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Mini-Base Station: A Novel Smart VirtualeNB for 5G and Beyond Mobile NetworksFOUAD A. YASEEN1, HAMED S. AL-RAWESHIDY.1, ( Senior Member, IEEE)1Wireless Networks and Communications Centre (WNCC), Department of Electronic and Computer Engineering, College of Engineering, Design and PhysicalSciences, Brunel University London, London, UB83PH, U.K.

Corresponding author: Fouad A. Yaseen (e-mail: Fouad.Yaseen@ brunel.ac.uk).

ABSTRACT This paper presents a proposal for an innovative base station for 5G and beyond for a mobilenetwork system based on the Evolved Packet System (EPS). Functions of EPS elements can be achieved bythe SVeNB, according to the type of function or service and the device that is used to achieve that functionor service. The current system, even if these functions and services have been virtualized by the system, itmay put them away from the users. Consequently, this causes a delay in completing the establishment ofa connection. Since these functions and services can be virtualized and programmed in Virtual Machines(VMs), we have been simulated the kernel of those functions and services executed by the SVeNB throughits VMs. Moreover, The SVeNB position is located close to the users, which helps to reduce the End-to-End delay. Our proposed approach gives the SVeNB the ability to share and contribute in making decisionswith the Core Network (CN) and BaseBand Unit (BBU) pool to initiate a connection, further in makingdeterminations within its vision regarding its users. This procedure leads to a reduction in the load on theBBU pool and the CN, thus lessening the latency, and to increasing the data rate that is delivered to the users.That means the SVeNB can regionally decide for all local users that are covered by SVeNB to connect toeach other or the Internet. The MATLAB platform has been used to evaluate the SVeNB performance, withthe results demonstrating that it surpasses. The obtained results point that the SVeNB passes the C-RANsystem by 68% in terms of user profiles processing, whilst also and it reducing the End-to-End delay by62%.

INDEX TERMS 5G, Control Plane, Communication Networks, SDN, NFV.

I. INTRODUCTION

5G communication network systems are geared towardsminimizing the size, cost, power consumption, and de-

ployment of physical hardware. In addition, it will increasethe transmission data rate, speeds up of the delivery of data,increases performance, and virtualizes physical resources ofthe support of the Software Defined Networking (SDN) andNetwork Functions Virtualization (NFV). The most signifi-cant challenges for 5G mobile communication networks areproviding sufficient capacity to accommodate the growth ofmobile data traffic and decreasing End-to-End latency. Thetechnologies of movable smart gadgets have been advancing.At the same time, the numbers of their users have beenexpanding, and intuitively, will lead to exceeding of thecapabilities of mobile communication networks. In order toaddress such obstacles, these networks should be efficientto satisfy expandable needs of mobile networks of effec-tive performance. To realize these goals, mobile networks

should be able to handle this expansion without increasingthe cost of these networks [1], [2]. Most of the currentor proposed mobile communication networks working un-der 4G conditions have implemented virtualization in bothhardware and software infrastructure. The theory of SDNdepends on splitting the Control Plane (CP) from the DataPlane (DP) of network devices [3]. This separation is tosimplify the control and management of networks. It is alsoto facilitate progression and development by abstracting thenetwork control functions into a virtually centralized CP thatallows for the network management to be programmable bysoftware developers in a smooth and easy way [4]. Makingdecisions is based on software controllers that are logicallycentralized away from the network devices that forward thedata packets. The forwarding devices can be controlled andprogrammed through an open interface by using the Open-Flow protocol to become pure data packet forwarding devices[5], [6]. SDN plays the influential role in reducing the power,

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minimizing the number of physical devices, decreasing thecost of installing a network, reducing control delay time,and in allowing the network administrator to configure theinstructions for the CP which are executed by the centralizedcontroller [7]. The integrity of SDN is NFV technology [8],which enables building an End-to-End network underpinnedby utilizing virtualization technology standards to empowerthe consolidation of different communication network de-vices [9]. Cloud computing paved the way to coping con-veniently with mobile communication network infrastructure[10]. The infrastructure of communication systems, whetherit be wireless, wired, motionless or mobile networks, thevirtualization technology is growing dramatically to designand build these networks. Such virtualization can be seen inmobile cellular air networks, such as Cloud Radio AccessNetwork (C-RAN), which is emerged as one of the results ofcloud computing. In C-RAN the BBUs (whether virtualizedor not) are gathered in a centralized BBU pool, where thefundamental RF functions are accomplished, such as estab-lishing the initial connection, processing RF signals and man-aging and maintaining the established connections. The pro-posed SVeNB has the ability to perform these responsibilitiesdepending on the users’ profiles that are received from theBBU and the CN. These user profiles have already processedby the BBU and the CN and sent to SVeNB which coversthe local users within its coverage area. This information isstored as a backup,for instance, to be used by SVeNB to em-power the resident subscribers who are covered by it, withoutcommunicating with the BBU pool and the CN. This processenormously mitigates the load on the CN, the BBU pool,while also lowering decisions making latency, which leads toexpediting up the establishment of the connection of the End-to-End device. Our proposal presents a new mini-base stationfor 5G and beyond for mobile communication networks thatuse virtualization techniques. The main contributions of thisresearch are follows:

• Proposing a novel mini-base station that utilizes dif-ferent virtualization technologies to simulate the kernelfunctions of mobile communication networks;

• Mitigating the burden on the BBU pool and the CNby making connection decisions between users withoutneeding to contact the BBU pool and the CN;

• Decreasing the End-to-End delay by reducing the num-ber of control messages amongst the serving entities ofmobile networks.

