Research Article On Novel Access and Scheduling Schemes ...downloads.hindawi.com/journals/misy/2016/3973287.pdf · IOT1 IOT2 IOT3 Trac probe F : e spatial distribution of varied IoT

Research ArticleOn Novel Access and Scheduling Schemes forIoT Communications

Zheng Jiang Bin Han Peng Chen Fengyi Yang and Qi Bi

China Telecom Beijing Information Science and Technology Innovation Park Southern Zone of Future Science and Technology CityBeiqijia Town Changping District Beijing 102209 China

Correspondence should be addressed to Zheng Jiang jiangzhctbricomcn

Received 17 June 2016 Revised 2 November 2016 Accepted 10 November 2016

Academic Editor Jose M Barcelo-Ordinas

Copyright copy 2016 Zheng Jiang et al This 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

The Internet of Things (IoT) is expected to foster the development of 5G wireless networks and requires the efficient support fora large number of simultaneous short message communications To address these challenges some existing works utilize newwaveform andmultiuser superposition transmission schemes to improve the capacity of IoT communication In this paper we willinvestigate the spatial degree of freedomof IoT devices based on their distribution then extend themultiuser shared access (MUSA)which is one of the typical MUST schemes to spatial domain and propose two novel schemes that is the preconfigured accessscheme and the joint spatial and code domain scheduling scheme to enhance IoT communication The results indicate that theproposed schemes can reduce the collision rate dramatically during the IoT random access procedure and improve the performanceof IoT communication obviously Based on the simulation results it is also shown that the proposed scheduling scheme can achievethe similar performance to the corresponding brute-force scheduling but with lower complexity

1 Introduction

Smart IoT devices are increasingly becoming an integralpart of our lives Such devices are being used in wide areassuch as intelligent transportation health care environmentalmonitoring energy metering and asset tracking [1] It isestimated that the number of such devices will grow intobillions within few years While IoT applications are char-acterized by some unique features which are different withthe traditional mobile users such as huge number of deviceslow power consumption high frequency access of networkmassive connectivity and shortmessage communication thisputs a great pressure for the existing LTE networks Fortackling the new IoT requirement and improve the networkefficiency for IoT communication the related standardizationwork has been carried out in 3GPP such as the Rel-13LTE MTC (machine type communication) where its featureenables a 14MHz compatible carrier which could be overlaidwithin 20MHz LTE signal without interference [2] and theRel-14 NB-IoT (Narrow-Band Internet of Things) which isfurther improving the LTE IoT support and will providesupport of a massive number of low-throughput devices

low delay sensitivity ultralow device cost low device powerconsumption and optimized network architecture [3] TheNB-IoT can be deployed in-band utilizing resource blockswithin a normal LTE carrier or in the unused resourceblocks within an LTE carrierrsquos guard-band or stand alone fordeployments in a dedicated spectrum

Even with the new support from LTE-A MTC and NB-IoT the varied service requirements of IoT are not satisfiedsufficiently because the new features introduced by eitherMTC or NB-LOT have to consider a certain degree ofbackward compatibility with current LTE system thereforesacrificing the flexibility of system design and new technicalintroduction So all professionals agree that the scenario andrequirement of IoTwill still be one of the key research areas inthe next generation systemdesign and these rapidly increasedrequirements are expected to be satisfied finally in the 5thgeneration (5G) wireless communications [4 5] for examplehigher spectral efficiency massive connectivity and lowerlatency

One of main challenges of IoT communication is massiveconnectivity and shortmessage communication for examplesuch devices aremost of the time inactive but regularly access

Hindawi Publishing CorporationMobile Information SystemsVolume 2016 Article ID 3973287 9 pageshttpdxdoiorg10115520163973287

2 Mobile Information Systems

the network for minorincremental report updates with nohuman interaction To address this challenge a few schemeshave been proposed recently A new waveform schemebased on biorthogonal frequency division multiplexing wasproposed to allow unused frequencies such as guard bandsto transmit IoT data [6] Some multiuser superpositiontransmission (MUST) schemes have been recently activelyinvestigated [7ndash11] MUST can improve spectral efficiencyand accommodate much more IoT devices by introducingthe controllable interferences to realize overloading with thecost of slightly increased receiver complexity In [12] a novelscheduling scheme was proposed to reduce the collision ratefor IoT access It can be observed that all of these above worksaim to improve the connectivity and communication capabil-ity for 5G IoT communication by varied approaches such asnew waveform new spreading code and scheduling scheme

On the other hand it is well known that massive MIMOis one of the core technologies expected to be adopted by5G systems With massive MIMO one sector can serve tensof user equipment (UEs) simultaneously on the same time-frequency resource therefore many schemes were proposedto maximize utilization of the spatial degree of freedom(DOF) introduced by massive MIMO to improve the cellularsystem performance [13ndash16] However until now utilizingthis additional spatial degree of freedom to enhance theIoT communication is not investigated deeply Moreover forIoT devices their distribution and traffic type are differentwith traditional mobile UEs such as their semistatic spatialdistribution short message transmission and dense commu-nication requests within the short span of time Thereforethe spatial DOF utilization and resource scheduling for IoTdevices have unique features and shall be well exploited

In this paper the spatial grouping of IoT devices basedon their distribution is investigated and the novel precon-figured access scheme is proposed to reduce the collisionrate of random access furthermore the joint spatial andcode domain scheduling scheme is proposed to improvethe performance of IoT communications The performanceof the proposed scheme is illustrated by simulations andcompared with the random scheduling scheme The resultsshow that the proposed scheme outperforms the randomschedulingwith andwithoutMUSAbecause of the additionalspatial-domain multiplexing gain It is also shown in oursimulations that the proposed scheduling scheme can exhibitthe close performance to the brute-force scheme with lowercomputational complexity

In this paper in order to clarify the IoT device andconventional mobile subscriber the word ldquousersrdquo refers toIoT devices and word ldquoUEsrdquo refers to conventional mobilesubscribers

The remainder of this paper is organized as followsSection 2 describes the system model and the features of IoTscheduling utilized to introduce the novel access and spatial-domain scheduling scheme Section 3 discusses the proposedaccess and scheduling scheme based on user spatial groupingFurthermore the performance and computational complex-ity of the proposed scheme are analyzed In Section 4 thesimulation results are provided Finally Section 5 concludesthe paper

IOT

BS

device 1

IOTdevice U

xy1

xU

Channel H

Nr

Figure 1 A massive MIMO BS to serve IoT devices

2 System Model and Feature ofIoT Scheduling

21 SystemModel In this section the uplink massive MIMOsystem is given as shown in Figure 1 as most of IoT devicesare the monitors and sensors they gather information fromthe monitoring equipment and environment and then reportto control center

