algorithm works as follows


Assign required RBs to the cellular device and updateRBu RBu + RBr which are of good channel quality(CQ) for cellular device

(b) Else ifFind the M2M device using the number ofRBsRBr minus (RBT minusRBu) with same CQ switch theM2M device call to 3G interface and handle Type-IIcellular call using (RBT minusRBu) and RBs of this call

(c) ElseFind the M2M device using the number ofRBsRBr switch the M2M device call to 3G in-terface and handle Type-II cellular call using RBs ofthis call

(d) EndFor Type-III which are usually high data calls +ealgorithm works as follows


Assign required RBs to theM2M gateway and updateRBu RBu + RBr which are of good channel quality(CQ) for M2M device

(b) Else+e M2M gateway will transfer the data to anotherM2M gateway with storage capability equal to dataof the call having better channel quality and iscloser to eNodeB using 3G interface STBD scheme+e second M2M gateway will now generate a newcall of rate including own data and data transferredto it

(c) EndFor Type-IV the algorithm works similar If therequired RBs are not enough to handle the new callthe M2M device will transfer its data to M2Mgateway using 3G interface STBD scheme

6 Performance Evaluation

In this section the simulation results of the proposed STBDschemes are compared with existing schemes in literature+e channel bandwidth used is 5MHz 25 resource blocks areused per subframe M2M devices varied between 1600and 7200 and M2M gateways varied between 320 and 1500+e path loss model equation [17] between gateway andeNodeB 1281 + 376 log(d) and between device and gate-way 489 + 40 log(d) where d is in km +e M2M devicesconsidered are of variable nature and different bandwidth re-quirements message bits size varies between 8 and 16 [7] +eresults are average of 10000 simulations +e proposed STBDscheme is compared with four schemes spatial and temporalaggregation (STA) [7] direct communication (DC) scheme andshortest distance (SD) [10] and the shortest time (ST) schemeproposed in [11]+ese are among the best schemes available inthe literature which improves one parameter at the expense ofthe other +e STA scheme proposed in [7] uses gateways toaggregate data and outperform the remaining three schemes+e devices using DC scheme upload data to eNodeB directlythe SD scheme uses the closest gateway for a particular deviceand STuses the path which takes minimum transmission time+e schemes are compared on the performance metrics ofcapacity and efficiency which include the following

Capacity

(i) Number of served M2M connections(ii) +roughput

Efficiency

(i) Data bits transmitted(ii) Number of RBs allocated

61 Capacity In Figure 3 the number of requests of devicesare compared with number of served requests +e STBDscheme services maximum requested connections as it usesOVSF codes on 3G interface to handle devices for directcommunication and in conditions when 4G interface re-sources are fully utilized +e STBD scheme can accom-modate more M2M devices when cellular traffic is less +ethroughput of all the schemes are compared in Figure 4 thethroughput of STBD scheme is comparable to STA schemewhich outperforms other scheme as it effectively aggregatessmall data +e STBD scheme uses different AMC schemesto improve throughput in case when devices are at longerdistance for improving link quality when using OVSF codesIt also uses M2M gateways when device cannot commu-nicate directly using 3G or 4G interface

62 Efficiency In Figure 5 the number of data bits trans-mitted is compared with number of devices request +eefficiency of data bits transmitted of all schemes increaseswith number of devices except DC as direct communicationlink quality decreases with distance +e number of data bitstransmitted for STBD scheme is significantly higher thanother schemes as it uses OVSF codes to provide services to


M2M devices in absence of 4G interface resources eOVSF codes uses dicurrenerent AMC scheme to counter the ecurrenectof distance

In Figure 6 the number of RBs allocated per second iscompared e STBD scheme outperforms all the schemes

e other scheme allocates RBs as resources while STBDscheme uses both RBs and OVSF codes However due tothis the throughput of the STBD scheme as shown inFigure 4 is lesser than other schemes for smaller number ofdevices with better system utilization

e throughput of STA scheme is higher than theSTBD scheme at the expense of utilization of highernumber of NRBs which leads to blocking of future re-quest e STBD scheme is the optimal scheme whichprovides better throughput while utilizing minimumNRB as it uses OVSF codes e percentage dicurrenerencebetween NRB utilized by STBD scheme and STA schemeis around 16 e remaining NRBs can be used tohandle more number of devices while keeping the samethroughput