II. BACKGROUND AND RELATED WORKSThere has been much research on virtualizing wireless mo-bile Base Stations (BSs) and some of this literature is con-sidered here in chronological order. In 2010, Zhu et al.executed the first TDD-SDR BS on a server and also, thefirst functional model of a Virtual Base Station (VBS) poolwas presented. The outcomes satisfied the demands of thesystem used at that time [11]. Likewise in 2010, Bhanageet al. introduced a proposal for a virtualization wide-areanetwork for 4G devices in a cellular BS to empower users

participating among multiple autonomous slice users [12].By 2011, the design of a mobile system that could reconfigureitself called Reconfigurable Mobile Network (RMN) hasbeen put forward by Khan et al. They employed virtualizationtechniques to distribute the resources of networks [1]. In2012, a design of Network Virtualization Substrate (NVS)for base station uplink and downlink sources split into partswas provided by Kokku et al. They adopted two situations:firstly, they showed the optimal slice list with resourcesand bandwidth bases. Secondly, they customized the flowscheduling within the base station on a single-slice basis [13].By 2013, the importance of isolation resource requirementsbeing provided to BSs had been grasped by Kiess et al.They focused on OFDM as a wireless interface, preparingfor the virtualizing of an OFDM base station [14]. In 2014,a structure for virtualizing the Long Term Evolution (LTE)BS was introduced to improve the subscribers’ performanceand decrease the interference of the downlink beacon. Theyemployed the assets of OpenFlow and SDN. The outcomesrevealed improvements in the throughput, the average in-terference, and lost packets transferred between End-to-Endusers [15]. Nakauchi et al. suggested a virtual base station,with the capability to produce dynamic virtualized wirelesscommunication networks. They utilised a couple of tech-niques, first, a VBS configured dynamically and controllingseveral physical wireless Access Points (APs) by applyingthe OpenFlow features. Second, seamless handover is han-dled between VBSs pre-authentication and pre-associationat the target VBS [16]. Xiaodong et al. utilised FNA withinSND for next-generation mobile networks with MOFP havebeen provided. Furthermore, they introduced an adoptionstrategy to concentrate on the User-Centric scenario in FNA[17]. Dawson et la. depending upon C-RAN, suggested aVBS network structure that enables connection to the CN[18]. In 2015, Wang et la. proposed virtual base stations,where every cell is dynamically determined by allotting virtu-alized signal sources to the fronthaul link for both the RRHsand BBUs. The functional process of the BBU is cloudifiedand virtualized according to the functional entities perfor-mance [19]. The considerable advancement of industries ofelectronic communication devices, especially in relation tofixed, wireless, and mobile devices production making thesedevices in tiny sizes. The Field-Programmable Gate Array(FPGA) technology is a hopeful advancement for solving theneeded elasticity for producing and implementing 5G andbeyond mobile communication networks. FPGA assists inthe LTE scheme virtualization by carrying out a particulartask in the CP. This usage can be observed in multi-mode basestation infrastructure devices [20]. Virtualizing a single entityof a CN was introduced by Heinonen et al., who workedon the mobility and latency of 5G network. They broughtforward the Mobility Management Entity (MME) to be setin a virtualized network located between the radio accessnetwork and the CN [21]. In [22], a logical architecture for5G systems is proposed, in the form of a scheme for man-aging mobility among different access networks based on

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SDN and NFV. However, to date, the current communicationnetworks, especially wireless and mobile networks have notwholly been virtualized [14], [21].

III. PROPOSED SYSTEMMobile communication systems suffer from End-to-End la-tency due to different types of delays such as RF registra-tion delay, control delay, transmission delay, and processingdelay. Minimizing these can be achieved by adopting newprocedures that manipulate the establishment and negotiationof a contact session. The servers (BBU and CN entities)are responsible for making decisions of an End-to-End con-nection based on executing the function programs. Theseservers are usually placed away from the User Equipments(UEs). To decrease the End-to-End latency, we proposed anovel BS that contributes to making connection decisionsthrough virtualizing the functions of the BBU and the CNservers and administering these functions near the UEs. Thenext section presents an explanation and illustration of theproposed system.

