Borderless Mobility in 5G Outdoor Ultra-Dense Networks · Corresponding author: P. Kela ([email protected]) ABSTRACT This paper considers borderless 5G ultra-dense networks

SPECIAL SECTION ON ULTRA-DENSE CELLULAR NETWORKS

Received June 30, 2015, accepted August 10, 2015, date of publication August 20, 2015, date of current version September 3, 2015.

Digital Object Identifier 10.1109/ACCESS.2015.2470532

Borderless Mobility in 5G OutdoorUltra-Dense NetworksPETTERI KELA1, JUSSI TURKKA2, (Member, IEEE), AND MÁRIO COSTA1, (Member, IEEE)1Huawei Technologies Oy (Finland), Ltd., Helsinki 00180, Finland2Magister Solutions Ltd., Jyväskylä 40720, Finland

Corresponding author: P. Kela ([email protected])

ABSTRACT This paper considers borderless 5G ultra-dense networks (UDNs). In particular, a novelscheduling algorithm is proposed that achieves a more uniform distribution of user-throughput than thatof the state-of-the-art maximum-throughput (MT) schedulers. The proposed scheduling algorithm alsotakes the coherence time of the channel into account as well as the impact to the acquired channel stateinformation. A novel radio frame structure that is appropriate for achieving a 1-ms round trip time latencyis also proposed. Such a low latency allows one to employ multiuser as well as cooperative multiple inputmultiple output schemes for mobile users. An evaluation of matched-filter and zero-forcing precoding formobile users in UDNs is included. The performance of the proposed 5G UDN concept is assessed using asystem-level simulator. Extensive numerical results show that the proposed borderless scheduling conceptachieves ∼77% higher median user-throughput than that of the MT scheduler at the cost of∼17% lower area-throughput. Such results are obtained for a high density of mobile users at velocitiesof ∼50 km/h.

INDEX TERMS 5G networks, mobility, scheduling, ultra-dense networks.

I. INTRODUCTION5G should provide native support for new kinds of networkdeployments such as ultra-dense networks (UDNs) [1].As emphasized in [2]–[4], 5G systems will leverage advancedsmall cell technology to bring uniform quality of experienceto a rapidly growing population of mobile cellular users.The exponential growth in the population of wireless mobileusers and data demand in cellular networks will be one ofthe biggest challenges for 5G during the next decade. It isexpected that 5G era will revolutionize the way we communi-cate by supporting new immersive applications requiring ultralow latency and high throughput in outdoor environments.To make this revolution a reality also for vehicular users,robust and reliable connectivity solutions are needed as wellas the ability to efficiently manage mobility. After all, thesupport for mobility is (and has been) one of the key addedvalue services of cellular networks.

The general consensus on the requirements for 5G are:• 1000× increase in area capacity with respect to LongTerm Evolution-Advanced (LTE-A).

• 1ms Round Trip Time (RTT) latency. This is a10× improvement from that of the LTE-A.

• 100× improvement in energy efficiency in terms ofJoules/bit.

• 10− 100× reduction in cost of deployment.

• Mobility support and always-on connectivity of usersthat have high throughput requirements.

In the dawn of 5G era, it will be extremely challengingto reach 1000× increase in capacity and still bring ‘desktop-like’ experience to mobile users regardless of their location.There have been great achievements in utilizing mmWave,Coordinated Multipoint (CoMP) and massive Multiple InputMultiple Output (MIMO) communication for the user accesslink, but it is not expected that these technologies can easilysupport high densities of highly mobile users in the nearfuture due to their sensitivity to Channel State Informa-tion (CSI) aging. Supporting vehicular users will be a sig-nificant differentiator for 5G since passengers in connectedcars [2] are behaving more and more like indoor users,thus making them primary consumers of Internet content.Furthermore, new innovative services such as augmentedreality for vehicles and mobile ultra-high definition videostreaming require low latency as well as high data rates [3].In order to achieve such throughput and QoE requirementsit is expected that network deployments in 5G will be muchdenser compared to that of 4G networks. Hence, new solu-tions are needed to allow 5G UDN to meet near-wirelinelatencies and to provide uniform quality of experience formassive amounts of users in highly populated urban outdoorenvironments consisting of dense deployment of small cells.

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P. Kela et al.: Borderless Mobility in 5G Outdoor UDNs

However, as network density increases, inter-cellinterference starts to limit the wireless system performanceespecially at cell borders. Mitigation of inter-cell interfer-ence has been studied extensively in previous work e.g., bymeans of CoMP [5], coordinated precoding [6] and NetworkMIMO [7], [8]. In particular, an interesting comparisonbetween massive MIMO and network MIMO can be foundin [9] where it is shown that cooperative MIMO schemesdo not typically outperform a non-cooperative massiveMIMO approach. The aforementioned techniques haveshown promising results in micro cellular environmentsbut their sensitivity to CSI uncertainties limits their usagein mobile scenarios. Moreover, and to the best of ourknowledge, the previous work has not addressed the perfor-mance of cooperative schemes in UDNs for highly mobileusers.

This paper presents a novel scheduling algorithm thatmakes it possible for 5GUDNs to provide a borderless experi-ence in outdoor environments. This is achieved by deductinglow user throughputs and unreliable link connectivity amongmobile users and serving cells. User velocities up to 100km/hare considered. A novel frame structure is also proposed thatis particularly attractive for UDNs and low-power mobiledevices.

This paper is organized as follows. In Section II,we describe physical layer and frame structure design ofthe 5G ultra-dense outdoor radio access concept. Section IIIdescribes in detail our proposed packet scheduling frameworkand discusses how the proposed scheduling algorithms cantolerate mobility and adapt to user specific coherence times.Section IV considers interference coordination and accessnode coordination in the ultra-dense outdoor environments.In Section V, different alternatives for precoder design aregiven. Section VI describes the employed simulation modeland Section VII provides numerical results illustrating theperformance of the proposed algorithms and solutions forultra-dense outdoor networks. Section VIII concludes thepaper.

II. 5G ULTRA-DENSE OUTDOOR RADIO ACCESSIn this section, a novel radio frame structure for5G ultra-dense networks (UDNs) is proposed. It is appro-priate for achieving a round trip time (RTT) latency of 1ms,and facilitates using multiuser as well as coordinated MIMOtechniques with mobile users [10]. The proposed framestructure supports a large amount of users, and the corre-sponding subframe is significantly shorter than thatof LTE-A. Short subframes facilitate using energy-efficientdiscontinuous reception (DRX) schemes for bursty and highbit-rate data traffic. In fact, video streaming and web brows-ing are expected to represent a significant part of the overallfuture traffic [11]. In particular, data bursts consisting of largebandwidth and short duration facilitate the maximization ofsleep time between bursts. We note that preliminary resultsof the frame structure proposed in this section have beenpresented in [12]. However, this section provides a few

FIGURE 1. Proposed frame structure for TDD based 5G UDNs. CSIT isobtained from UL beacons. A control channel with small bandwidth at thecenter of the frequency band allows for low-power monitoring and efficientDRX schemes due to the smaller sampling rate. Short subframe durationfacilitates achieving RTT latencies smaller than 1ms.

modifications and a more detailed discussion than that givenin [12].