7 Conclusion

e research on M2M communications is ongoing wheremachines can communicate directly or using eNodeB eM2M devices produce huge amount of trac and handlingthe communication of these devices using LTE resources isnot possible In this paper a 3G interface is used to handlethe communication of these devices most of the time whichleads to considerable reduction in utilization of RBs forM2M devices e STBD scheme handles more number ofdevices as compare to other schemese spectral utilizationof 3G resources is improved and 4G resources can be usedfor future requests is improved overall performance andeciency of the used eNodeB e M2M gateways are alsoused when eNodeB resources are utilized In future workcan be done to transfer dynamically the calls between 3G and4G interfaces

Data Availability

e data of the research are still in use for the researchersand releasing data at this stage might result in utilization ofdata for research purpose by others e sharing of dataresources is limited by researchers involved

0

200

400

600

800

1000

1200

Thro

ughp

ut

2400 3200 4000 4800 5600 6400 72001600Number of devices

DCSDST

STASTBD

Figure 4 roughput of the system

05

1015202530354045

Dat

a bits

tran

smitt

ed


DCSDST

STASTBD

Figure 5 Number of requests from devices compared with numberof served requests

0

05

1

15

2

25times104

NRB

s allo

cate

d


DCSDST

STASTBD

Figure 6 Number of requests from devices compared with numberof RBs allocated per second

0

1000

2000

3000

4000

5000

6000

Num

ber o

f ser

ved

conn

ectio

ns


DCSDST

STASTBD



Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

+e authors thank the Cape Peninsula University of Tech-nology Cape Town South Africa

References

[1] Ericsson ldquoOn the pulse of the networked societyrdquo EricssonMobility Report MWC Edition 2015

[2] H Shariatmadari R Ratasuk S Iraji et al ldquoMachine-typecommunications current status and future perspectives to-ward 5G systemsrdquo IEEE Communications Magazine vol 53no 9 pp 10ndash17 2015

[3] S Landstrom J Bergstrom E Westerberg andD Hammarwall ldquoNB-IOT a sustainable technology forconnecting billions of devicesrdquo Ericsson Technology Reviewvol 93 no 3 2016

[4] A Al-Fuqaha M Guizani M Mohammadi M Aledhari andM Ayyash ldquoInternet of +ings a survey on enabling tech-nologies protocols and applicationsrdquo IEEE CommunicationsSurveys and Tutorials vol 17 no 4 pp 2347ndash2376 2015

[5] S C Lin and L Gu ldquoDiversity-multiplexing tradeoff incognitive machine-to-machine networksrdquo in Proceedings ofthe 2015 IEEE Global Communications Conference GLOBE-COM 2015 San Diego CA USA December 2015

[6] K Zheng F Hu W Wang W Xiang and M Dohler ldquoRadioresource allocation in LTE-advanced cellular networks withM2M communicationsrdquo IEEE Communications Magazinevol 50 no 7 pp 184ndash192 2012

[7] P Y Chang J M Liang J J Chen K R Wu and Y C TsengldquoSpatial and temporal aggregation for small and massivetransmissions in LTE-M networksrdquo in Proceedings of the IEEEWireless Communications and Networking ConferenceWCNC San Francisco CA USA March 2017

[8] J Luo R S Blum L J Cimini L J Greenstein anda M Haimovich ldquoLink-failure probabilities for practicalcooperative relay networksrdquo in Proceedings of the 2005 IEEE61st Vehicular Technology Conference Stockholm SwedenJune 2005

[9] D Zhang G Li K Zheng X Ming and Z H Pan ldquoAnenergy-balanced routing method based on forward-awarefactor for wireless sensor networksrdquo IEEE Transactions onIndustrial Informatics vol 10 no 1 pp 766ndash773 2014