A. EPS FUNCTIONS SLICING AND VIRTUALIZINGThe emergence of new technologies is helping in the designand creation of different types of network topologies. Thesetechnologies such as SDN, NFV, network virtualization, net-work slicing, network cloudification, and other virtualizingtechniques, are facilitating the network service providersand operators in eliminating the subordination of traditionalphysical network infrastructure elements and the dedicationof physical servers for each function [8]. The functions ofthese servers can be performed by adopting an appropriatevirtualization technique for emulating in making decisionsthat are yielded by completing the execution of those func-tions. These decisions can be defined as the outputs of thosefunctions that are implemented by the BBU pool and the CNentities, which follow software or programs that control thefunctions. Because the EPS system makes decisions (outputsof function) to establish, create and maintain a connectionbetween End-to-End according to those functions. ThereforeSVeNBs’ VMs host the kernel of those functions to makeits decisions regarding the local UEs. Examples of the EPSfunctions that are hosted by SVeNB are:

• Policy Control and Charging Rules Function (PCRF):detects the service flows, determines how data streammoves, and it is responsible for charging policy;

• Packet Data Network Gateway (P-GW): is responsiblefor assigning IP addresses to UEs, implementing a QoSapplication, and following PCRF per flow rules;

• Serving Gateway (S-GW): creates, deletes, and modifiesthe bearers for each UE that connected to the EPS.Performing of these functions depends on per PacketData Network (PDN) established connections for eachUE;

• Mobility Management Entity (MME): achieves thehigh-level signaling of the UE , the issues of authenti-

cation and security, besides to the management of datastreams that unrelated to radio communications.

Functions slicing is defined as partitioning a function be-tween two servers or VMs to complete a specific task to makedecisions, one of which is complementary to the other?sfunctionality. One of these servers represents a MASTERentity, which is located at the CN, whereas the second isa SLAVE installed in SVeNB as a VM. The SLAVE entityreceives a function task pre-processed in the MASTER entity,that is, the former acts to complement the function alreadypre-processed by the latter. While functions virtualizing tech-nique supports the possibility to virtualize any task or func-tion that could be achieved by software programs, to bevirtualized through a VM to produce session establishmentdecisions. i.e., functions of the BBU pool and CN serverssuch as MME, S-GW, P-GW, and PCRF as traditional off-the-shelf servers could be logically virtualized their functionsby suitable VM’s configuration [23].

B. ARCHITECTURE AND FUNCTIONS OF THE SV ENB

Any virtualization of a network device needs three mainphysical hardware elements to build and implement a VM:CPU, RAM, and storage (HDD or SSD). Moreover, threemain software elements, including an Operating System(OS), application programs, and data need to be considered.The SVeNB has been proposed to consist of one small devicehas CPU, RAM, storage, besides to peripheral slots allow toinsert FPGA boards, which could be added for achieving atleast one of the EPS server functions. Each slot represents atleast a single VM depends on SVeNB physical hardware andshares its resources. The SVeNB has three main interfaces,the first being connect to the BBU pool and CN, whilst thesecond is to connected to the Internet gateway and the thirdinterface connected to the RRH. A hypervisor is used tocoordinate the VMs with physical resources. Figure 1 showsthe proposed architecture of the SVeNB.

FIGURE 1: The architecture of SVeNB

The functions of the SVeNB are that it will have theability to achieve a connection between the local subscribersor to the Internet directly. It can perform this connectionwithout waiting for the decisions that are usually made inthe EPC to create the requirements for the session. SVeNBcan serve the subscriber who wants to make this session orto connect to the Internet. This is based on the requirements

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that are pre-processed in the BBU pool and CN for all localsubscribers covered by SVeNB. All user profiles are savedin the storage of SVeNB as cached user profiles. Accordingto the user information profiles, SVeNB controls and governsthe connection conditions between local subscribers. It takespart in making the decisions that are pre-negotiated with theBBU pool and the CN. It saves these decisions to be appliedto make the final ones that enable the subscribers to commu-nicate with each other. Also, it reserves pre-emption of bearerradio resources (ready to use), which are assigned by SVeNBto the users who want to connect with EPC without askingthe BBU pool and CN again. The allocated IP addresses byP-GW are stored in SVeNB as ready to use. The SVeNB?sVMs can detect and serve the type of required service, such asassigning an IP address. Consequently, based on this servicedetection the controller can decide to give QoS to the user.Moreover, it selects the interface to flow the classified data.Depending on the rules of PCRF (operates in real time inorder to determine policy rules in the CN), the controlledflow-based charging functionalities in the Policy Control En-forcement Function (PCEF) that are virtualized and installedon the VM that works as the PCEF virtual server (supportsoffline and online charging interactions while PCRF doesnot support these). This virtual PCEF is responsible fordedicating an appropriated link connection, charging sessionestablishment, and maintaining the established connection.The LTE and EPC functions that determine the decisions ofa connection establishment are sent to SVeNB, which storesthese decisions to be used for resolving the final decisionsof the contact establishment. This procedure will lighten theload on the BBU pool and the CN. Furthermore, this willdecrease the End-to-End delay, and expedite the process ofinitiating the communication connections, in particular, whenthe local users attempt to link with others or to the Internet.

