Survey on Bandwidth Availability Prediction Models forLTE Networks

Stijn van WinsenUniversity of Twente

P.O. Box 217, 7500AE EnschedeThe Netherlands

s.vanwinsen@student.utwente.nl

ABSTRACTNumerous bandwidth availability prediction models havebeen proposed for cellular networks in the past. How-ever, none of these prediction models were designed towork with virtualized Long Term Evolution (LTE) cellu-lar networks. This paper uses the requirements imposedby virtualized LTE systems and compares how existingbandwidth availability prediction models can satisfy them.None of the found bandwidth availability prediction mod-els can completely satisfy the virtualized LTE based re-quirements.

KeywordsBandwidth availability prediction, LTE, Cellular networks

1. INTRODUCTIONThe amount of mobile users is evermore increasing andusers are demanding more and more from the mobile in-frastructure. To comply to this increasing demand, re-searchers are working on the development and improve-ment of a new networking technology called Long TermEvolution (LTE).

LTE is a new technology that uses a packet-switched net-work for the support of any type of services, including realtime services, e.g., telephony, instead of using a circuit-switched network. This means that a new architecturewas developed for this system of which an overview isgiven in figure 1. The LTE cellular network consists of twomain parts, which are the Evolved UMTS Terrestrial Ra-dio Access Network (E-UTRAN) and the Evolved PacketCore (EPC) network. The typical E-UTRAN consists onlyof evolved Node-Bs (eNode-s), which represent the BaseStations (BSs) used to provide radio access to all UserEquipment (UE) that are within its radio coverage. TheEPC provides permission to UEs to access the LTE cel-lular system and support for multimedia service connec-tivity, roaming and mobility. The EPC consists of severalnetwork entities such as the Mobility Management En-tity (MME) that supports the mobility management, theHome Subscriber Server (HSS) that maintains the sub-scription profiles of each user, the Packet Data NetworkGateway (P-GW) that represents the packet data network

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copiesare not made or distributed for profit or commercial advantage and thatcopies bear this notice and the full citation on the first page. To copy oth-erwise, or republish, to post on servers or to redistribute to lists, requiresprior specific permission and/or a fee.19th Twente Student Conference on IT June 24, 2013, Enschede, TheNetherlands.Copyright 2013, University of Twente, Faculty of Electrical Engineer-ing, Mathematics and Computer Science.

Figure 1. LTE Architecture

gateway to the Internet, the Serving Gateway (S-GW)that manages the user data tunnels between the eNode-Bs and the P-GW, under the supervision of the MMEand the Policy and Charging Rules Function (PCRF) thatmonitors and controls the policy rules and charging ofmobile users for the services they are using. Currentlymobile cellular networks are highly centralised and there-fore they are not optimised for high-volume data applica-tions, which will evolve with 4G (e.g., LTE) and beyondtechnologies. Using shared distributed mobile network ar-chitectures bottlenecks can be avoided by better utilisingavailable resources and minimise delay. Virtualized LTEsystems could be used, where the cloud computing modelis applied in LTE systems, to solve this issue by offeringdecentralised computing, smart storage, on-demand, elas-tic and Pay-asyou-Go services to third party operators andusers, see e.g. [6].

Because of the increasing usage of mobile systems andthe limited bandwidth the cellular network can give, themost crucial resource in Mobile Cellular Networks is stillbandwidth. It is therefore vital that the bandwidth thatis available is utilized as best as possible. Current 3Gand older networks already use mechanisms to provide aquality of service to its users to prevent interruptions intheir mobile usage. If, for example, the networks turnsout not to have enough bandwidth available, it will haveto interrupt an ongoing mobile connection, which leadsto unpredictive behavior. To prevent this, prediction ofthe available bandwidth in coverage areas (e-nodeB con-trolled cells, S-GW and P-GW controlled service areas),over time will be useful. These so-called bandwidth avail-ability prediction models are used to predict this availablebandwidth over time in the different parts of the cellularsystem.

Research on bandwidth availability prediction models hasbeen done in the past, but this research was mainly fo-cussing on older cellular networks. This paper uses the re-quirements imposed by virtualized LTE systems and com-pares how existing bandwidth availability prediction mod-els can satisfy them.

The main research question is: ”Which bandwidth avail-ability prediction models can be used in LTE cellular sys-tems to predict bandwidth availability in LTE cells and onan LTE end-to-end path?”

This research will answer the following subquestions:

1. What are the LTE requirements on bandwidth avail-ability prediction?

2. How can bandwidth availability in cellular systemsbe accurately predicted?

3. Which bandwidth availability models are currentlyavailable?

4. Which bandwidth availability prediction model(s) cansatisfy the LTE requirements on bandwidth avail-ability prediction?