A. PROPOSED RADIO FRAME STRUCTUREThe proposed frame structure is illustrated in Fig. 1. The firstfive symbols are dedicated for uplink (UL) beacon transmis-sions. Such beacons are used for obtaining low latency CSIat the transmitter (CSIT) as well as user positioning informa-tion. Each beacon duration equals that of a symbol, i.e. 3.2µs.The UL beacons may be shared among users in the frequencydomain. More precisely, the first symbol of the frame struc-ture is dedicated to UL narrowband beaconing and facilitatesmobility management in UDNs. Narrowband beaconing maybe used for estimating the user position thus allowing forpaging-free type of solutions to be employed [13]. Moreover,knowing the position of the user and that of the closest access-nodes allows for continuous reachability, always-on connec-tivity, and enables fast wake-up from idle/sleep modes. Theremaining four beacon symbols are dedicated for obtainingwideband CSIT. In the proposed frame structure, the down-link (DL) and UL control channels are located at the center ofthe overall frequency-band; see Fig. 1. This allows for low-power control-channel monitoring during DRX due to thesmall bandwidth (5MHz). A guard period of duration 0.4µsis added to each UL/DL switching point.

B. DISCUSSION ON PHYSICAL LAYER PARAMETERSThe proposed frame structure is based on orthogonalfrequency-division multiplexing (OFDM) waveform.

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OFDM has been used in many wireless communicationstandards, including LTE and WLAN (Wireless Local AreaNetwork) 802.11ac. Moreover, OFDM facilitates the devel-opment of multiuser and cooperative MIMO schemes as wellas radio positioning techniques [13]. In fact, OFDM providesa good comprise in terms of overhead, complexity, latency,and multiantenna schemes when compared to Filter bankmulticarrier (FBMC) waveforms [14].

TABLE 1. Frame structure numerologies.

The numerology of the proposed frame structure isgiven in Table 1 along with a comparison with those ofWLAN 802.11ac and LTE-A. The bandwidth of the OFDMwaveform considered herein is 200MHz. Such an assumptionis similar to alternative frame structure designs [15], [16]. Thesubcarrier spacing of the considered multicarrier waveformis 312.5kHz, thus leading to a symbol duration of 3.2µs(without cyclic-prefix). This is identical to that ofWLAN 802.11ac. Such a subcarrier spacing leads to lowinter-carrier interference (ICI) due to Doppler spread andphase-noise while being smaller than the typical coherencebandwidth found in Line-of-Sight (LoS) urban micro-cellenvironments (approx. 3MHz for 50% correlation) [17].We consider a cyclic-prefix (CP) duration of 0.5µs thusleading to an overhead of 15.6%. Such a short CP followsfrom the typical root-mean-squared (RMS) delay spread ofUrban Micro (UMi) channels (65 − 129ns) [17], as well asshort inter site distances (ISD) (approx. 50m) and low-powertransmissions (0 − 10dBm) of UDNs. In other words, inter-symbol-interference (ISI) can bemitigatedwith a CP durationof 0.5µs as long as the communication range is up to 50m.Mitigation of ISI beyond 50m can be achieved by directionaland low-power transmissions.

The OFDM waveform considered herein is composedof 640 subcarriers thus leading to an Fast Fourier Trans-form (FFT) size of 1024. From the subcarrier spacing(or symbol length) it follows that the sampling frequencyis 320MHz.

III. SCHEDULING OF MOBILE USERSAs discussed in [10], channel aging is a significantproblem in advanced MIMO systems using MultiuserMIMO (MU-MIMO) and CoMP techniques. Hence, whenmaking scheduling decisions in such systems, channel aging

properties should be taken into account by monitoringindividual coherence times and prioritizing users with lowtime since last beacon (TSLB). TSLB denotes the timeelapsed since the last CSI measurement. Typically,MU-MIMO scheduling is efficient only when the schedulingcandidate group is large compared to the amount of accessnode antennas as discussed in [18]. Hence, when servinghighly mobile users there should be enough resources for CSIbeacons to keep the set of scheduling candidates large enoughfor making resource utilization in scheduler more efficient.This means also that it would be beneficial to take schedulingpriorities already into account in CSI beacon scheduling toensure that users prioritized in data scheduling haveup-to-date CSI available and can be thus considered as validscheduling candidates.

A. PACKET SCHEDULING MODELTo take spatial domain scheduling into account and toovercome channel aging problems in advancedMIMO techniques, a new scheduler framework thinking isneeded compared to LTE-A packet scheduling. In [10] oneefficient way of adding spatial and channel coherence timeproperties into packet scheduling model was introduced.It was proposed that packet scheduling could be split intothree phases with each phase handling different schedulingdomains, which are time, frequency and space. This isillustrated in Fig. 2.

FIGURE 2. Proposed scheduling framework for multiuser and cooperativeMIMO scheduling.

First, time domain (TD) packet scheduler chooses a subsetof users from all scheduling candidates that have data intheir buffers. This selection is based on TSLB value i.e., timeelapsed from the last CSI measurement. A threshold value forthe maximum acceptable TSLB can be adjusted dynamicallyfor each user. One way to determine the maximum acceptableTSLB is to use HARQ feedback to observe the time afterwhich the transmissions begin to fail and comparing that to

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the time of last CSI measurement. Thus, TSLB thresholdin TD scheduling captures the effect of coherence time andlimits the scheduling candidate set right in beginning to thosecandidates for which CSI still expected to be accurate. Thisinitial step also reduces computational complexity by reduc-ing the scheduling candidate set, which is important whenper Transmission Time Interval (TTI) scheduling should bedone rapidly for high user densities under the constraints of areal time operating system environment with finite computa-tional resources. Moreover, whenmobile users are scheduled,scheduling efficiency depends upon using CSI measurementswhich are as fresh as possible. Efficiency is monitored bythe throughput measurement (TPM) entity, which gives anestimate of past average throughput for scheduler. Further,the maximum number of selected candidates based on TSLBcan also depend on how many users can be computationallyscheduled within the scheduling time constraint.

After the scheduling candidates are selected for the currentTTI in TD scheduling phase, frequency domain (FD) schedul-ing shall take place. In this phase a certain scheduling prioritymetric is used to select the first user from the scheduling can-didate set. Even though TD scheduling phase drops out users,whose TSLB value is considered to be too big, TSLB shouldbe taken into account when estimating user throughputs forFD scheduling priority metrics. It was observed in [10] thathighest throughputs can be achieved when CSImeasurementsfrom beacons are as fresh as possible. It follows that bothTSLB and estimated rate should be taken into account whenevaluating achievable user throughput in scheduling. Hence,Maximum Throughput (MT) scheduling priority metric foruser n was:

Mn =dn

TSLBn, (1)

where dn denotes the instantaneous non-precoded data ratederived from CSI. This principle of evaluating achievableuser throughputs in the scheduling phase can be used also forderiving e.g. proportional fair (PF) scheduling prioritymetric:

Mn =dn

rnTSLBn, (2)

where rn denotes the past average precoded throughput fornth user. It is worth noting that, in this study, the FD schedul-ing step allocated to user n one resource block occupying thewhole bandwidth although the scheduling can also be doneper subband. Thus, one scheduled resource block consistsof 640× 45 resource elements.