C. UE − ENB CONNECTION IN C-RAN AND SV ENB

In LTE, the Random Access Channel (RACH) consists of64 Random Access Preambles (RAP) allocated for each cellof an eNB and there are two ways to get RACH by a UE:(i) Contention-based, where any UE can access this whenin need of an uplink connection; and (ii) Contention-freeRACH can be used in cases, such as handover and downlinkdata stream, where low latency is required. The messageexchange between the UE and eNB is either for location areaupdates or for initial access to set up a connection. In thelatter case, the UE sends a request via a RAP message onthe Physical Random Access Channel (PRACH) resourcesassociated with Random Access Radio Network TemporaryIdentity (RA-RNTI) to the eNB, which covers the UE. TheeNB replies with a Random Access Response (RAR) onthe Physical Downlink Shared Channel (PDSCH), whichis addressed by an ID and then, RA-RNTI is sent. TheUE sends Layer 2 / Layer 3 messages. This information isthe first scheduled uplink transmission on the PUSCH andformulates the use of a Hybrid Automatic Repeat Request(HARQ), including carrying of UE identifiers. This is the

actual random access procedure message. The last action isthe contention resolution message, which the UEs respondto on reception in two possible ways. The first is through aPositive Acknowledgement (ACK), which indicates that theUE has accurately decoded the message and thus, detectedits own identity. The second probability is DiscontinuousTransmission (DTX), which means that either the UE hascorrectly decoded the information and discovered that itcontains another UE?s identity (contention resolution), or itcannot decode the message. Figure 2 illustrates the randomaccess procedure [24], [25].

FIGURE 2: Contention-based Random Access procedure

At this point, our proposed SVeNB works as legacy eNB,based on the local user profiles that are saved by its stor-age unit. The installed VMs check the user profiles to seewho needs service and then, it takes a decision (to beginthe establishment of a session or not), according to havingchecked the user charge allowance, authentication, and otheruser profile matters. Subsequently, it assigns an IP addressto any user who needs to make a call or contact the Internet.The connection between SVeNB and the Internet can be madein two ways. The first, is via the direct fiber link that isconnected directly to the Internet through the operator?s S-GW, thereby bypassing the BBU pool and CN. This pathis the default way to reaching all IP networks. The secondway is through the BBU pool, CN, and Operator?s S-GW,which is a backup path for connecting to all IP networks.The default path mitigates the load on the BBU pool and theCN, expedites the data delivery to the users, decreases endto end latency, increases the amount of delivered data by endusers, and decreases the utilization of network bandwidth forcontrol messages.

IV. END-TO-END DELAYS ANALYSISIn this section, discussion on End-to-End delays or sessionestablishment delays is presented. We compared the pro-posed SVeNB and C-RAN structure connected by the fiber

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in the fronthaul and backhaul links, concentrating on theEnd-to-End delay analysis. The scenario suggests that twolocal UEs try to connect to each other within one RRH. Wecompared our proposal with the C-RAN system to initiateconnection steps. Figure 3 shows the representation of theC-RAN steps of session establishment verification. In step1, the user contacts the RRH to get the radio bearer and IPaddress, whilst Step 2 the RRH asks the BBU for this request.The BBU processes the measurements of digital signalingand forwards the request to the CN to complete the sessionestablishment. During Step 3, the CN performs the principalproceedings, such as determining the QoS for the bearer, IPdistribution, and allowance charging, etc. This requires allthe source CP messages crossing through the BBU and CN.Steps 4, 5, and 6 describe preparing to communicate with thetarget UE.

FIGURE 3: The C-RAN End-to-End CP session establish-ment

Figure 4 represents the principal steps of processing toset up an End-to-End association. The transmission delaybetween the RRH and the BBU (fronthaul link) as well asthe BBU pool and the CN (backhaul link) relies upon theseparation distance of the connection between them. In theCN, there are several servers or entities executing the jobsof establishing the End-to-End session, with each having itsprocessing and queuing delay times. The connection stepsabove mentioned become more complicated if the users areunder different RRH coverage, for, the BBU and the CNmake a check on each user to initiate the connection betweenthem. Consequently, in such a case the End-to-End delay

FIGURE 4: The C-RAN End-to-End CP processing steps

will increase due to need for more processing to achieve thesession connection. However, with our proposal, the sessionconnection is manipulated and treated locally in SVeNB,which initiates the connection based on the information re-ceived from the BBU pool and CN.

SVeNB accomplishes the tie without attaching to the BBUpool or the CN again. The idea is a creative step because ofthe semi-centralized nature of initial connection processingand the capability of SVeNB to make decisions for the UEsas a semi-standalone base station. Figure 5 explains the

FIGURE 5: The Proposed SVeNB End-to-End CP sessionestablishment

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structure of this scenario. The procedure of this scenario asfollows.