The base station (BS) is equipped with 119873119903 antennas toreceive the messages from 119880 IoT devices with one antenna

We assume that H119899 = [h1119899 h119899119903 119899 h119873119903119899]119879 is thefrequency channel matrix of size 119873119903 times 119880 that representsthe channel between the BS and 119880 IoT devices on the 119899thsubcarrier

h119899119903119899 = [ℎ1119899119903 119899 ℎ119906119899119903 119899 ℎ119880119899119903 119899] ℎ119906119899119903 119899 is the channelstatistic information of device 119906 on the 119899th subcarrier in 119899119903thantenna

The received signal of BS is denoted as

y119899 = H119899x119899 + n119899 (1)

where y119899 denotes the collection of received symbols fromall the 119880 IoT devices on the 119899th subcarrier and x119899 isthe transmitted signal vector of dimensions 119880 times 1 x119899 =[1199091119899 119909119906119899 119909119880119899]119879 where 119909119906 is the transmitted symbol foruser 119906 on the 119899th subcarrier n119899 denotes additive complexGaussian noise with zero mean and variance 12059020

Based on the system model described in (1) the MMSEfrequency-domain equalization is performed to mitigate theuser interference where the MMSE matrix can be expressedas

wMMSE = (H119867119899 H119899 + 1205902I)minus1H119867119899 (2)

The estimated symbol x119899 after MMSE frequency-domainequalization (FDE) is

x119899 = wMMSEy119899 (3)

For uplink BS can achieve channel statistic information(CSI) H119899 of all users by using the uplink pilot send by UEsthen SINR for each user can be calculated by

SINR119906119899 =1003816100381610038161003816h11990611989910038161003816100381610038162

12059020 + sum119906 =1199061015840 1003816100381610038161003816h1199061015840 11989910038161003816100381610038162 (4)

where h119906119899 = [ℎ1199061119899 ℎ119906119873119903 119899]119879 is the channel statisticinformation of119873119903 antennas of 119906th user on the 119899th subcarrierand the SINR119906119899 is the SINR of 119906th user on 119899th subcarrierBased on SINR119906119899 BS can schedule the properUEs to transmittheir information in uplink

Mobile Information Systems 3

22 Feature of IoT Scheduling For conventional cellularsystem the main target of the scheduling scheme is max-imizing the cell throughput and spectral efficiency on thebasis of maintaining user fairness to fulfill user growth trafficrequirement therefore based on this scheduling target thecost function can be mathematically described by

(1198781015840119880 1198781015840119873) = argmax sum119906isin119878119880 119899isin119878119873

log2 (1 + SINR119906119899) (5)

where 119878119880 119878119873 are the sets of all user and subcarrier indexesand 1198781015840119880 1198781015840119873 are the sets of indexes corresponding to thescheduling users and their operating subchannels respec-tively However considering some unique features of IoTapplication as mentioned before the target of schedulingscheme for IoT communication is different from the conven-tional mobile system and shall be to maximize the numberof active IoT devices to fulfill their dense communicationrequests within short span of time

Therefore we define a utility function that

sgn (119909) = 1 119909 ge 00 otherwise

(6)

and the cost function of the number of active user maximiza-tion can be described by


sgn (SINR119906119899 minus 119881SINR) (7)

where 119881SINR is the predetermined threshold of SINR whichshall be the minimum value of SINR to maintain the normaloperation of IoT devices and119881SINR is assumed to be equal forall IoT devices in this paper

In order tomaximize the number of the active IoT devicesand system spectral efficiency some multiuser superpositiontransmission (MUST) schemes have been proposed recently[7ndash11] MUST introduces some controllable interferencesto realize overloading at the cost of slightly increasedreceiver complexity As a result higher spectral efficiency andmore connectivity can be achieved However the existingscheduling schemes of MUST mainly force on the power-domain or code domain to schedule users to multiplex thesame time-frequency resources and not exploit the spatialdegree of freedom (DOF) based on the user distributionto accommodate more user access and improve the systemspectrum efficiency especially for IoT communication

Multiuser shared access (MUSA) is one of the typicalMUST schemes recently proposed In the uplink MUSAsystem symbols of each user are spread by a spreadingsequence which is picked randomly from a sequence poolby access user Then all spreading symbols are transmittedover the same time-frequency resources [21 22] At thereceiver SIC is performed to separate superimposed symbolsaccording to the SINR difference The typical overloadingfactor of MUSA is 150

Actually if the spreading sequences of MUSA in theresource pool can be reused by the different IoT devices basedon their spatial distribution and proper scheduling then the

access number and performance capacity can be improveddramatically Hence the user spatial DOF shall be introducedin the scheduling scheme of MUSA to accommodate moreuser access and improve system capacity for 5G IoT commu-nication

If we research the distribution and location of IoTdeviceswe will find that different types of IoT devices may havethe different spatial distribution For example as shown inFigure 2 there are three types of IoT devices one is thesensors around the parking spots the location height of thistype of sensors is below 2m the second type of IoT devicesis the traffic monitor located on the street pole the height ofthem is about 6ndash8m the third type is thewireless surveillancecameras or environment detectors installed in the shoppingmall or high rise building the height of this type is a uniformdistribution on the vertical height from about 3m to the topof the building So it can be observed that the different usagetypes of IoT devices may be in different spatial locationsmeanwhile once the IoT devices are installed their positionsare not changed frequently not like conventional mobileusers Based on their static spatial characteristic IoT devicescan be separated into different spatial groups previously andthe devices in different spatial groups can share the same setof spreading sequences for random access and data transmis-sion Therefore with multiplexing the spreading sequencesthe overloading factor of systems can be increased obviously

Meanwhile because of the lack of site the eMBB andeMTC services may be deployed in the same site in mostcases this condition is similar to the current 4G condi-tion Utilizing the advantage of eMBB and eMTC colocateddeployment we can use massive MIMO which is usedfor eMBB originally to enhance the eMTC performanceconveniently Hence we can use the spatial DOF to increasethe number of active IoT devices in 5G massive MIMOsystem

Although BS can schedule uplink multiuser transmis-sion based on channel estimation by brute-force schedulingscheme with the explosion of communication request fromIoT devices the complexity of the brute-force scheduling willincrease considerably and become unacceptable

Hence in the next section the preconfigured accessscheme and the joint spatial and code domain schedulingscheme with lower complexity are proposed to improve thesystem performance based on user spatial grouping