After each user selection in the FD scheduling phase, thespatial domain (SD) scheduling phase checks which userscan still be scheduled for using the same time and frequencyresources. A semi-orthogonality principle [18] was used todo this in our study. However there are also other meth-ods, like the signal-to-interference-plus-leakage-plus-noiseratio (SILNR) based approach presented in [19], which canbe used as well.

To find suboptimal a set of users to be scheduled for eachTTI, FD and SD scheduling phases are looped until there are

no more users fulfilling the spatial orthogonality criteria oruntil the maximum number of users is scheduled accordingto network capabilities. In order to maximize the sum-rateover a certain time-frequency resource, an exhaustive searchover all possible combinations of users could be employed.However, such an approach is computationally expensive,and challenging to implement in practical systems due toshort time-frames.

B. BORDERLESS QUALITY OF EXPERIENCEIN SCHEDULINGThe traditional way of adjusting scheduling fairness is to usedifferent scheduling metrics e.g., PF, for selecting user forcertain time and frequency resource in packet scheduling.Typically, the scheduling fairness is achieved at the expenseof the performance on spectral efficiency as shown in [20].However, in MU-MIMO systems, the spectral efficiencydepends highly on the number of spatially multiplexed users.In this case, achieving fairness with the traditional schedul-ing is not straightforward and more effort on designing agood spatial domain scheduler must be paid to achieve goodfairness and high spatial multiplexing gain as the number ofantenna elements grows. Moreover, in ultra dense networksalmost all users can be reached with good received signalpower due to short propagation distances and high probabilityof being in LoS to the closest access node. However, withoutcoordinating the spatial domain scheduling, high inter-cellinterference can significantly decrease the borderlessexperience.

To make these new services possible with wirelessconnection and to revolutionize user experience for mobileusers, uniform service experience with high data rates andlow latencies throughout the covered area have to be ensured.Hence, 50+ Mbps everywhere target for 5G has been setin [3] to enable comfortable data rate with low latency forevery user allowing high resolution video combined withother digital services. This is aimed to be a consistent tar-get throughout covered area including also cell border areasand highly mobile users. To reach this borderless quality-of-experience (QoE) target, we propose a simple interferencecoordination solution consisting of scheduling coordinationand beamforming coordination. The aim of the schedulingcoordination is to limit the scheduling candidate set for somesubframes in every access node based on the past averageestimated user throughput. By limiting the scheduling of highthroughput users in those subframes, interference levels ofusers in less advantageous channel conditions is reduced andquality of experience can be made more borderless among allusers. Hence, to get some tradeoff between performance andfairness, we propose that every nth subframe is scheduled ina way that every user fulfilling the coherence time criteria isa valid candidate. Other subframes are scheduled by limitingthe scheduling candidate set further with candidates whosepast average throughput is below xth percentile requirementamong the users connected to certain access node. Limitingthe candidate set ensure all users will be scheduled while

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invoking some trade-off between performance and fairness.The algorithm can be tuned by selecting values for n and x.For example, the throughput distribution can be made moreuniform by reducing the candidate set for every subframeand using low x, but reducing the scheduling candidate setwill also decrease usage of the available degrees of freedom.Sharing resources more equally naturally decreases overallperformance. Moreover, coordinated beamforming aims toeliminate poor scheduling decisions i.e., users experiencinghigh interference due to beamforming, as discussed in moredetail in Section IV.

C. ADAPTATION TO EXPERIENCED COHERENCE TIMEThe underlying assumption is that channel aging togetherwith short coherence time increases BLER. Thus, CSI agingthreshold or coherence time limit can be determined usinguser reported Hybrid Automatic Repear Request (HARQ)feedback. The proposed algorithm follows the sameprinciples as the Outer-Loop Link Adaptation (OLLA)algorithm described in [21]. Hence, if ACK is received,maximum acceptable delay Tmax for TSLB can be increasedby Tup, while it is decreased by Tdown if NACK is received.Hence, ratio between Tup and Tdown is used to reach wantedBLERtarget with following expression:

Tup =Tdown

1/BLERtarget − 1. (3)

Therefore, each user will fulfill their individual coherencetime criteria given as:

TSLBn < Tmax,n, (4)

in TD scheduling phase can be selected as a valid schedulingcandidate for FD and SD scheduling phases.

D. CSI BEACON SCHEDULINGAbove it was proposed that TSLBmetric should be taken intoaccount in data scheduling, it also means that scheduling ofCSI beacons should be considered. On one hand, a simpleapproach is to use some random but fair scheduling metriclike round robin (RR). However, when TSLB is used with thescheduling metric and amount of the users is small, then RRcan reduce throughput fairness. On the other hand, to maxi-mize system performance, beacons should be requested fromusers, which can be spatially multiplexed with high Signalto Interference and Noise Ratio (SINR). However, knowingthis before obtaining the channel state information is not astraightforward task for spatial domain scheduler. Even ifhigh SINR ismeasured from twoCSI beacons, it might be thatthose users are not getting as high SINR if they are scheduledon same time and frequency resources due to uncertaintiesin CSI. In this work, location-based beacon scheduling isused to reduce pilot interference. For a group of adjacent CSIbeacons, the first CSI beacon symbol is scheduled based onround robin in such a way that a minimum CSI beacon reuse

distance criterion is fulfilled. The remaining beacons on adja-cent symbols are scheduled for users which are located withinthe CP compensation distance to control the inter-symbolinterference in channel estimation. The location-based coor-dination of pilot interference is discussed in Section IV inmore detail. In particular, the CP compensation distance andCSI beacon reuse distance are illustrated in Fig. 4.

E. LINK ADAPTATIONSince dynamic scheduling can be done for eachTTI separately, the power (interference) conditions changesfor each TTI in multi-user MIMO systems using beam-forming. Hence, interference levels experienced by users arechanging every TTI. For this reason, well known solutions foradapting to experienced interference levels like OLLA [24],are not working well any more. The success of preciselyestimating SINR per TTI and selecting adequate modulationand coding scheme for each scheduled user will dependmore on cooperation of neighboring access nodes. Sinceour UDN concept relies on uplink beaconing, it is assumedthat several nearby access nodes can measure the channelfor users transmitting beacons. Therefore, link adaptationshould be done jointly with the closest neighboring accessnodes in a way that inter-cell interference caused by theclosest neighbors can be estimated precisely. This can bedone either by utilizing a centralized scheduler which aggre-gates channel measurements or by utilizing a joint schedulerapproach where cooperating access nodes exchange channelmeasurements with their closest neighbors.