• Step 1 depicts the UE?s request to obtain a bearer andIP address. SVeNB responds to this request by checkingthe user profiles stored in its storage unit. Then, theinstalled VMs of SVeNB can allot and assign the bearerand the IP to that UE. These VMs perform the CPmeasurements of this session, such as authentication,security, charge allowance, and IP assigning.

• Step 2 depicts the association with the destination UE tobegin the session and exchange data between it and thesource.

Figure 6 explains the procedure for achieving this associa-tion. When the user contacts SVeNB, the latter checks thelocation of the UE by the virtual MME (VMME) to verifythe position and authentication through the interaction withthe virtual HSS (VHSS). A virtual machine (P/S-GW) actsas a gateway or mobile IP anchor of SVeNB to the Internet,with the radio bearer and IP address being assigned in thisstage. The connection of two users that are covered bya different coverage area of RRHs is easily prepared andexecuted by SVeNB. This is owing to the information ofRRHs, such as frequency bands and IP addresses, havingbeen saved and ready to use by SVeNB. Hence, SVeNB caneasily overcome the complication of communication betweentwo supported by different RRHs. In other words, users donot need to contact the BBU pool and the CN to allow forsession establishment. As a result, the End-to-End delay timedecreases due to no more demand for processing to achievethe session connection.

At the end of these steps, the user has two choices. Thefirst, is to connect to another user, which is also covered bythe same SVeNB or it covered by another one. The secondchoice is to connect to the Internet via the direct fiber link,which has been proposed to connect SVeNB directly to themobile operator?s S-GW in case the UE requires surfing ofthe Internet, besides to the regular path (SVeNB-BBU pool-CN-S-GW path).

FIGURE 6: The Proposed SVeNB End-to-End CP processingsteps

We assumed in the C-RAN system, the delays time of a

transmission between a user and RRH is tLRC which impliesthe radio link control delay. The link delay between the RRHand BBU pool which stands for the fronthaul delay is (tF ),and the delay between the BBU pool and the CN as the back-haul delay is (tB). DT is the summation of the transmissiondelays of the radio bearer of the RRH, the fronthaul, and thebackhaul links. Hence, the total transmission delay can berepresented by the Equation 1,

DT = tLRC + tF + tB (1)

The processing delay time in the BBU pool is tPBB , whichexpresses the overall time that is consumed by the BBU poolfor the CP signal to initiate the association. The processingdelay time that is spent by the CN is tPCN , which denotesthe overall time that is consumed by the CN for controlinformation data to establish that connection. Hence, thesummation of the processing delays of the BBU pool and CNis DP . It can be estimated by,

DP = tPBB + tPCN (2)

DQ is the summation of queuing delay time in the BBUis tQBB , and the queuing delay time in the CN is tQCN .Consequently, DQ can be expressed by,

DQ = tQBB + tQCN (3)

Consequently, the total delay time D can be expressed withthe following equations,

D = DT +DP +DQ (4)

The processing and queuing delay times in the RRHsare very small, so it can be ignored. All these time delaysare introduced by the C-RAN and the CN just for a ses-sion establishment of End-to-End association (i.e. only CPmessages exchange). Whereas with SVeNB, the equivalentconnection can be made, but with the elimination of DT .This is because this delay time has already been consumed bythe first attachment of the BBU pool and the CN by SVeNBwhen the profiles of the subscribers are manipulated and sentto the SVeNB. The processing and Queuing delay times arereduced because they are processed by one physical devicethrough many VMs supported by SVeNB. This leads to adecrease in the period of the delays time of the processingand the queuing. Therefore, to compare between the SVeNBand C-RAN, we should take into consideration those facts.With SVeNB the link delay time will be dropped from thedelay calculation, whilst the processing and queuing delaysshould be estimated as a VM. Hence, the delay time equationfor SVeNB will be,

DS = DPS +DQS (5)

where, DS is the total delay time in SVeNB, DPS isthe processing delay, and DQS is the queuing delay in theSVeNB. TheDPS andDQS notably rely on the specifications

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of the SVeNB physical hardware and capital expenditure candecrease this delay through hardware with high specifica-tions.

A. RADIO LINK DELAY ANALYSISAccording to 3GPP TS 36.213 and TS 36.306, for trans-mission in mobile networks, the Radio Link Control (RLC)has been using in the LTE to enhancement the Frame ErrorRate (FER), according to the condition and availability ofthe Bandwidth (BW) channels. However, we assume usingdifferent BW for LTE (5, 10, 15, and 20 MHz) and then, thecapacity of each channel can be found by a simple calcula-tion. Consequently, the number of frames per packet (k) canbe calculated for each channel rate. The LTE frame durationτ equals to 10 ms. Also, assume modulation scheme QPSKis used. The 1526 bytes is a Maximum Transmission Unit(MTU) for IPv4 packets. While IPv6 can convey much largerpackets that make it well matched with the LTE system,which can handle the large size of packet or frame. The LTEframe is known as a Transport Block (TB) and it containsnumerous sub-frames. Every sub-frame consists of two slots,each of which has six or seven OFDM symbols accordingto the extended or normal cyclic prefix, respectively. Thenumber of bits in each symbol depends on the modulationscheme. In LTE, the maximum control message size does notexceed 10% of the sub-frame that is assigned to the UE, withthe number of bits (b) in one sub-frame being:

b =2(RB)× 12 (sub − carriers)