3 On Novel Access and Scheduling Schemes

In the proposed schemes the strategy is designed in thefollowing three parts

(1) Based on user (IoT device) location and their channelmeasurement BS can split whole channel space intoseveral disjoint subspaces by using prebeamformingmatrix and then partition its serving user into severalsubspace groups with approximately similar channelcovariance eigenvectors

(2) Based on the spatial grouping of users the preamblecode can be reused by users in different spatial groupduring the random access procedure


Sensors

Detectors

IOT1

IOT2

IOT3

Traffic probe

Figure 2 The spatial distribution of varied IoT devices

(3) For IoT scheduling the spatial and code domaincharacters of each user can be identified by a setof indices that is spatial group index 119892 spreadingsequence index 119898 For users marked with differentspreading sequence indices or users marked withsame spreading sequence index and different spatialgroup indices they can be scheduled on the sametime-frequency resource

Among the above the user spatial grouping preamblemultiplexing and user scheduling in spatial and code domainare three key issues for the system performance the followingdiscussion will focus more on the strategies of these threeissues

31 User Grouping As mentioned before in order to exploiteffectively the access and joint scheduling approach theusers will be partitioned into spatial groups according to thefollowing qualitative principles (1) users in the same grouphave channel covariance eigenspace spanning approximatelya given common subspace which characterizes the spatialgroup BS can get this information byUECSI estimated basedon uplink pilots (2) the subspaces of different spatial groupswhich served on the same time-frequency resource by jointscheduling must be approximately mutually orthogonal or atleast have empty intersection

In this paper the fixed quantization algorithm of usergrouping is employed it is considered as an effective andlow complexity scheme for the implementation in practicalnetwork

In fixed quantization algorithm based on the geometryof the user locations and their channel scattering119880 users canbe divided into 119866 subspace groups and the subspace of 119892thgroup is V119892 isin R119873119903times119903119892 119892 isin 1 2 119866 As the locations ofmost IoT devices are almost fixed the subspace V119892 can be

predetermined based on the CSI of IoT devices and fulfill themaximal 119889119881119892 119892 isin 1 2 119866 119889119881119892 is

119889119881119892 = min 119889119888 (V119892V1198921015840)119889119888 (V119892V1198921015840) = 10038171003817100381710038171003817V119892V119867119892 minus 11988111989210158401198811198671198921015840 100381710038171003817100381710038172119865

(8)

where 119889119888 is the chordal distance of two matrices 119866 is thenumber of spatial groups 119903119892 is the number of dominanteigenvalues of channel covariance R119892 V119892 is the dominanteigenvectors of 119877119892 with respect to 119903119892 and R119892 is the channelcovariance matrix of users in 119892th subspace group

It is easy to see that if sum119866119892=1 119903119892 = 119873119903 then we can chooseV119892 as disjoint subsets of the columns of a unitary matrixof dimensions 119873119903 times 119873119903 such that all group subspaces aremutually orthogonal and 119889119881119892 is maximized Here the disjointblocks of adjacent columns of the unitary DFT matrix can beused as group subspaces

For example if we suppose 119866 = 3 and assign 119903119892 =lfloor119873119903119866rfloor = 119903 and let F denote the unitary DFT matrix thenwe haveV119892 formed by taking the (119892minus1)times119903+1 to119892times119903 columnsof matrix F [23]

Once V119892 is predetermined based on the set of userchannel information h119906119899 119906 = 1 2 119880 the users can bepartitioned into different spatial groups

For user grouping there is a threshold 120572 and let 119889lowast119888 =119889119888(V119892V1198921015840) 119889119892119906= 119889119888(V119892 h119906119899) if this userrsquos 119889119892119906 lt 120572119889lowast119888 weassume that the 119906th user only belongs to spatial group 119892 andif 120572119889lowast119888 lt 119889119892119906 lt 119889lowast119888 and 120572119889lowast119888 lt 1198891198921015840 119906 lt 119889lowast119888 this means that 119906thuser is located in the intersection of V119892 and V1198921015840 subspaceshence user 119906 can be assigned to both 119892th and 1198921015840th spatialgroups Based on the user grouping two sets 119878119906 and 119878119892 canbe obtained 119878119906 is a set of the spatial group indexes 119892 isin (1 119866)that the 119906th user belongs to and 119878119892 is a set of the user indexes119906 isin (1 119880) that the 119892th spatial group contains


In this algorithm the group subspaces are fixed a prioribased on the geometry of users and their CSI When weincrease the number of fixed quantization subspaces toreduce coverage holes the overlapping between differentspatial groups will also increase and cause the strong inter-ferences of intergroups In this case we allocate differentorthogonal spreading sequences dynamically for users whobelong to adjacent groups in order to reduce the interferenceof intergroups and the dynamic allocation scheme of thespreading sequences will be given in the proposed jointscheduling scheme in Section 33

As the location of IoT devices is fixed their channelcharacteristic is more static than the traditional mobile UEstherefore the spatial grouping for IoT devices is more easilyperformed

32 Preconfigured Scheme for Random Access Randomaccess is generally performed when the IoT devices turnon and send their reports to control center In the randomaccess procedure a user sends a random access preamble toBS by choosing it randomly from a preamble pool which ispreallocated by BS Once the different users send the samepreamble the collision of random access will happen

In our scheme based on the spatial orthogonal usergrouping the preamble codes can be shared in the differentspatial groups therefore more IoT devices can initiate arandom access procedure to transmit the uplink message

However as the users located in the overlapping areas ofdifferent spatial groups can cause the random access collisionwith the users in adjacent spatial groups if they select thesame preamble code the preconfigured scheme is proposedto reduce the collision rate of these users by preconfiguringpreamble pools of spatial groups for these users selection intheir random access procedure

The proposed preconfigured scheme is suggested toschedule the users to start from the user who has the largestnumber of spatial groups that this user belongs to becausethis user has the most spatial group resource available forpreconfiguration

In this sense assuming that user1199060 has the largest numberof spatial groups that it belongs to therefore the set 1198781199060 can beobtained and in addition we have the set 1198801198920 = 119892 119892 isin 1198781199060

The 119892lowastth spatial group of which the preamble pools canbe used for the 1199060th user can be selected by [24]

119892lowast = argmax119892isin1198801198920

(119873lowast119892 ) (9)

119873lowast119892 = min119906isin119878119892 119892isin1198801198920

(size (119878119906)) (10)

where size(119878119906) is the utility function to measure the size ofthe set 119878119906 The above process follows the principle of leavingthe maximal spatial groups for the next loop to perform thepreamble pool preconfiguration