IV. INTERFERENCE COORDINATION BASED ONACCESS NODE COOPERATIONInterference coordination is an effective way of mitigat-ing inter-cell interference at the cell edges and thereforean obvious approach for achieving ubiquitous user experi-ence in 5G [22]. Interference coordination techniques can beclassified as semi-static or dynamic coordination techniquesbased on the information sharing across the cells [5].

The semi-static coordination techniques such as fractionalfrequency reuse, soft frequency reuse and enhanced inter-cell Interference Coordination (eICIC) schemes, exchangetime-sensitive information between the cells for coordinat-ing the scheduling decisions and can typically tolerate longdelays [5]. Exchanged information can be e.g., a hypothesiswith a benefit such as a map of resource blocks or subframesthat are suggested to be muted by other neighboring accessnodes and the benefit for the sending access nodes. Suchsemi-static coordinated scheduling can provide benefits ininterference managing with relative low backhaul overheadand complexity. However, the associated scheduling restric-tions results reduce the efficiency of resource utilization anddo not work well in dynamic scheduling/beamformingscenarios.

Dynamic coordination techniques such as coordinatedmultipoint transmission and reception of signals i.e., CoMPor Network MIMO (sometimes as Distributed MIMO), can

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bring significant gains in spectral efficiency and more uni-form user experience [7] but require more backhaul capacityand dedicated methods for channel state reporting, inter-ference measurements, and design of reference and controlsignaling [5].

A. COORDINATED MULTI-POINT TRANSMISSIONIn 3GPP framework, CoMP schemes are categorized asdynamic point selection (DPS), dynamic point blank-ing (DPB), joint transmission (CoMP-JT), and coordinatedscheduling/beamforming (CS/CB) [23]. In DPS, the datatransmission occurs from a single access node but thescheduling assignment is dynamic and the node transmittingdata to a particular user may be changed each subframeeven if the channel remains stationary. Such fast switchinggives ‘virtually’ simultaneous connection to multiple cellswithin a coordination area comprising multiple access nodes.In DPB, dominant interferers within the coordination areaare identified and muted on a per-subframe basis for lower-ing the interference. In CoMP-JT, two or more transmissionpoints are transmitting signal to single users using the sametime-frequency resources. CoMP-JT can be based either oncoherent or non-coherent transmission of signals but only thecoherent CoMP-JT can offer the best performance amongall CoMP schemes. The drawbacks of CoMP-JT include therequirement for tight synchronization, as well as the low-latency and high-bandwidth backhaul needs for enablingcoherent multipoint transmission. These requirements makeCoMP-JT less practical than DSP, DPB or CS/CB schemesdespite the increase in the area throughput.

In CS/CB, scheduling and beamforming decisions betweendifferent transmission points are aligned to reduce inter-ference. By exchanging information of scheduled UEs andtheir beamforming weights (or beam identifiers), schedulerscan help to lower strong interference at the cell edge withappropriate beamforming. Such techniques have been studiedearlier in several papers e.g., in [6], [23], and [24]. In par-ticular, the effect of velocity with coordinated beamformingwas studied in [23] which showed performance gain in themicro cell simulation scenario with moderate inter-site dis-tances and low user velocities. However, when the aim is tosupport higher velocities, coordinated beamforming becomesmore challenging since it is sensitive to uncertainties due toCSI aging. Thus, coordinated beamforming would requireexhaustive acquisition and exchange of channel state infor-mation in UDN before the measurements get outdated.

In this work, the interference coordination between theaccess nodes is based on CS/CB approach with two prin-ciples. Firstly, the central scheduler eliminates the poorscheduling decisions made by selfish access nodes. Thiselimination is done in link adaptation phase by estimatingthe precoded interference levels before selecting a modula-tion and coding scheme. Secondly, the location-aware pilotscheduler is used to mitigate pilot contamination. The pilotcontamination is mitigated by adjusting pilot re-use distancefor pilot scheduling as discussed in following sections.

B. BEAMFORMING COORDINATIONIn our earlier work [10], a joint multipoint CS/CB approachknown as CoMP-Coordinated Beamforming (CoMP-CB)was found to be beneficial in ultra-dense small cell networkswith low user mobility. However, when the user mobility wasincreased, the benefit of the CoMP-CB approach diminisheddue to CSI uncertainties. Thus, in this work, the focus is onCS/CB scheme, where central scheduler aims at transmittingdata to a user from a single access node in a manner that theinterference caused by the neighboring nodes is either miti-gated or predicted. In particular, the interference mitigationis achieved by using larger antenna aperture i.e., narrowerbeams with higher array gains, and eliminating poor schedul-ing decisions by predicting the precoded interference.

FIGURE 3. Illustration of the proposed coordinated schedul-ing/beamforming scheme where uplink beacons from user ui arereceived by several ANds. The central scheduler aggregates the channelstate information from a coordination area composed of a set of ANds inorder to predict the interference due to precoders in downlink.

To estimate the precoded interference, the central sched-uler has to aggregate uplink channel state information fromseveral access nodes (ANd) and utilize that informationto estimate the precoded interference. This is illustratedin Fig. 3. In this work, such coordination does not targetjoint beamforming or complete interference cancelling fromneighboring access nodes but the aim is to eliminate poorscheduling decisions during the link adaptation phase. Sincethe initial selection of spatially multiplexed users is doneper-access node basis, poor scheduling decisions may resultin high inter-cell interference at the cell edges withoutcoordination.

The downlink interference prediction for coordinatedbeamforming is modeled in the following way. First, a setof users is selected to be scheduled by each access node.After each access node has completed the users selectionand precoder calculation, the central scheduler determines theinstantaneous per-subcarrier precoded signal to interferenceand noise ratio SINRi,j for the ith scheduled user ui fori ∈ {1, . . . ,K } scheduled in jth ANdj for j ∈ {1, . . . ,N }according to:

SINRi,j =‖wHi,jh̃i,j‖

2∑Nn=1

∑Kk=1 k 6=1 ‖w

Hk,nh̃i,n‖

2 + σ 2n

, (5)

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where {·}H denotes the Hermitian-transpose operator. Thecomplex-valued vector wi,j ∈ CLj×1 denotes the precoder foruser i determined by access node j e.g., using zero forcingor matched filter criterion. Note that the jth access-node iscomposed of Lj antennas. Vector h̃i,j ∈ CLj×1 is the estimatedeffective channel and σ 2

n is the noise-variance. In this work,the received uplink channel consists of an ideal channelhi,j ∈ CLj×1 and complex-valued error term εi,j defined as:

h̃i,j = hi,j + εi,j, (6)

where the error term εi,j ∈ CLj×1 contains noise and inter-ference e.g., sum of simultaneous transmissions of uplinkbeacon signals from different users reusing the same beaconresources. To obtain h̃i,j, the channel is estimated using least-squares estimator (LSE) for hi,j e.g., as discussed in [25].Finally, the central scheduler eliminates users which SINRi,jdoes not fulfill minimum predicted SINR threshold. Theminimum required SINR threshold is chosen according tominimum MCS requirement being approximately −6 dB.If a certain user is chosen by more than one ANd, then thesignal is transmitted only from ANd j that maximizes theSINRi,j metric. In case the central scheduler eliminates alltransmissions from a particular ANd due to low estimatedSINR, no user data is transmitted on that ANd in downlink.This results in almost blanked subframes.