× 7 (assuming7OFDMsymbols)

× 2 (slotspersub − frame)×M=336×M

M represents the modulation scheme. If the QPSK is usedfor control messages, then M equals 2. So, 336 x 2 x 0.1 (10%as assumed above) ≈ 68 bits. Consequently, the number ofbytes per Transmission Time Interval (TTI) can be calculatedfor the specific channel as the channel BW multiplied by TTIdivided by 8, for instant, 10 Mbps x 10 ms x 1

8 = 1250 Bytes.This leads to the smallest transmission channel capacity thatwill cover the largest transmitted control message.

Assume the probability for QPSK to transmit an RLCframe successfully is:

Ps = 1− P (Bn) (6)

The frames that are dropped or lost is denoted as Bn , andn is the number of re-transmission trial (typically 3). Theprobability of effective packet loss is:

Pf = P (Bn) = P [P (2− P )](n+n2)

2 (7)

Assuming that the LTE channel operates under normalconditions, the probability of the FER is p = 10−2, and n ≤3, the Pf can be calculated as follows:Pf = 10−2[(2× 10−2 − 10−4)]6 = 6.21× 10−13

From [26], [27], and [28], the typical value of the prop-agation delay is Dpr = 100 ms. Thus, the equation of RLCtransmission delay can be calculated as,

tRLC =Dpr +[Pf − (1− P )]

P 2f

× |n∑j=1

j∑i=1

P (Cij)

× [2jDpr + (j(j + 1)

2) + i)× τ ]|

(8)

The first frame is received by end point properly is denoted asC(ij), at re-transmission ith of the transmitted frame at jthre-transmission trial.

B. FIBRE LINK DELAY ANALYSISThe speed of light v decreases when it transfers into fiberoptics cables due to the refractive index nr of the materialmade these cables. The speed of light c in free space is299792.458 km/s. In the single-mode optical fiber transmis-sion, the standard wavelengths are 1310 nm and 1550 nmwith refractive indices 1.4676 and 1.4682, respectively. Then,the delay time for a 1 km length of the fiber and a differentrefractive index according to a particular wavelength can becalculated by Equation 9,

tλ =1km

vλ=

(1km× nλ)c

(9)

The delay times for wavelength 1310 nm and 1550 nmare 4.895 µs and 4.897 µs, respectively [29]. So, for theRound Trip Time (RTT) for these values should be doubled.In the C-RAN architecture, the distance between the BBUand RRH is restricted by processing and transmitting delaytimes which should be not more than 3 ms (the period fromuplink to downlink) [30]. In other words, when increasing thedistance between the BBU and RRH, the processing delay ofthe former should decrease. The maximum distance of fiberoptics cable that links the BBU pool and the RRHs can begiven by solving the following equation

Md =RTT × c2× nλ

(10)

Equation 10 can be used to determine the maximum distancebetween the BBU and RRH with different kinds of opticalfiber link for a specific λ.

C. QUEUING DELAY ANALYSISQueuing delays between the sender and the receiver UEsdepend upon the amount of data packets in every specificqueue at each entity in the network. The queuing delaytime for establishing the EPS session can be representedas the summation of the delays of all the entities involvedin initiating the session of the CN network and the BBUqueuing delays. AS these entities successively process itsfunctions due to some of the entities (Master) administratorand control the other entities (Slave). In other words, some

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decisions determine the beginning or lunching of other func-tion to make decisions. For example, the decisions regardingauthentication and allowance charging functions are firstmade, then the other functions are implemented to make thefinal decisions to establish a session connection. The finaldecisions are yielded from execution the functions of diverseentities to establish this connection. As a result, we can con-sider the M/M/1 queue assumption of CP messages at theseentities. We proposed for each entity of the EPS network aqueuing model of M/M/1 queue and Poisson signaling arrivalrate process. For M/M/1, if the first input rule is Poisson,then the subsequent stage input is additionally Poisson andindependent of the input process and so on [31], [32].