The preconfigured scheme for random access can bedescribed as follows

(I) Partition users into predetermined 119866 spatial groupsbased on their CSI

(II) Preconfigure the preamble resources which are usedfor user random access Note that we can share thesame preamble resources for users in different spatialgroups

(III) For the users belong to several spatial groups they canselect the preamble resourceswith one of these groupswhich is selected based on (9) in their random accessprocedure

33 Joint Scheduling in Spatial and Code Domain In thissection we discuss the scheduling scheme for IoT commu-nication Based on user spatial grouping and their initiallyestimated SINR the joint spatial-code scheduling scheme isproposed tomaximize the number of active users Comparedwith brute-force search scheme with exponential complexity[24] the proposed scheme has less complexity and closeperformance

In proposed scheme a schedulingmatrixM119878 with the sizeof 119866 times 119880119873119888 is defined The row wise of M119878 stands for thespatial group indexes and the column wise of M119878 stands forthe user index and subcarrier index which the user requests119866 is the number of spatial groups 119880 and119873119888 are the numberof users and subcarriers respectively If the 119906th user andhis request 119899th subcarrier in the 119892th group have not beenscheduled the (119892 (119906 minus 1)119873119888 + 119899) entry of M119878 denoted by119898119892119906119899 is set to ldquo1rdquo (or otherwise ldquo0rdquo) Andwe define a resourcematrixM119862with the size of119866times119862119873119888 the rowwise ofM119862 standsfor the group indexes and the column wise of M119862 standsfor the spreading sequence and subcarrier index 119862 is thenumber of spreading sequencesThe (119892 (119888minus1)119873119888+119899) entry ofM119862 stands for the 119888th spreading sequence and its respondingto 119899th subcarrier resource in the 119892th spatial group and ifthis spreading sequence and subcarrier resource have beenallocated to the users the (119892 (119888 minus 1)119873119888 + 119899) entry of M119862denoted by 119888119892119888119899 is set to ldquo0rdquo (or otherwise ldquo1rdquo)

The proposed method is to schedule the users to startfrom the group 1198920 whose set S1198920 contains the largest numberof users needed to be scheduled because such spatial groupcan provide the greater flexibility in user scheduling

The index of such group is denoted by

1198920 = argmax119892isin[1119866]

(119873119878119892) (11)

119873119878119892 = sum119906isin119878119892 119899isin[1119873119888]

119898119892119906119899 (12)

Based on the set of user indexes S1198920 we can select the 119906th userwho requests the resource of spatial group 1198920 (ie 119906 isin S1198920)to form the set 119878119906 which contains the spatial group indexesrequested by the 119906th user (ie 119892isin S119906) Then we can obtain119873min119906 via

119873min119906 = min

119892isin119878119906(119873119878119892) (13)

If 119906th user belongs to spatial group S1198920 and also fulfilling thecondition

119906 = argmax119906isin1198781198920 119892isin119878119906

(119873min119906 ) (14)


then we will allocate the spreading sequence and subcarrierresources of spatial group 1198920 to the 119906th user The above pro-cess follows the scheduling principle that is the scheduleduser needs to satisfy the following

(1) Its spatial groups have the largest number of userswhich need to be scheduled

(2) After this user is scheduled the maximal spatial andcode resource can be left for the next loop to performresource allocation

Afterwards the scheduled user the allocated spreadingsequence and subcarriers need to be marked in the schedul-ingmatrixM119878 and resourcematrixM119862Then repeat the aboveprocess until there are no resources to allocate

To sum up the proposed scheme can be described asfollows


(II) Select 1 119880 users where SINR gt 119881SINR from theusers which need to be scheduled

(III) Initialize sets 119878119892 119878119906 and matrixes M119878 M119862 based onstep (I) and step (II)

(IV) Set 119894 = 0(V) While 119894 ⩽ 119866 lowast 119862 lowast 119873119888

(a) increase i by 1(b) update 119878119906 and 119878119892(c) select the set 1198781198920 based on (11)(d) schedule the user 119906 in group 1198920 based on (13)

and (14)(e) allocate the resource indices (119892 119888 119899) to user 119906(f) replace the element of 119892th row and (119906minus1)119873119888 +119899

column with 0 inM119878 that is119898119892119906119899 = 0(g) replace the element of 119892th row and (119888 minus 1)119873119888 +119899

column with ldquo0rdquo inM119862 that is 119888119892119888119899 = 0(h) if M119862 orM119878 is a zero matrix break

(VI) End while

In the above process for each loop one spreadingsequence and subcarrier in one spatial group are allocated toa selected user Therefore the total number of loops is equalto the number of scheduled users of the system Accordingto the objective of the number of active users maximizationthe scheduling method is considered to be optimum if thenumber of loops is maximized However in general theproposed method cannot guarantee such global optimalityInstead it can achieve local optimality by giving the maximalnumber of residual scheduling resources for the next loop toperform resource allocation

In the case of existing multiple solutions to (11) we selecta spatial group as 1198781198920 with its term max(119873min

119906 ) to be themaximum among all the candidates Based on our discus-sions above such selectionmaintains the suboptimality of theproposed method

34 Performance Analysis As the spatial DOF is introducedas a critical factor to maximize the number of active users inthe proposed scheduling scheme the probability of attainingmaximal DOF can be employed to analyze the schemeperformance [24]

We define 120588119906119892119888119899 to be the probability for the 119906th userto be scheduled to the 119888th spreading sequence and 119899th sub-carrier in the 119892th spatial groups and assume 119880 users are dis-tributed evenly onto119866 orthogonal spatial groups where thereexist119866119862119873119888 unassigned spatial-code and frequency resourceswhich are statistically independent with each other Mean-while we assume 120588119906119892119888119899 forall119906 119892 119888 119899 are identical with respectto the indexes (119906 119892 119888 119899) and thus 120588119906119892119888119899 is denoted by 120588

For the first user there exist 119866119862119873119888 unassigned spatial-code resources in a subcarrier Hence the probability for thefirst user to have one spatial-code resource to access is

119866sum119892=1

119862sum119888=1

119873119888sum119899=1

120588119906119892119888119899 = 119866119862119873119888120588 (15)

Once the first user is assigned the second user has only119866119862119873119888ndash1 options Accordingly the 119906th user has only 119866119862119873119888 minus119906+1 options and its probability to have one resource to accessis (119866119862119873119888 minus 119906 + 1)120588

Hence the probability for all of119866119862119873119888 users to be assignedis

119866119862119873119888prod119906=1

((119866119862119873119888 minus 119906 + 1) 120588) = (119866119862119873119888)120588119866119862119873119888 (16)