C. LOCATION BASED COORDINATION OFPILOT CONTAMINATIONOne drawback of advanced MIMO systems is that theyrequire a large fraction of radio frame resources to be allo-cated for pilot resources to achieve high spectral efficiency.In case of dense reuse of pilots, the same pilot signals fromseveral users are combined at the access node receiver. Thisresults in pilot contamination where CSI is mixed and partlycorrelated for several users. When the access node definesbeamforming vectors, a fraction of the beam energy is steeredtowards the correlated user causing interference. In large cellnetworks with high user density, pilot resources must bereused frequently to achieve sufficient pilot per populationratio that guarantees high MU-MIMO gain and good spectralefficiency. Since, coherence block length is shorter for mobileusers, pilot per user ratio has to be higher or otherwise unre-liable CSI decreases system performance. Thus, it becomesvery challenging to utilize massive MIMO efficiently formobile user in large cells. Simulation results e.g., in [26], sug-gest that in the worst case inter-cell interference conditions,pilot reuse ratio 7 is optimal for small number of antennaelements. As the number of antenna elements grows, theoptimal reuse ratio is decreased and pilot reuse of 3 is foundto be a decent choice in most of the cases.

In ultra dense networks, pilot resources can be reused moreoften in spatial domain than in micro cell networks due tosmaller inter-site distances and lower transmission powers.This increases the density of available CSI beacon resourcesper area (increasing pilot per population ratio). Additionally,with a dedicated frame structure design, it is easier to provide

more CSI beacon resources in time domain due to possibilityof using shorter symbols with reasonable CP-plus-GP over-head in ultra dense networks.

When designing CSI beacon reuse pattern for a shortCP system, it is beneficial to take the short CP duration intoaccount to avoid pilot contamination. One design goal is toensure that distance between ANds and users transmittingbeacons is within the range of CP to avoid inter-symbolinterference from adjacent beacons in case the propagationdelays become longer than the short CP. Moreover, it isalso beneficial to separate the users to have some diversitybetween the channels for maximizing the spatial domaingain. Hence, we utilize a location-based scheduling scheme,where a group of users is scheduled in a way that the userstransmitting beacons on adjacent symbols are geographicallyseparated but close to each other. This is controlled withCP compensation distance metric. Such grouping helps tomitigate ISI on adjacent beacons and ensures efficient reuseof beacon resources by another group of users. Moreover, thesame beacon resources are reused by another group of usersafter some safety distance to also mitigate interference fromcolliding beacon resource as illustrated in Fig. 4.

FIGURE 4. Illustration of the location-based pilot contamination schemewith CP compensation distance and CSI reuse distance. CP compensationdistance is used to control how far users transmitting on adjacent beaconsymbols can be whereas CSI reuse distance restricts the minimum distanceof scheduling users to use same beacon symbols.

V. PRECODER DESIGNWe focus on linear precoding rather than nonlinear precodingschemes. In particular, both the zero-forcing (ZF) and thematched-filter (MF) precoders are considered in this paper.This follows from the number of antenna elements on eachaccess-node comprising the UDN that is evaluated herein.In fact, the performance difference between the linear andnonlinear precoding schemes vanishes when the number ofantenna elements L grows with respect to the number ofscheduled users K [27]. Linear precoding schemes also pro-vide a good compromise between performance and complex-ity in practice. Expressions for the ZF and MF precoders canbe found from the estimated channel vector as follows [27]:

wMF = H̃Hj , wZF = H̃

†j , (7)

where {·}† denotes the Moore-Penrose pseudoinverse.Moreover, matrix H̃ j ∈ CLj×K contains the esti-mated channel-vectors of the K scheduled users at theaccess-node j as follows:

H̃ j =[h̃1,j, . . . , h̃K ,j.

](8)

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The MF is known to be the optimal precoder when thearrays at the access-nodes are assumed to have infinitelymany antenna elements [27]. For practical antenna arrays theZF precoder typically outperforms the MF in terms of SINRgiven that the CSI is accurate. However, MF precoder outper-forms ZF when the CSIT is acquired in the low SNR regime;see Fig. 5. Moreover, MF is more robust to CSI aging thanZF precoder. This is illustrated in Fig. 5 where the CSI relia-bility is modeled by the parameter ζ . The results in Fig. 5 areaccording to the expressions given in [27, Table 1]. Similarresults can be found in [10], where the sensitivity ofZF precoder (including its regularized version) to CSI aginghas been analyzed for high mobility users. MF precoderis also computationally more attractive than ZF. In partic-ular, the complexity of MF is O(LK ) while that of ZFis O(LK 2). Hence, MF precoder provides a good compro-mise between performance and complexity in high mobilityscenarios.

FIGURE 5. Performance of MF and ZF precoders in terms of SINR as afunction of the SNR used for channel estimation. The CSI aging is modeledby parameter ζ (ζ = 1 means that the CSI is up-to-date). The resultsillustrated in this figure are based on [27], and assume that the numberof antenna elements at the access-node L and the number of scheduledusers K approaches infinity. It is also assumed that L/K = 2. MF is morerobust to channel aging than ZF precoder.

We also consider an approximation of the channel matrixused by both MF and ZF precoders that is useful for UDNsand high mobility users. In particular, the matrix H̃ j usedby the linear precoders in (7) is given by an estimate of theLoS path between scheduled users and access-nodes. Moreprecisely, matrix H̃ j is given by:

H̃ j =[a(ϑ̂1, ϕ̂1), a(ϑ̂2, ϕ̂2), . . . , a(ϑ̂K , ϕ̂K )

], (9)

where a(ϑ̂k , ϕ̂k ) ∈ CLj×1 denotes the Lj-elementmultiantenna output due to the estimated LoS path of thekth user. In particular, ϑ̂k ∈ [0, π] and ϕ̂k ∈ [0, 2π )denote the estimated elevation and azimuth arrival anglesof the LoS path of the kth user, respectively. Such anglesmay be found using the method in [13] and follow from theposition of the scheduled users with respect to the access-node given that they are in a LoS condition. A statistical

test on the Ricean K-factor may be employed in order todetermine whether a user-node is on a LoS condition, forexample as in [28]. In the remainder of this paper, precodersthat use the approximation in (9) are called position basedprecoders. Note that channel estimation errors are taken intoaccount by introducing an error in the angles; see Section VII.In particular, the numerical results in Section VII show thatposition based precoders are more robust to channel agingthan conventional (channel based) precoders, particularly inhigh mobility scenarios. This may be understood by notingthat the rate of change of the angles (ϑ, ϕ) is typically smallerthan the phases of all the propagation paths comprising theradio channel.