[10] G A Elkheir A S Lioumpas and A Alexiou ldquoEnergy ef-ficient AF relaying under error performance constraints withapplication to M2M networksrdquo in Proceedings of the IEEEInternational Symposium on Personal Indoor and MobileRadio Communications PIMRC Toronto Canada September2011

[11] H Li Y Cheng C Zhou and W Zhuang ldquoRouting metricsfor minimizing end-to-end delay in multiradio multichannelwireless networksrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 11 pp 2293ndash2303 2013

[12] S N KhanMarwat Y Mehmood C Gorg and A Timm-GielldquoData aggregation of mobile M2M traffic in relay enhancedLTE-A networksrdquo EURASIP Journal on Wireless Communi-cations and Networking vol 2016 no 1 2016

[13] J Plachy Z Becvar and E C Strinati ldquoCross-layer approachenabling communication of high number of devices in 5G

mobile networksrdquo in Proceedings of the 2015 IEEE 11th In-ternational Conference on Wireless and Mobile ComputingNetworking and Communications WiMob 2015 Abu DhabiUAE October 2015

[14] M Laner P Svoboda N Nikaein and M Rupp ldquoTrafficmodels for machine type communicationsrdquo in Proceedings ofthe Tenth International Symposium on Wireless Communi-cation Systems ISWCS 2013 Ilmenau Germany August 2013

[15] S Garkusha A M K Al-Dulaimi and H D Al-JanabildquoModel of resource allocation type 1 for LTE downlinkrdquo inProceedings of the 2015 International Conference on Antenna$eory and Techniques Dedicated to 95 Year Jubilee of ProfYakov S Shifrin ICATT 2015 Kharkiv Ukraine April 2015

[16] M Hasan E Hossain and D I Kim ldquoResource allocationunder channel uncertainties for relay-aided device-to-devicecommunication underlaying LTE-A cellular networksrdquo IEEETransactions on Wireless Communications vol 13 no 4pp 2322ndash2338 2014

[17] B Groenewald V Balyan and M T E Khan ldquoSmart utili-zation LTE-UMTS spectrum in Smart Grids for communi-cationrdquo in Proceedings of the 25th Conference on the DomesticUse of Energy DUE 2017 Cape Town South Africa April2017

[18] B Groenewald V Balyan and M T E Kahn ldquoFast channelload algorithm for downlink of multi-rate MC-DS-CDMAand smart grid communicationrdquo Journal of Applied Engi-neering Research vol 13 no 20 pp 14607ndash14613 2018

[19] B J Chang and C H Wu ldquoAdaptive load balancing MDP-based approach of two-dimensional spreading for VSF-OFCDM in 4G next-generation cellular communicationsrdquoIEEE Transactions on Vehicular Technology vol 68 no 3pp 1143ndash1156 2009


International Journal of

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VLSI Design



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+e LTE-M assigns a resource block (RB) which cantransmit hundreds of data bits at a time On the other handthe IoTapplications require small data to be transmitted at atime as they are mostly event based +e assignment of a RBto IoT applications will severely reduce efficiency of thenetwork as a whole and will lead to blocking of cellular calls+e M2M calls carry small data with required quality ofservice (QoS)

In order to address these problems the IoT calls aremostly handled using 3G interface and cellular calls usingLTE-M interface in this paper +e proposed solution will usethe available 3G interface in the base stations (BSs) which ismostly used for voice calls in cellular communications +ereis only topological difference in which M2M devices areconnected in this paper and they are connected to a gatewayand can also communicate directly with the BS +eremaining topology of the M2M network is similar to to-pology available in literature like transmission data can beaggregated on one device from multiple devices and thatdevice is a gateway for those devices +e proposed topologyuses OVSF codes as resources for handling IoT calls LTE-Mresources are used in case of emergency and during nonbusyhours of cellular communication In this paper a new servicetime-based region division of OVSF code tree is used which isbest suited for IoT applications like metering information ofgrid surveillance information periodic update etc the IoTcalls for which data is known in advance +is will furtherimprove the resource efficiency +e OVSF code tree is di-vided based on service time required which will reduce la-tency and provide better QoS +e overall network efficiencywill also increase