In total there exist 119862119866119862119873119888119880 groups as above thus the overallprobability of achieving the maximal degree of freedom isgiven by

Probmax = ( 119880119866119862119873119888) (119866119862119873119888)120588

119866119862119873119888 (17)

From equation (17) it can be observed that the maximalspatial-code domain DOF can be attained with very highprobability for the proposed scheme when the number ofusers 119880 is large (eg massive number of IoT devices)

35 Computational Complexity Discussion For the proposedscheme the computational complexity mainly comes fromthe order statistics in (11)ndash(14) Given the maximal DOFof 119866119862119873119888 the complexity of order statistics in (11) is upperbounded by119874((119880119866119873119888)2) the same upper bound applies alsoto the procedure from (12) and (14) Since the maximal num-ber of loops is119866119862119873119888 the overall computational complexity isupper bounded by 119874((119880119866119873119888)2119866119862119873119888) which is significantlylower than the exponential complexity offered by the brute-force search [25]

4 Performance Evaluation and Analysis

In this section computer simulations were used to evaluatethe proposed schemes in terms of the probability of attaining


Table 1 Major simulation parameters

Parameters Values

Tx power 23 dBm for 3D-UMa 500m for UEs and IoTdevices

Duplex FDDTx antennaconfigurations

Tx antenna elements config 1 omni for bothUEs and IoT devices

Rx antennaconfigurations

BS Rx antenna elements config 8 times 4 times 2(plusmn45)05 120582H08 120582V

Traffic modelDate rate 05Mbps TR36814 Ftp model 2 forUEs [17]Data rate 80 kbps for IoT devices

Modulation AMC for UEsQPSK for IoT devices

Coding scheme AMC for UEsTurbo Code (code rate 023) for IoT

System bandwidth 10MHz (50 PRBs)

Power control Open loop fractional power control for UEsalpha = 07 1198750 = minus80 dBm

Networksynchronization Synchronized

MUSA Spreading sequences are generated bypseudorandom sequences

UE distribution Number of UEs = 300 distribution accordingto 36873 [18] Poisson arrival with rate 10sec

IoT distributionNumber of devices varied from 1 to 30000 insteps of 3000 distribution according to 36873Access intensity as traffic model 2 in [19]Non-ideal channel estimation

Receiver MMSE-IRC detailed guidelines according toRel-12 assumptions [20]

SRS 1Tx 5ms periodicity wideband

maximal number of active users the collision rate andthroughput performance

In our simulation there is a single BS (Macro) covering anarea that includes UEs and IoT devices BS is equipped withantenna array of 8times8 x-pol elements For the simulation thereare119866 = 8 prebeamforming groups by using prebeamformingvectors V119892 119892 isin 1 8 which can be given by the unitaryDFTmatrix F of size 8times8V119892 is formed by taking the (119892-1)times119903 + 1 119903 = 1 Hence UEs and IoT devices in serving cell canbe partitioned into eight spatial groups based on their CSIestimation For IoT scheduling the threshold of SINR119881SINR is0 dB The major simulation assumptions are listed in Table 1

41 The Probability of Attaining Maximal Number of ActiveIoT Devices Figure 3 displays the simulation results of theprobability of obtaining maximal number of active usersfor the joint spatial-code scheduling scheme and the brute-force scheme It can be observed that with the number ofIoT devices varying from 3000 to 30000 the probability ofattaining maximal number of active users increases fromabout 50 to 90 Meanwhile the result shows that theprobability of the proposed scheme and the brute-forcescheme is almost the same to each other and the trend of

03 06 09 12 15 18 21 24 27 305

06

07

08

09

1

Number of IOT devices

Prob

abili

ty

Analytical methodProposed schemeBrute-force scheme

times104

Figure 3 The probability of attaining maximal number versus thenumber of IoT devices

03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te

Random access wo MUSARandom access wMUSA

Proposed schemeBrute-force scheme

times104

Figure 4 Collision rate versus the number of IoT devices

simulation results is coincided with that of analytical methodof (17)

42 The Collision Rate of Random Access Let us denote totalnumber of devices which send random access request as 119870119901and total number of devices that experienced collision as 119870119888Then we define the collision rate 119862119877 as

119862119877 = 119870119862119870119875 (18)

Figure 4 displays the collision rate of the preconfiguredaccess scheme against the random access scheme with andwithout MUSA and the brute-force scheme with differentnumbers of IoT devices


03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05

Number of IOT devices and UEs

Col

lisio

n ra

te



times104

Figure 5 Collision rate versus the number of IoT devices and UEs

The results show that with the number of IoT devicesfrom 3000 to 30000 the collision rates of the random accessscheme without MUSA increase from 17 to 472 and thecollision rates of the random scheme with MUSA increasefrom 09 to 232 As introduced in the spatial DOF thecollision rate of the proposed scheme can reduce to about 5when the number of IoT devices is 30000 From Figure 4 itcan also seem that the performance of the proposed schemeand the brute-force scheme is close to each other

Figure 5 displays the performance of these schemeswith different numbers of IoT devices and UEs The resultsshow that the proposed access scheme still has the betterperformance than the random access scheme Meanwhile asthe number of UEs is very small compared with IoT devicesthey have no effect on the result of collision rate Hence thesimulation results of collision rate with UE and IoT randomaccess in Figure 5 are almost the same to the results of onlyIoT random access in Figure 4

43 Throughput Performance The simulation results of cellaverage spectrum efficiency are provided in Figures 6 and 7Figure 6 displays the cell spectrum efficiency of the proposedscheme the random scheduling scheme with and withoutMUSA and the brute-force scheme versus the number ofIoT devices The results show that the proposed scheme canachieve higher average spectrum efficiency than the randomscheduling both with and without MUSA as it can achieveadditional spatial-domain multiplexing gain The proposedscheme achieves about 633 and 1049 of the meanimprovement rate of spectrum efficiency compared with therandom scheduling with and without MUSA respectivelywith the number of IoT devices varying from 3000 to 30000per cell

Figure 7 displays the average spectrum efficiency of theseschemes versus the number of IoT devices and UEs Itcan be observed that the proposed scheme still have the

0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)

Random scheduling wo MUSARandom scheduling wMUSA

Proposed schedulingBrute-force scheduling

times104

Figure 6 Cell average spectrum efficiency (SE) comparison versusthe number of IoT devices