VI. DETAILS OF THE DEVELOPED SIMULATORThis section provides details of the simulator employed toevaluate the performance of the algorithms and methodsproposed in sections III-V. Numerical results are givenin Section VII. The developed simulator is based on the radiointerface for 5G UDNs proposed in Section II.

A. SCHEDULING, LINK ADAPTATION, ANDCHANNEL ESTIMATIONThe access nodes perform the scheduling algorithm proposedin Section III independently. A BLER target of 20% has beenused for coherence time adaptation (CTA). A joint solutionfor the link adaptation has been considered where the closestneighboring access nodes are taken into account in determin-ing the interference experienced by the user nodes. The CSIThas been acquired from the UL wideband beacons describedin Section II using a least-squares estimator. Such widebandbeacons consist of QPSK symbols taken from a pseudo-random Gold sequence of length 255 [29, Ch. 8]. Theestimated UL wideband channel has been used for schedul-ing, precoding and link adaptation. Frequency and codedomain scheduling have not been considered.

Modulation and coding scheme (MCS) selection consistedof joint inner-loop link adaptation, and was performed afterthe scheduling decisions. Link adaptation was based onestimated SINR as in (5) where interference has been calcu-lated and shared among the closest access nodes. The MCStable considered herein is based on LTE-A values. Note thata 256-QAM has been used for the highest SINR values.

B. DEPLOYMENT SCENARIOA deployment scenario based on METIS TC6 has beenused [30]. Fig. 6 illustrates the deployment scenario con-sidered herein. In particular, it consists of a 500 meter longhighway with 6 lanes. The width of each lane is 3m. TheISD of the UDN is 50m. The access nodes are located at thecenter section of the highway; see Fig. 6. The users have beendropped randomly on the simulated lanes at each realization.A user density identical to that in METIS TC6 has beenconsidered [31]. Table 2 provides additional details on theparameters used by the developed simulator.

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FIGURE 6. Illustration of the deployment scenario considered in thedeveloped simulator. Both the deployment scenario and the user densityare according to the METIS TC6. The access nodes comprising the UDN arelocated at the center of the highway, and the ISD is 50m.

TABLE 2. Simulation parameters.

C. GEOMETRY BASED STOCHASTIC CHANNEL MODELThe geometry based stochastic channel model (GSCM)described in METIS has been used [17], [32]. The focus hasbeen on the 3-D UMi propagation scenario. In particular, themaximum number of clusters in the 3-D UMi scenario is12 for LoS and 19 for a NLoS condition [32]. The LoS prob-ability of every access node - user node link is a function ofdistance, and follows that in [17]. Moreover, the clusters thatare at least 25dB weaker than the cluster with largest powerare discarded. Each cluster is composed of 20 propagationpaths. Each propagation path is characterized by a power,

delay, elevation angle, azimuth angle, complex-path weightsand a cross-polarization ratio. Details regarding the calcu-lation of the aforementioned parameters of the propagationpaths as well as the corresponding probability distributionsfor the large-scale parameters may be found in [17] and [32].

The LoS propagation path is also included in case of a LoScondition. The Ricean K-factor is used to weight the LoSpropagation path as well as the remaining clusters accord-ingly. The parameters of the LoS propagation path, namelythe elevation angle, azimuth angle, delay and path-weights,follow from the distance between the access node and usernode as well as their relative location. A distance-dependentcorrelation of the large-scale parameters has been used [32].It accounts for the spatial correlation among different user-nodes as well as among the access-nodes. Moreover, a plane-wave assumption has been used on our GSCM. It is importantto note that the parameters of the LoS propagation path onour GSCM vary according to the movement of the user-node. The double-directional parameters of the multipath-components are fixed during the simulation time except fortheir relative phases which vary according to the correspond-ing Doppler components. In other words, we have considereda true motion model for the LoS path but a virtual motionmodel for the multipath components [17], [32].

FIGURE 7. Effect of antenna array and precoder selection to thethroughput performance in ultra-dense deployment for different uservelocities. As the velocity increases, MF precoder becomes better thanZF precoding strategy.

VII. NUMERICAL RESULTSThis section presents the numerical results to justify thedesign of the proposed coordinated beamforming schemeto support mobility in UDN. First, different antenna arraysand precoders are compared following with more detailedanalysis of other system key performance metrics. Fig. 7shows area throughput comparison for Uniform CircularArray (UCA), Uniform Planar Array (UPA) and UniformLinear Array (ULA) in UDN scenario. It can be seen thatMF tolerates better the channel estimation error due to themobility. Performances of circular and planar array geome-tries are rather similar although their geometries are quite

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different. Antenna aperture size of the circular array is largerin horizontal domain compared with the planar array but thecircular array cannot do beamforming in elevation domain asefficiently as the planar array. Therefore the planar array canget gain from elevation beamforming, but due to wider beamsin horizontal domain more interference is generated. As itwas also shown in [33], horizontal linear antenna arrangementappears to be best suited for massive MIMO operation interms of pure performance. However, the physical size ofthe horizontal linear array may not be so practical for UDNdeployment, where access node size should be so small that itcan be affixed to existing infrastructure like lamp posts. Thephysical sizes of the different antenna array geometries arecompared in Table 3 for used 3.5GHz frequency withλ/2-separation of the antenna elements. Circular array waschosen for UDN deployment due to its good performancewith MF precoding in high velocities and due to rather com-pact physical size of the antenna array. For 2-sector micro cellcase planar arrays were chosen due to their relatively compactphysical size when compared to linear or circular arrays.Hence, it could be assumed that physical sizes of chosen arraygeometries for 2-sector micro cell and UDN deployment canbe installed to the existing street lighting posts facing thedirection of the highway. Further, with the chosen arrays thetotal available degrees of stays comparable betweenUDN andMicro cell deployments, since in UDN there are 250 elementsand in 2-sector micro cell case there are 288 elements in thesimulation area.

TABLE 3. Size of different antenna array geometries. Center frequency of3.5GHz and λ/2 inter-element spacing.

A scheduler which tries to maximize throughput for eachTTI cannot guarantee similar user throughput and QoE for allusers. This is shown by the results in Fig. 8. If scheduling ismade to ensure more borderless quality of experience throughthe simulation area, then scheduling fairness and more uni-formQoE can be obtained. Borderless QoE scheduling in thispaper is done as described in Section III by selecting valuesx and n based on our earlier experience. Hence, schedulingof every third subframe is using only MT metric for usersfulfilling coherence time criteria. During other subframes,only the users who fulfill coherence time criteria and whosepast average estimated throughput is below 30th percentileare considered as scheduling candidates. Moreover, Border-less/MF Borderless/ZF refer to scheduling strategies whereeither MF or ZF precoding is used with Borderless scheduler.Similarly, MT/MF and MT/ZF refer to scheduling strategieswhere MT scheduling is used instead of Borderless schedul-ing. In Fig. 8 it can be seen that Borderless/MF precoding

FIGURE 8. User throughput CDF for 10km/h velocity.

produces more uniform QoE i.e., user throughput. This is dueto the reduced sensitiveness to channel measurement errors.