The research questions 1, 2 and 3 will be answered usinga literature study. Question 4 will be answered by do-ing a qualitative comparison. Section 2 answers the firstresearch question and presents the requirements imposedby virtualized LTE cellular on bandwidth availability pre-diction models. Section 3 answers research question 2 byproviding a taxonomy of bandwidth availability predic-tion models. Section 4 answers research question 3 andpresents a number of existing bandwidth availability mod-els. Due to the large number of existing bandwidth avail-ability prediction models, this paper will only present andcompare the most promising ones in more detail, focusingon their inputs, the used algorithms and outputs.

Section 5 compares the most promising bandwidth avail-ability prediction models by using as criteria the listedLTE requirements. Finally Section 6 presents the conclu-sions and provides recommendations for future work.

2. LTE BASED REQUIREMENTSA virtualized LTE system imposes different requirementson bandwidth availability prediction models. This sectiondiscusses these requirements. The identified LTE require-ments are as follows:

1. user mobilityshould be able to use history of or current mo-bility of users Knowing the location of the currentuser and the direction in which the user is movinghelps significantly in knowing how much bandwidtha user will generate when roaming within one cell.For a model to be accurate, the model needs to beable to determine the location and movement of usersat certain moments and use this input for the pre-diction of bandwidth availability in a network

2. traffic typeshould be able to use generated traffic typeand volume as input Knowing certain character-istics about the type of service being used and theamount of bandwidth that that service uses will in-crease the accuracy of the bandwidth availabilityprediction. The model should therefore be able touse information about the traffic type used by ser-vices as input for the prediction.

3. couplingprediction models for mobility and bandwidthavailability should be strongly coupled Outputof mobility prediction models is used as input to pre-dict bandwidth availability.

Bandwidth availability schemes

Figure 2. Bandwidth availability prediction modelhierarchy, based on [18]

4. individual usersshould be able to use mobility prediction ofindividual users For the optimization of the mi-gration/relocation of content, functions and contextassociated to an individual user. This is, for exam-ple, used in Distributed Mobility Management oper-ations when a user moves from one distributed mobil-ity anchor to another. The prediction model shouldbe able to support prediction for individual usersmoving through different geographical locations.

5. group usersshould be able to use mobility prediction ofuser groups This is used to define when and whereto migrate/relocate a virtualized network entity indifferent geographical locations. In order to optimizethe bandwidth availability prediction, the model shouldbe able to recognize users in a group and use the mo-bility of groups as input.

6. topologyshould be able to use different levels of predic-tion To make sure that the model can provide band-width availability prediction on the different parts ofthe LTE system, e.g., cell, S-GW or P-GW servicearea, the model should be able to use different net-work topologies as input.

3. TAXONOMY OF BANDWIDTH AVAIL-ABILITY PREDICTION MODELS

Existing bandwidth availability prediction models can beclassified into two main categories: Non-predictive schemesand predictive schemes. The non-predictive schemes donot take user mobility prediction into account whereas pre-dictive schemes do. The Predictive schemes can again beclassified into three different categories: (1) prediction us-ing current mobility factors, (2) prediction using previousmovement′s history and (3) a combination of these two[18]. Some of these schemes also use information aboutthe usage of bandwidth availability into account.

3.1 Non-predictive schemesThese schemes do not take mobility prediction into ac-count and are generally the most lightweight, simply be-cause they need less information and therefore less com-puting power to deal with the data. These schemes do,however, generally provide less accurate predictions thanpredictive schemes. An example can be found on [14],which only uses local bandwidth information and band-width information on nearby cells.

3.2 Bandwidth availability predictive schemesusing current mobility factors

These predictive schemes only use current mobility factorsof mobile users. The method MSP-CACRR (Multidimen-sional Sequential Patterns based Call Admission Controland Resource Reservation) for example, uses Spatial In-formation (What is the current location of the user) andTemporal Information (What is the time and day) [19].This information is purely based on current mobility fac-tors. Only using current information makes these schemesmore lightweight than schemes that use history factors aswell, but can provide less accurate predictions.

3.3 Bandwidth availability predictive schemesusing previous movement history

These predictive schemes do not use current mobility fac-tors, but compute their prediction mainly on history ofthe user′s mobility. Such a scheme, for example, uses Hid-den Markov Models to accurately predict user mobility onbasis of its history.

3.4 Predictive schemes using both current mo-bility factors and previous movement his-tory

As mentioned previously, these schemes take both cur-rent mobility factors as previous movement history intoaccount.

4. BANDWIDTH AVAILABILITY PREDIC-TION MODELS

Several research activities focussed on bandwidth avail-ability prediction models that are applied on older cellularsystems. These can be categorized as discussed in Sec-tion 3. This section is discussing the most promising ofthese prediction models, categorize them and give a shortoverview on how they operate.