0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104

Figure 7 Cell average spectrum efficiency (SE) comparison versusthe number of IoT devices and UEs

significant performance gain compared with the randomscheduling scheme with and without MUSA and the meanimprovement rate of spectrum efficiency is about 424 and919 respectively Furthermore as AMC is used in uplinkUE scheduling procedures the data transmission of UEs canachieve higher spectrum efficiency than IoT devices in thehybrid scheduling of UEs and IoT devices Therefore theresults of spectrum efficiency in Figure 7 are higher than thatin Figure 6

FromFigures 6 and 7 it can be observed that the proposedscheduling scheme has the similar performance to the brute-force scheme with computation complexity reduction


5 Conclusion

In this paper two novel schemes are proposed to enhancerandom access and improve the system capacity for IoTcommunication based on user spatial grouping

In the proposed preconfigured access scheme the pream-ble resources are multiplexed to reduce the collision rate ofrandom access based on user spatial grouping In the pro-posed joint scheduling scheme each IoT device is identifiedwith a set of spatial-code indices based on these indicesBS can schedule users to transmission on the same time-frequency resources to achieve additional spatial-domainmultiplexing gain The simulation results validate that thepreconfigured access scheme can obviously reduce the col-lision rate and the proposed scheduling scheme can achieveabout 633 and 1049 of the mean improvement rate ofspectrum efficiency compared with the random schedul-ing with and without MUSA respectively Furthermorethe results show that the proposed scheduling scheme canachieve similar performance to brute-force scheme withlower scheduling complexity

Disclosure

Qi Bi is a Fellow at IEEE Bin Han is currently working atChina Telecom Corporation Limited Technology InnovationCenter

Competing Interests

The authors declare that there is no conflict of interestsregarding the publication of this article

References

[1] L Atzori A Iera and G Morabito ldquoThe internet of things asurveyrdquoComputer Networks vol 54 no 15 pp 2787ndash2805 2010

[2] 3GPP R1-142919 ldquoNarrow band LTE for MTC in LTE Rel-13rdquoMediaTek RAN178 August 2014

[3] J Gozalvez ldquoNew 3GPP Standard for IoTrdquo IEEE VehicularTechnology Magazine vol 11 no 1 pp 14ndash20 2016

[4] F Boccardi R Heath Jr A Lozano T L Marzetta and PPopovski ldquoFive disruptive technology directions for 5Grdquo IEEECommunications Magazine vol 52 no 2 pp 74ndash80 2014

[5] A Osseiran F Boccardi V Braun et al ldquoScenarios for 5Gmobile and wireless communications the vision of the METISprojectrdquo IEEE Communications Magazine vol 52 no 5 pp 26ndash35 2014

[6] M Kasparick G Wunder P Jung and D Maryopi ldquoBi-orthogonal waveforms for 5G random access with short mes-sage supportrdquo in Proceedings of the 20th European WirelessConference (EW rsquo14) pp 293ndash298 Barcelona Spain May 2014

[7] Y Saito Y Kishiyama A Benjebbour T Nakamura A Liand K Higuchi ldquoNon-orthogonal multiple access (NOMA) forcellular future radio accessrdquo in Proceedings of the IEEE 77thVehicular Technology Conference (VTC rsquo13) pp 1ndash5 DresdenGermany June 2013

[8] J Van De Beek and B M Popovic ldquoMultiple access with low-density signaturesrdquo in Proceedings of the IEEE Global Telecom-munications Conference (GLOBECOM rsquo09) pp 1ndash6 December2009

[9] M Al-Imari P Xiao M A Imran and R Tafazolli ldquoUplinknon-orthogonal multiple access for 5G wireless networksrdquo inProceedings of the 11th International Symposium on WirelessCommunications Systems (ISWCS rsquo14) pp 781ndash785 August 2014

[10] H Nikopour and H Baligh ldquoSparse code multiple accessrdquo inProceedings of the IEEE 24th Annual International SymposiumonPersonal Indoor andMobile Radio Communications (PIMRCrsquo13) pp 332ndash336 IEEE London UK September 2013

[11] L Dai B Wang Y Yuan S Han C I and Z Wang ldquoNon-orthogonal multiple access for 5G solutions challenges oppor-tunities and future research trendsrdquo IEEE CommunicationsMagazine vol 53 no 9 pp 74ndash81 2015

[12] P K Wali and D Das ldquoA novel access scheme for IoTcommunications in LTE-Advanced networkrdquo in Proceedings ofthe IEEE International Conference on Advanced Networks andTelecommunication Systems (ANTS rsquo14) pp 1ndash6 New DelhiIndia December 2014

[13] E G Larsson O Edfors F Tufvesson and T L MarzettaldquoMassive MIMO for next generation wireless systemsrdquo IEEECommunications Magazine vol 52 no 2 pp 186ndash195 2014

[14] K Zheng L Zhao J Mei B Shao W Xiang and L HanzoldquoSurvey of large-scale MIMO systemsrdquo IEEE CommunicationsSurveys amp Tutorials vol 17 no 3 pp 1738ndash1760 2015

[15] T L Marzetta ldquoNoncooperative cellular wireless with unlim-ited numbers of base station antennasrdquo IEEE Transactions onWireless Communications vol 9 no 11 pp 3590ndash3600 2010

[16] Z Jiang BHan P Chen F Yang andQ Bi ldquoDesign of joint spa-tial and power domainmultiplexing scheme formassiveMIMOsystemsrdquo International Journal of Antennas and Propagationvol 2015 Article ID 368463 10 pages 2015

[17] 3GPP TR36814 V900 ldquoFurther advancements for E-UTRAphysical layer aspectsrdquo March 2010

[18] 3GPP TR36873 (V1220) ldquoStudy on 3D channel model forLTErdquo July 2015

[19] 3GPP TR 37868 V081 lsquoStudy on RAN Improvements forMachine-type Communicationsrsquo August 2011

[20] 3GPP TR36866 (V1201) ldquoStudy on network-assisted inter-ference cancellation and suppression (NAIC) for LTErdquo March2014

[21] B Wang K Wang Z Lu T Xie and J Quan ldquoComparisonstudy of non-orthogonal multiple access schemes for 5Grdquoin Proceedings of the 10th IEEE International Symposium onBroadband Multimedia Systems and Broadcasting (BMSB rsquo15)Ghent Belgium June 2015

[22] L Dai Z Wang and S Chen ldquoA novel uplink multiple accessscheme based on TDS-FDMArdquo IEEE Transactions on WirelessCommunications vol 10 no 3 pp 757ndash761 2011

[23] J Nam A Adhikary J-Y Ahn and G Caire ldquoJoint spatialdivision and multiplexing opportunistic beamforming usergrouping and simplified downlink schedulingrdquo IEEE Journal onSelected Topics in Signal Processing vol 8 no 5 pp 876ndash8902014