FIGURE 9. User throughput CDF for 50km/h velocity.

Figure 9 shows similar user throughput curves for 50km/huser mobility. When user velocity is increased, the networkis adapting to 20% BLER target adjusting TSLB and thusnot being able to serve as many users simultaneously due tomore rapid channel aging than in case of 3km/h. In partic-ular, user throughputs of MT/ZF and Borderless/ZF sched-ulers suffer from higher user mobility. A comparison withFig. 8 shows that the median user throughput decreases from90Mbps to near 10Mbps for MT/ZF and to near 30Mbpsfor Borderless/ZF while 50th percentile user throughout ofBorderless/MF does not change significantly. Moreover, theBorderless/MF provides ∼ 77% higher gain compared withthe second best MT/MF scheme.

A. MOBILITY TOLERANCEFigure 10 shows system area throughput for different pre-coder/scheduler strategies in case of different user velocities.For low user velocities from 3km/h to 10km/h, ZF precod-ing strategy is better but when the velocity increases the

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FIGURE 10. User velocity impact to proposed schedulers and precoding.

area throughout of MF precoding strategy is better. The areathroughput of Borderless/MF strategy is 43% to 17% lowercompared with the best achieved area throughput in differentvelocity cases but it provides better and fairer user throughputand QoE as shown in figs. 8 and 9. If Borderless/MF resultsin Fig. 10 are compared to CoMP-JP UDN results in [10],it can be observed that circular antenna array of 25 elementsresults in 37% to 110% better area throughput than the coor-dinated beamforming with smaller antennas in case of higheruser velocity than 10km/h. Coordinated beamforming withZF precoding starts to degrade when velocity is more than10km/h due to poorer tolerance against CSI uncertainties andchannel aging compared with Borderless/MF.

FIGURE 11. Number of simultaneously scheduled users on average peraccess node for each TTI with different user velocities.

The mean number of the scheduled users per access nodeis shown in Fig. 11. It can be seen that the degrees of free-dom (ratio between scheduled users and antenna elementsper access node) decrease more rapidly with ZF precoderthan with MF precoder when the velocity is increased. Thishappens because BLER is rising more rapidly for ZF due to

increased errors in channel estimation. This causes the CTAalgorithm to decrease the maximum tolerated coherence timelimits for each user to keep BLER around 20%. This in turndecreases the number of users, which can be considered asvalid scheduling candidates making it more difficult to findusers that can be served simultaneously.

FIGURE 12. SINR CDF for 10km/h velocity.

Even though beamforming with MF precoding can toleratevelocity better and thus it is able to send data to more userssimultaneously, ZF cancels inter-beam interference moreeffectively. This can be interpreted from Fig. 12 showingapproximately 8dB SINR difference between 50th percentileSINR for MF and ZF precoders when 10km/h user velocityis applied. Moreover, Borderless/MF and Borderless/ZF pre-coding strategies results in 1 to 2dB better SINR performancethan regular MF and ZF precoding strategies.

FIGURE 13. Effect of channel aging and CSI beacon reusing.

B. REUSING OF CSI BEACON RESOURCESIn larger networks, reusing CSI beacon resources isinevitable. Hence, the effect of reusing CSI beacon resourceswithin simulation area is studied. In Fig. 13, the effect of

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channel aging and CSI beacon reusing is illustrated.As discussed in Section IV, reusing beacon resources resultsin additional CSI error due to the beacon contamination but,if rough user location is used in beacon scheduling, this errorcan be controlled. In this study, the beacon resources werereused using 200m minimum reuse distance and 25m CPcompensation distance. As shown in Fig. 13, increasing inter-ference with CSI reusing does not significantly decreaseMF precoding performance. However, performance ofZF precoding decreases when velocity is 50km/h due to theadditional CSI error. Moreover, when velocity is increasedto 100km/h, additional CSI error due to CSI beacon reusingis not as severe as the CSI error due to channel aging. Fromthis finding it is concluded that CSI beacon reuse distancecan be adjusted depending on the channel aging. In otherwords, if the channel is aging rapidly, then the reuse distancecan be short. In contrary, for low user velocities e.g., 3km/h,it is worthwhile to try to use ZF precoding with longer CSIreuse distance due to the fact that CSI contamination is amoresignificant factor in channel estimation error.

FIGURE 14. UDN area throughput performance compared againsttwo sector micro-cell base stations.

C. ULTRA-DENSE NETWORK VS MICRO-CELL NETWORKIn Fig. 14, UDN area throughput performance is comparedwith two sector micro cell access node configuration. It canbe seen that UDN outperforms micro-cell deployment inthe ability of providing area capacity by factor of 1.5× incase of 3km/h user velocity and by factor of 2.8× in caseof 50km/h user velocity. In both cases, users are movingon a rather narrow street canyon of 24 meters as depictedin Fig. 6. The main differences between the UDN and micro-cell scenario are antenna configuration, site placement andlonger distance to serving cell which affects line-of-sightprobabilities and channel characteristics. Site placement andstreet geometry limits average azimuth and elevation separa-tion of users in line of sight conditions. In UDN scenario,the maximum azimuth angle separation at the cell center(distance of 12.5meters) is 87.6 degreeswhereas inmicro cell

scenario it is only 21.7 degrees at the cell center (distanceof 62.5 meters). Similarly, the maximum elevation angleseparation between the cell edge and the center is 10 degreesin UDN case and only 2.3 degrees in the micro-cell case.

In micro cell scenario, 6 × 6 dual polarized antennaarray can provide some beamforming gain in horizontal andvertical domain. However, the gain in elevation domain isexpected to be rather small due to very narrow elevation anglespread. Some horizontal beamforming gain can be achievedwith the planar array due to the wider azimuth spread butthe gain is smaller compared to the gain of circular array of25 elements which can form narrower beams. Thus, benefitsmore from the wider azimuth spread in UDN scenario. There-fore, it is more difficult to achieve sufficient beamformingperformance with the planar array in micro cell configurationcompared with the circular antenna configuration in UDNcase. Moreover, since the mean distance to serving accessnode is increased in micro-cell case, the probability of havingsignificant gain from dominant line of sight signal is lost formany users.

Precoder behavior in micro cell configuration is similarto UDN configuration. Significant gain can be achieved byusing ZF precoding in low velocities. However, uncertaintiesin CSI spoils inter-beam interference cancelling advantagesof zero forcing in case of higher velocities. MF on the otherhand cannot form enough beams to narrow sector in a waythat micro-cells could compete against the performance ofultra-dense deployment of circular antennas.