+e rest of the paper is organized as follows +e lit-erature survey of the related work is done in Section 2 InSection 3 the network architecture and problem is definedIn Section 4 the proposed scheme is discussed and used forhandling IoT calls Simulation and results are presented inSection 5 +e paper is concluded in Section 6

2 Related Work

In the literature the work done is focused on coveragecapacity and emergency services using LTE-M andornarrow band IoT +e work in [6] has examined the plat-forms for providingM2M service and explored the problemswhich might appear while handling M2M services+e workin [7] focuses on resource allocation in both spatial andtemporal manners+e network performance is improved in[8ndash11] using path selection strategies In [8] the path isselected on the basis of channel conditions this improvesthroughput as the path with best channel conditions reducesloss of information +e work in [9] imposed the devices toform connection with the nearest gateways in order toimprove transmission quality +e scheme in [10] decidesthe path depending upon the received channel quality whileaccommodating both parameters efficiency and resourceutilization +e devices in [11] choose their gateways basedupon transmission time which improves the transmissionefficiency All these suffer from wastage of radio resource asindividually connected devices lead to blocking of resources

+is problem is solved by sharing the physical resourceblocks between devices in [12]

+e work is also done for efficient utilization of RBs ineNodeB in [13] the number of devices that can be handledby eNodeB are increased using a cross-layer approach +eTCPIP overheads are reduced by clustering and bufferingUsing this approach eNodeB can handle up to 65K devicesat a time for a 10MHz bandwidth case However it affectsQoS of both M2M and cellular communication in con-gestion state A source modelling approach which is basedon Coupled Markov Modulated Poisson Processes(CMMPP) is proposed in [14] to handle massive devices andproblems associated with them It proposes the parallelimplementation of 30K M2M devices A mathematicalmodel was proposed in [15] for LTE downlink bandwidthassignment to improve QoS for the devices the bandwidthadaptation was not discussed while proposed with the aim ofproviding a good QoS for each UE the coexistence betweenLTE-M and LTE-A systems and the bandwidth adaptationare not spotted +e work in [16] uses a cognitive radio-based access approach which works on both the priority andqueuing +e M2M devices are differentiated on the basis oftheir QoS requirements

Most of the work in the literature uses LTE bandwidthfor M2M communication No work is available in the lit-erature which uses already available 3G interface at the BS+is interface can be used to provide faster access to theM2M devices without creating much interference +isinterface can also handle mission critical cellular calls in caseof emergency

3 Network Architecture

+e network architecture is introduced in this section forboth M2M devices and cellular communication +e eNo-deB has an LTE interface which assigns RB(s) and a 3Ginterface which assigns OVSF codes +e network is as-sumed to have one eNodeB a group of nests are connectedto it In this section the network architecture of LTE-M isfirst defined+en the traffic features and QoS requirementsof the M2M devices are described +e 3G interface isequipped with OVSF code tree of 9 layers

31 LTE-M Network Architecture In the LTE-M networkfor communication two types of machines are involvedM2M devices and M2M gateway +e M2M devices con-sidered in this paper are consumer devices which enablecommunication and can communicate directly witheNodeB or connect to gateways using two hop commu-nication +e gateways improve the transmission efficiencyas they help devices to transfer data to eNodeB +isformation of nesting and gateway is based on distance thegateways are the devices which are closer to eNodeB andprovide connection to the devices in the nest eNodeB +egateways can be dynamic or static depending upon theremobility +e device can join and leave a nest dynamically+e LTE-M network architecture with cellular users isshown in Figure 1










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VLSI Design



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Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia










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RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



















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Acknowledgments


References
























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia















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classes is given by

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RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia







(d) End













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Acknowledgments


References
























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






7 Conclusion


Data Availability


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400

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Acknowledgments


References
























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




Acknowledgments


References
























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


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