[24] J Hou N Yi and YMa ldquoJoint space-frequency user schedulingfor MIMO random beamforming with limited feedbackrdquo IEEETransactions on Communications vol 63 no 6 pp 2224ndash22362015

[25] A Levitin Introduction to the Design amp Analysis of AlgorithmsPearson Educ Harlow UK 3rd edition 2011

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

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Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


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Biomedical Imaging


ArtificialNeural Systems

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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



the network for minorincremental report updates with nohuman interaction To address this challenge a few schemeshave been proposed recently A new waveform schemebased on biorthogonal frequency division multiplexing wasproposed to allow unused frequencies such as guard bandsto transmit IoT data [6] Some multiuser superpositiontransmission (MUST) schemes have been recently activelyinvestigated [7ndash11] MUST can improve spectral efficiencyand accommodate much more IoT devices by introducingthe controllable interferences to realize overloading with thecost of slightly increased receiver complexity In [12] a novelscheduling scheme was proposed to reduce the collision ratefor IoT access It can be observed that all of these above worksaim to improve the connectivity and communication capabil-ity for 5G IoT communication by varied approaches such asnew waveform new spreading code and scheduling scheme

On the other hand it is well known that massive MIMOis one of the core technologies expected to be adopted by5G systems With massive MIMO one sector can serve tensof user equipment (UEs) simultaneously on the same time-frequency resource therefore many schemes were proposedto maximize utilization of the spatial degree of freedom(DOF) introduced by massive MIMO to improve the cellularsystem performance [13ndash16] However until now utilizingthis additional spatial degree of freedom to enhance theIoT communication is not investigated deeply Moreover forIoT devices their distribution and traffic type are differentwith traditional mobile UEs such as their semistatic spatialdistribution short message transmission and dense commu-nication requests within the short span of time Thereforethe spatial DOF utilization and resource scheduling for IoTdevices have unique features and shall be well exploited

In this paper the spatial grouping of IoT devices basedon their distribution is investigated and the novel precon-figured access scheme is proposed to reduce the collisionrate of random access furthermore the joint spatial andcode domain scheduling scheme is proposed to improvethe performance of IoT communications The performanceof the proposed scheme is illustrated by simulations andcompared with the random scheduling scheme The resultsshow that the proposed scheme outperforms the randomschedulingwith andwithoutMUSAbecause of the additionalspatial-domain multiplexing gain It is also shown in oursimulations that the proposed scheduling scheme can exhibitthe close performance to the brute-force scheme with lowercomputational complexity

In this paper in order to clarify the IoT device andconventional mobile subscriber the word ldquousersrdquo refers toIoT devices and word ldquoUEsrdquo refers to conventional mobilesubscribers

The remainder of this paper is organized as followsSection 2 describes the system model and the features of IoTscheduling utilized to introduce the novel access and spatial-domain scheduling scheme Section 3 discusses the proposedaccess and scheduling scheme based on user spatial groupingFurthermore the performance and computational complex-ity of the proposed scheme are analyzed In Section 4 thesimulation results are provided Finally Section 5 concludesthe paper

IOT

BS

device 1

IOTdevice U

xy1

xU

Channel H

Nr

Figure 1 A massive MIMO BS to serve IoT devices

2 System Model and Feature ofIoT Scheduling

21 SystemModel In this section the uplink massive MIMOsystem is given as shown in Figure 1 as most of IoT devicesare the monitors and sensors they gather information fromthe monitoring equipment and environment and then reportto control center

The base station (BS) is equipped with 119873119903 antennas toreceive the messages from 119880 IoT devices with one antenna

We assume that H119899 = [h1119899 h119899119903 119899 h119873119903119899]119879 is thefrequency channel matrix of size 119873119903 times 119880 that representsthe channel between the BS and 119880 IoT devices on the 119899thsubcarrier

h119899119903119899 = [ℎ1119899119903 119899 ℎ119906119899119903 119899 ℎ119880119899119903 119899] ℎ119906119899119903 119899 is the channelstatistic information of device 119906 on the 119899th subcarrier in 119899119903thantenna

The received signal of BS is denoted as

y119899 = H119899x119899 + n119899 (1)

where y119899 denotes the collection of received symbols fromall the 119880 IoT devices on the 119899th subcarrier and x119899 isthe transmitted signal vector of dimensions 119880 times 1 x119899 =[1199091119899 119909119906119899 119909119880119899]119879 where 119909119906 is the transmitted symbol foruser 119906 on the 119899th subcarrier n119899 denotes additive complexGaussian noise with zero mean and variance 12059020

Based on the system model described in (1) the MMSEfrequency-domain equalization is performed to mitigate theuser interference where the MMSE matrix can be expressedas

wMMSE = (H119867119899 H119899 + 1205902I)minus1H119867119899 (2)

The estimated symbol x119899 after MMSE frequency-domainequalization (FDE) is

x119899 = wMMSEy119899 (3)

For uplink BS can achieve channel statistic information(CSI) H119899 of all users by using the uplink pilot send by UEsthen SINR for each user can be calculated by

SINR119906119899 =1003816100381610038161003816h11990611989910038161003816100381610038162

12059020 + sum119906 =1199061015840 1003816100381610038161003816h1199061015840 11989910038161003816100381610038162 (4)

where h119906119899 = [ℎ1199061119899 ℎ119906119873119903 119899]119879 is the channel statisticinformation of119873119903 antennas of 119906th user on the 119899th subcarrierand the SINR119906119899 is the SINR of 119906th user on 119899th subcarrierBased on SINR119906119899 BS can schedule the properUEs to transmittheir information in uplink




log2 (1 + SINR119906119899) (5)



sgn (119909) = 1 119909 ge 00 otherwise

(6)


















Sensors

Detectors

IOT1

IOT2

IOT3

Traffic probe








119889119881119892 = min 119889119888 (V119892V1198921015840)119889119888 (V119892V1198921015840) = 10038171003817100381710038171003817V119892V119867119892 minus 11988111989210158401198811198671198921015840 100381710038171003817100381710038172119865

(8)
















(119873lowast119892 ) (9)


(size (119878119906)) (10)










1198920 = argmax119892isin[1119866]

(119873119878119892) (11)

119873119878119892 = sum119906isin119878119892 119899isin[1119873119888]

119898119892119906119899 (12)


119873min119906 = min

119892isin119878119906(119873119878119892) (13)


119906 = argmax119906isin1198781198920 119892isin119878119906

(119873min119906 ) (14)