D. POSITION BASED PRECODINGFig. 16 compares the performance of channel estimationbased precoding and user position based precoding discussedin Section V. In case of channel estimation based precoding,there is error coming from channel aging. In case of positionbased precoding, static beam angle error is used to modelthe error instead of channel aging. In [13], joint positioningsolution utilizing CSI beaconing for position estimation wasproposed for ultra-dense networks. It was claimed that withthe said solution 0.4m mean user positioning error can beachieved. In Fig. 15 it is shown how beam angle error maps toposition errors in the studied UDN simulation scenario takingalso antenna heights into account.

As can be seen in Fig. 16, with 3 degree error in thebeam angle, satisfactory channel estimation based precodingperformance can be achieved. If the mean positioning error inthe whole simulation area would be 0.4m, then angle errorswould be even lower. This would improve the performanceof position-based beamforming due to more precise beamorientation, since the user is notmoving significantly betweenfew millisecond CSI beacon intervals (a few ms). Thus, theposition estimation error from CSI beacons is dominant overthe position aging caused by mobility.

It was also seen that on average 59%more users per accessnode were served per TTI with position based precodingwhen compared to the channel estimation based precodingin the case of 50km/h user velocity case.

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FIGURE 15. Used static beam angle error values mapped to positioningerror and distance between user and access node.

FIGURE 16. Area throughput performance while serving users moving50km/h. Position based precoding with beamforming angle error is com-pared against channel measurement based precoding.

Further, it was seen that mean user SINRs were lowerwith position based precoding when compared to channelestimation based precoding. On the other hand, on average59% more users per access node were server per TTI withposition based precoding when compared to channel estima-tion base precoding in utilized 50km/h user velocity case.This is possible since, when only line of sight path is usedwith the precoder, less interference is leaked towards otherscheduled users in rich scattering urban environment. Thesefacts help to create more uniform throughput distributionand thus help to provide more desktop-like low latency userexperience to all users as can be seen in Fig. 17. Furtherit can be seen that with borderless scheduling and positionbased ZF precoding 94% of users can achieve the 50+Mbpseverywhere target as defined for 5G in [3].

Even though the performance of position-based precodingin ultra-dense network looks promising, future studies shouldtake into account its limitations in NLoS. In particular, theusers which do not have LoS connection cannot be served by

FIGURE 17. User throughput CDF for 50km/h with 3 degree beam angleerror position based precoding.

using only position information. In spite of this limitation,the LoS probability in ultra-dense networks is very high [32],which emphasizes developing accurate user positioning andusing the position information for beamforming even further.

VIII. CONCLUSIONS AND FUTURE WORKNovel techniques for handling mobile users in multiantennaultra-dense networks have been proposed. The proposedspatial-domain scheduler adapts to the users’ mobility andcoordinates its decisions within a coordination area com-posed of several access-nodes. A novel metric called time-since-last-beacon is proposed in order to take the aging ofthe users’ CSI into account. Extensive numerical results showthat the proposed scheduler achieves ∼ 77% higher medianuser-throughput than that of maximum-throughput schedulerat a cost of ∼ 17% lower area-throughput. Hence, the pro-posed scheduler can provide a significantly better borderlessexperience in 5G outdoor ultra-dense networks with minimalsystem-performance degradation. Such results remain valideven for a high-density of mobile users with velocitiesranging from 3km/h to 100km/h.

An extensive numerical study on the impact of CSI aging tomatched-filter and zero-forcing precoding schemes has beencarried out for various antenna array configurations. Resultsshow that the circular antenna array provides a good compro-mise between throughput, robustness to channel aging, andarray size, given that the proposed scheduler is employed.In particular, a combination of the proposed borderless sched-uler with matched-filter precoder was able to tolerate highuser velocities up to 100km/h as well as provide robustnessto pilot contamination due to beacon reuse. Moreover, theproposed strategy performed better on a UDN deploymentthan on a micro-cell deployment using substantially largerplanar antenna arrays.

Finally, location-based ZF precoding scheme has beenfound to perform surprisingly better than that of MF pre-coding based on measured CSI. Such findings motivate us to

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study the feasibility of location based precoding rather thanthat based on measured CSI in our future work on UDNs.

In this study, realistic traffic models were not utilized.Hence, it would be interesting to take that aspect into accountdue to 5G’s below 1ms RTT latency target. Additionally,in future work, multi-antenna receivers shall be considered,since it is easy to envision vehicles with antenna arraysinstead of a single omni-directional antenna. Even thoughmultiple antennas would increase user node complexity andpower consumption, it would make it possible for user nodesto do the interference cancellation and receive multi-streamtransmissions. Hence, usage of degrees of freedom could beincreased even further by having better interference tolerancein user nodes.

ACKNOWLEDGMENTThe authors wish to acknowledge Mr. Mark Hawryluck,Mr. Pauli Seppinen, and Dr. Kari Leppänen for helpfulcomments on the paper.

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PETTERI KELA received the M.Sc. degree fromthe University of Jyväskylä, Finland, in 2007.From 2001 to 2007, he was with the Departmentof Mathematical Information Technology, Univer-sity of Jyväskylä. He joined Nokia, as a ProtocolDesign Engineer, where he has been involvedin LTE UE modem software development. From2010 to 2013, he was a Senior Researcher andTechnical Lead with Renesas Mobile Corporation,working on LTE-A system research, and

L1/L2 modem software architecture and implementation. Since 2013, he hasbeen with Huawei Technologies Oy (Finland) Company, Ltd., as a SeniorResearcher. His research interests include 5G, L1/L2 protocols, ultra-densenetworks, and multiuser MIMO techniques.

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JUSSI TURKKA received the M.Sc. andD.Sc.(Tech.) degrees from the Tampere Univer-sity of Technology, Finland, in 2008 and 2014,respectively. He has been with Magister SolutionsLtd., as a Senior Research Consultant in severalprojects, since 2009. He has authored over15 scientific publications, contributing to3GPP LTE specifications and filed several patents.His areas of expertise are in the field of self-organizing cellular networks, mobility robustness

optimization, minimization of drive tests, and knowledge mining.

MÁRIO COSTA (S’08–M’13) was born inPortugal in 1984. He received the M.Sc. (Hons.)degree in communications engineering from theUniversidade do Minho, Portugal, in 2008, andthe D.Sc.(Tech.) degree in electrical engineer-ing from Aalto University, Finland, in 2013.From 2007 to 2014, he was with the Department ofSignal Processing andAcoustics, AaltoUniversity.In 2011, he was an External Researcher with Con-nectivity Solutions Team, Nokia Research Center.

Since 2014, he has been with Huawei Technologies Oy (Finland) Company,Ltd., as a Senior Researcher. His research interests include sensor array andstatistical signal processing, and wireless communications.

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Borderless Mobility in 5G Outdoor Ultra-Dense Networks · Corresponding author: P. Kela ([email protected]) ABSTRACT This paper considers borderless 5G ultra-dense networks