Assume Z is the number of entities (the BBU and the CN)included in session establishment, δ is the Poisson arrivalprocess rate (packet/sec), and µ is the transmission arrivalpacket rate of the queue (packets/sec). Every element allowsa traffic load of ρi= δi /µ, i = 1, 2, ... , Z. Moreover, inmobile communication networks some of the servers dependon others to make their decisions, i.e. the ith entity receivestraffic from the leader (Master ) entity (any chief entity couldbe either a leader or follower (Slave) as per the instance ofthe necessity of information preparing among them) with apacket arrival rate δi. Consider the queuing delay time atthe recipient buffer only (for simplicity) [33]. The expectedsum of the queuing delay time is the sum of the expectedqueues in tandem at every element, which can be representedas follows:

E[X] =

Z∑i=1

Ei

[1

µi

](11)

where, X = 1/µ is the service delay time of the entity, andµ is the transmission packet rate of the queue. These amountsof delays can be determined by formulas that are derived bythe Markov chain. Then, the possibility of there being packetsin the queue is, P = ρN (1 − ρ), where, N is the number ofpackets that are sent by a channel, and ρ = δ/µ, from this wecan get N = ρ/(1− ρ). So, the delay time for one entity is,

tQ =N

δ=

ρ

δ(1− ρ)=

1

(µ− δ)(12)

For the C-RAN system, the total queuing delay time canbe given as:

DQ =

Z∑i=1

1

(µi − δi)(13)

Whereas in SVeNB the queueing model is M/M/m [33] be-cause its VMs perform m jobs and consequently, the queuingdelay time will be expressed as [2]:

DQS =

Z∑i=1

1

(mµi − δi)(14)

D. PROCESSING DELAY TIMETo estimate the processing delay time that is required to buildup a session in the EPS, we need to delimit the time that isconsumed per entity to complete its responsibility. Authorsin [30] mentioned that the BBU processing for one roundtrip is 3 ms. The EPC entities (servers) need to performfurther processing on arrived packets, i.e. the entity shoulddecapsulate the arrived packets to take a decision and thenencapsulate them again. The processing delay time dependsupon the packet length. Let L denotes the IP length (32 bit or128 bit), Ms is the machine word size, W represents arrivedword size, R is the user profile record size, and S representsthe size of the server’s processor architecture, e.g., 32 or 64bits of the processor of an entity. We propose that the memoryaccess correlates with the lookup delay time at around 100 ns.So, the processing delay time for one entity can be obtainedfrom [34]:

tP = 100W

Ms×[logsysR +

L

S

](15)

Next, we have derived the total processing delay timeequation as:

DP =

Z∑i=1

F∑j=1

100Wij

Msi×[logsysRij +

LjSi

](16)

where, Z represents the number of entities of the EPS, Fdenotes the number of the user’s messages to a specific entity.j th is user’s parameters to be treated by i th entity, and sys isthe system constant [2].

V. PERFORMANCE EVALUATION ANALYSISThis section discusses the results of the local UEs? End-to-End delay control messages, with consideration of bothSVeNB and C-RAN. Figure 7 explains the relationship be-tween the service rate and the End-to-End delay of the controlsignaling to establish a session for local UEs that are coveredby a base station. At the service rate of 310 packets/secondSVeNB performs with a delay time of almost 4.5 ms. Whilstin C-RAN it is slightly more than 9 ms. Both systems producelower values in the delay time when increasing the averageservice rate. However, the SVeNB processing delay timebecomes one-third of that of C-RAN at the service rate 400packet/second. The performance of SVeNB for control planesignaling to establish End-to-End session is at least double ofthe C-RAN in the case of the low value of average servicerates. The delay time reduces with increasing the servicedpackets rate. When initiating the session, SVeNB and C-RANalmost work in a similar way (i.e. the initial access to setup a session establishment), with each executing almost thesame procedure to set up the RACH for every user. However,following the setting up of the RACH the delay time forSVeNB becomes much less than with C-RAN due to theformer not needing to connect the BBU and the CN, because

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FIGURE 7: Session establishment CP delay with service ratecomparison

all the information that it requires to achieve a session isalready stored into its storage unit [2].

Figure 8 explicates SVeNB?s superiority over C-RANwhen serving the same number of served user profiles. Thefigure exhibits that when a user attempts to establish aconnection, both systems take a long time of processing inthe beginning, due to having to prepare the associated dataof the user to switch from the inoperative to operative state.Subsequent to that, both systems arrive in a steady state caseand the processing delay time experienced with SVeNB isaround 0.2 µs at 100 bytes of user profile size. In contrast,C-RAN consumes approximately 0.6 µs for the same userprofile size. Expanding the size of the user profile in the rangeof 100 times will provide an additional delay time of about0.1 µs in both systems for the steady-state case.

FIGURE 8: Session establishment processing delay and userprofiles size

In C-RAN, the BBU and the CN serve several cells and

hence, the processing delay time grows according to theincrease in the number of served cells. As a result, theprocessing delay time in C-RAN is greater than SVeNB, thisbeing due to the massive number of user profiles and growthin their size. Intuitively, the base station should wait for moretime to be served by the C-RAN structure.