(VI) End while







119866sum119892=1

119862sum119888=1

119873119888sum119899=1

120588119906119892119888119899 = 119866119862119873119888120588 (15)



119866119862119873119888prod119906=1

((119866119862119873119888 minus 119906 + 1) 120588) = (119866119862119873119888)120588119866119862119873119888 (16)


Probmax = ( 119880119866119862119873119888) (119866119862119873119888)120588

119866119862119873119888 (17)







Parameters Values




















03 06 09 12 15 18 21 24 27 305

06

07

08

09

1


Prob

abili

ty


times104


03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104




119862119877 = 119870119862119870119875 (18)



03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104






0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104


0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104





5 Conclusion



Disclosure


Competing Interests


References

































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






log2 (1 + SINR119906119899) (5)



sgn (119909) = 1 119909 ge 00 otherwise

(6)


















Sensors

Detectors

IOT1

IOT2

IOT3

Traffic probe








119889119881119892 = min 119889119888 (V119892V1198921015840)119889119888 (V119892V1198921015840) = 10038171003817100381710038171003817V119892V119867119892 minus 11988111989210158401198811198671198921015840 100381710038171003817100381710038172119865

(8)
















(119873lowast119892 ) (9)


(size (119878119906)) (10)










1198920 = argmax119892isin[1119866]

(119873119878119892) (11)

119873119878119892 = sum119906isin119878119892 119899isin[1119873119888]

119898119892119906119899 (12)


119873min119906 = min

119892isin119878119906(119873119878119892) (13)


119906 = argmax119906isin1198781198920 119892isin119878119906

(119873min119906 ) (14)















(VI) End while







119866sum119892=1

119862sum119888=1

119873119888sum119899=1

120588119906119892119888119899 = 119866119862119873119888120588 (15)



119866119862119873119888prod119906=1

((119866119862119873119888 minus 119906 + 1) 120588) = (119866119862119873119888)120588119866119862119873119888 (16)


Probmax = ( 119880119866119862119873119888) (119866119862119873119888)120588

119866119862119873119888 (17)







Parameters Values




















03 06 09 12 15 18 21 24 27 305

06

07

08

09

1


Prob

abili

ty


times104


03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104




119862119877 = 119870119862119870119875 (18)



03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104






0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104


0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104





5 Conclusion



Disclosure


Competing Interests


References

































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Sensors

Detectors

IOT1

IOT2

IOT3

Traffic probe








119889119881119892 = min 119889119888 (V119892V1198921015840)119889119888 (V119892V1198921015840) = 10038171003817100381710038171003817V119892V119867119892 minus 11988111989210158401198811198671198921015840 100381710038171003817100381710038172119865

(8)
















(119873lowast119892 ) (9)


(size (119878119906)) (10)










1198920 = argmax119892isin[1119866]

(119873119878119892) (11)

119873119878119892 = sum119906isin119878119892 119899isin[1119873119888]

119898119892119906119899 (12)


119873min119906 = min

119892isin119878119906(119873119878119892) (13)


119906 = argmax119906isin1198781198920 119892isin119878119906

(119873min119906 ) (14)















(VI) End while







119866sum119892=1

119862sum119888=1

119873119888sum119899=1

120588119906119892119888119899 = 119866119862119873119888120588 (15)



119866119862119873119888prod119906=1

((119866119862119873119888 minus 119906 + 1) 120588) = (119866119862119873119888)120588119866119862119873119888 (16)


Probmax = ( 119880119866119862119873119888) (119866119862119873119888)120588

119866119862119873119888 (17)







Parameters Values




















03 06 09 12 15 18 21 24 27 305

06

07

08

09

1


Prob

abili

ty


times104


03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104




119862119877 = 119870119862119870119875 (18)



03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104






0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104


0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104





5 Conclusion



Disclosure


Competing Interests


References

































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in













(119873lowast119892 ) (9)


(size (119878119906)) (10)










1198920 = argmax119892isin[1119866]

(119873119878119892) (11)

119873119878119892 = sum119906isin119878119892 119899isin[1119873119888]

119898119892119906119899 (12)


119873min119906 = min

119892isin119878119906(119873119878119892) (13)


119906 = argmax119906isin1198781198920 119892isin119878119906

(119873min119906 ) (14)















(VI) End while







119866sum119892=1

119862sum119888=1

119873119888sum119899=1

120588119906119892119888119899 = 119866119862119873119888120588 (15)



119866119862119873119888prod119906=1

((119866119862119873119888 minus 119906 + 1) 120588) = (119866119862119873119888)120588119866119862119873119888 (16)


Probmax = ( 119880119866119862119873119888) (119866119862119873119888)120588

119866119862119873119888 (17)







Parameters Values




















03 06 09 12 15 18 21 24 27 305

06

07

08

09

1


Prob

abili

ty


times104


03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104




119862119877 = 119870119862119870119875 (18)



03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104






0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104


0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104





5 Conclusion



Disclosure


Competing Interests


References

































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in

















(VI) End while







119866sum119892=1

119862sum119888=1

119873119888sum119899=1

120588119906119892119888119899 = 119866119862119873119888120588 (15)



119866119862119873119888prod119906=1

((119866119862119873119888 minus 119906 + 1) 120588) = (119866119862119873119888)120588119866119862119873119888 (16)


Probmax = ( 119880119866119862119873119888) (119866119862119873119888)120588

119866119862119873119888 (17)







Parameters Values




















03 06 09 12 15 18 21 24 27 305

06

07

08

09

1


Prob

abili

ty


times104


03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104




119862119877 = 119870119862119870119875 (18)



03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104






0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104


0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104





5 Conclusion



Disclosure


Competing Interests


References

































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





Parameters Values




















03 06 09 12 15 18 21 24 27 305

06

07

08

09

1


Prob

abili

ty


times104


03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104




119862119877 = 119870119862119870119875 (18)



03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104






0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104


0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104





5 Conclusion



Disclosure


Competing Interests


References

































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




03 06 09 12 15 18 21 24 27 30

005

01

015

02

025

03

035

04

045

05


Col

lisio

n ra

te



times104






0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104


0 03 06 09 12 15 18 21 24 27 30

5

10

15

20

25

30


Spec

trum

effici

ency

(bps

Hz)



times104





5 Conclusion



Disclosure


Competing Interests


References

































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




5 Conclusion



Disclosure


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



Documents

Research Article On Novel Access and Scheduling Schemes ...downloads.hindawi.com/journals/misy/2016/3973287.pdf · IOT1 IOT2 IOT3 Trac probe F : e spatial distribution of varied IoT