FIGURE 9: Average Delay per User against Packets ArrivalRate

Fig. 9 shows the average delay versus packet arrival ratefor a single user. Considering the transmission, queuing,and processing delays, SVeNB provides higher performancethan C-RAN in delivering data to a user for the same cir-cumstances. This means that the data rate is increased asthe total delay (transmission, queuing, and processing ) isdecreased, when comparing the attainment between SVeNBand C-RAN. Moreover, the average delay in C-RAN expandsdramatically due to the accumulation of different deviceswhich cause delay, while SVeNB does not suffer from suchdelays. This fact is true if we exclude the first contact to theBBU and the CN by SVeNB for acquiring the profiles of theusers. The provided service rate of a network is influencedby the number of users that are served by it. That is, theservice rate decreases with an increasing number of users inthe network. This degradation is the result of the numerousoperations performed by separated physical machines in theC-RAN network to perform a particular task. SVeNB over-comes this obstacle through virtualizing and mimicking theseseparated servers and functions, whilst gathering them into asingle physical device. Fig. 10 presents the service rate versusthe number of users. Also, the figure shows the performanceof the proposed SVeNB against C-RAN network. In practicallife, UE needs to communicate with the BBU and the CNto perform the important measurements to initial the session.Fig. 11 illustrates the average delay of transmission links thatconnect the RRH, BBU, and CN. At the starting point of thegraph, both SVeNB and C-RAN required the same time to be-gin a session. SVeNB has the ability to handle and process therequirements of a session without the need to communicate

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FIGURE 10: Impact of Increasing number of Users on Ser-vice rate

FIGURE 11: Average Transmission Delay per User andCommunication With BBU and CN

with the BBU pool and CN again. Conversely, the C-RANnetwork cannot initiate a session without contacting themmore than once and thus, the line graph of SVeNB drops to aminimum value, whereas that of C-RAN rises to a maximumone.

Fig. 12 exhibits the End-to-End average delay comparisonbetween SVeNB and C-RAN. This increases as the numberof nodes increases due to each node (physical or virtualentity) suffering from its own delays, such as transmission,queuing, and processing. SVeNB can handle multiple criteriafor user profiles at the same time and on the same server.This procedure leads to a reduction in the End-to-End delaytime required to establish a session between users. Moreover,the End-to-End average delay in C-RAN grows dramaticallydue to the successive delays of different devices that includeestablishing a connection. While SVeNB does not suffer from

FIGURE 12: Average End to End Delay

the accumulative delays as well as in C-RAN which needs toacquire the profiles of the users from different servers.

VI. CONCLUSIONThis paper has presented a novel base station idea based onvirtualization technologies and the End-to-End delay timehas been analyzed. The outcomes indicate that the delaytime to establish the End-to-End association by the proposedSVeNB is less than that by the C-RAN. Mobile networksneed to make substantial progress to meet the requirementsof 5G networks and beyond. This requires more interactionbetween the new technology of the physical infrastructureand VMs to achieve complete virtualized networks. Themost significant delay time is consumed by the processingof the arrived control data packets from/to the UE andCN for establishing the End-to-End link. Most notably, theprocessing delay of the control plane messages is where thehighest delay time occurs. Moreover, control plane signalingin best conditions requires more than 60 ms to establish asession. Our proposal concentrates on lowering this delaytime by virtualizing and slicing the primary functionalitiesof the BBU pool and CN. Moreover, bringing these functionsclose to the end user enables SVeNB to produce a significantreduction in control plane delay time. A comparison has beenperformed with the results that were obtained by SVeNB andthe C-RAN emulating approach. The comparison involvedvariant values of average service rates. SVeNB exhibitedaround 62% less delay time than C-RAN. In addition, theperformance of SVeNB is higher than of the C-RAN by 68%in terms of user profile processing. Our recommendation isthat a considerable solution to overcoming the End-to-Enddelay problems is by virtualizing and slicing functions of theBBU and CN. Also, this requires bringing those virtualizedfunctions close to the end user. The semi-decentralization ofthe processing functions mitigates the load on the BBU pooland CN.

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FOUAD A. YASEEN received the BSc degreefrom the University of Technology, Baghdad, Iraq,in 1994. He received the MSc degrees from Uni-versity of Baghdad, Baghdad, Iraq, in 2010, bothin electronic and communications engineering.He is currently doing his PhD at Brunel Uni-versity London since 2016. His research inter-ests include wireless communication networks andmobile communication systems. He is a studentmember of the IEEE.

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HAMED S. AL-RAWESHIDY received theB.Eng. and M.Sc. degrees from the University ofTechnology, Baghdad, Iraq, in 1977 and 1980,respectively, the Post Graduate Diploma degreefrom Glasgow University, Glasgow, U.K., in 1987,and the Ph.D. degree from the University ofStrathclyde, Glasgow, in 1991. He was with theSpace and Astronomy Research Centre, Baghdad,PerkinElmer, Waltham, MA, USA, British Tele-com, London, U.K., Oxford University, Oxford,

U.K., Manchester Metropolitan University, Manchester, U.K., and KentUniversity, Canterbury, U.K. He is currently the Director of the WirelessNetwork Communications Centre, Brunel University London.

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