1. Bt(Ci,Tj): The total bandwidth of cell Ci at time Tj

2. Bu(Ci,Tj): The estimated bandwidth used by usersin cell Ci at time Tj

3. Br(Ci,Tj): The bandwidth reserved by cell Ci forhandover calls at time Tj

The available bandwidth Ba in cell Ci and at time Tj isthen calculated by (as used by [17])

Ba(Ci,Tj) = Bt(Ci,Tj) −Bu(Ci,Tj) −Br(Ci,Tj) (1)

Note that for these calculations, the mentioned cell Cicould be an inter-radio coverage area, an inter-S-GW ser-vice area, inter MME pool area or platforms where thevirtualization of the LTE network entities can be realised,i.e., data centres.

The formula given in (1) calculates the available band-width and requires three points of information: The totalbandwidth in a network, the amount of bandwidth usedin that network, and the amount of bandwidth that isreserved in a network. Bandwidth availability predictionmodels generally work as follows. The total amount ofbandwidth on a certain place in the network is determinedat the design phase. The amount of data used and theamount of data reserved is determined by the models.

The following subsections give a few examples and briefoverviews for each of the categories discussed in Section3. Furthermore, it considers a few promising prediction

Current Mobile Network

Figure 3. Service Delivery Phases, based on [1]

schemes in more detail, by focusing on their required in-put, which algorithm is used to predict the available band-width and the provided output.

4.1 Non-predictive schemesRecent work has proven that although these schemes arevery lightweight, they are not very accurate. The mostpromising non-predictive scheme is the one proposed in[5]. It introduces a new resource reservation scheme anda companion borrowing scheme. At setup time, the con-nections should specify their desired amount of bandwidthand the minimum amount of bandwidth that they need toensure the adequate level of quality. When a cell (cover-age area) does not have enough available bandwidth fornew connections/sessions, it can borrow bandwidth fromexisting connections/sessions on a temporary basis. Theamount of bandwidth that is reserved in this scheme isfixed being a percentage of the total amount of supportedbandwidth by a cell.

4.1.1 Network resource pre-reservationIn [1] a bandwidth availability prediction scheme is pro-posed that uses the monitored service load to better es-timate the desired bandwidth (resources) for a UE. Theypropose a mechanism for pre-reserving multimedia contentdelivery in HWNs (Heterogeneous Wireless Networks).

This mechanism builds on the idea that the already ex-isting PCRF in a network can help in estimating the re-sources a user is going to request because it knows moreabout how much bandwidth a service will require. Cur-rently, services are delivered as can be seen in figure 3.This scheme proposes an extra overlay layer on top of thiswhich are marked by numbers 1 to 4. The first part isthe advertisement phase, where users receive informationabout services that are offered. The second phase is thesubscription phase, where users can subscribe to these ser-vices for the near future. The third phase is the resourcereservation phase, where the service providers request re-source reservation functionality of the IP core. The lastphase is the user notification, where subscribed users re-ceive information about the availability of the multimediacontent.

Note that in the third phase, the resource pre-reservationphase, is where the network receives information about fu-ture service load on the network for specific users. Thisinformation could help the network to better predict band-width usage and availability in certain nodes in the system.This scheme requires two extra connections between theservice provider and the cellular network, as can be seen

Figure 4. Network resource pre-reservation archi-tecture, copied from [1]

in figure 4. One connection between the service demandin a service provider and the demand estimation withinthe cellular network, and another connection between thesession management in the service provider and the admis-sion control in the cellular network. The first connectionwill provide the main contribution in bandwidth availabil-ity prediction, since it provides additional information onwhat the demand from certain users will be at specifictimes.

Input The proposed system will need to operate togetherwith a monitoring system. The proposed scheme will needinformation from a user regarding the services that theuser will use in the future.

Processing The system can then use this input to calcu-late how much bandwidth the user will need at a futuremoment in time, since it knows the amount of bandwidththe service requires at that specific future moment in time.

Output It can then give this information to the cellularsystem in the form of the amount of bandwidth a user willneed at a specific future moment in time.

The proposed scheme does not completely predict the band-width usage in the cellular system, but it will provide de-tailed information of the bandwidth usage of multimediaservices users that can be applied to improve the totalprediction as a whole.

4.1.2 Dynamic Resource Reservation Scheme forHMIPv6

The scheme proposed in [8] combines a path predictingalgorithm with a dynamic resource reservation with pre-resource reservation. In particular, it proposes an extraoverlay layer in the cellular network, which will use mo-bility anchor points (MAP), see figure 5. The MAPs areused for registration of users in the network for multicastconnectivity, and distribution of their packets.

Input This scheme uses information about the relativedistance between a user and the surrounding base stationsto know when a handoff needs to take place.

Path prediction The model operates as follows: the mo-bile user keeps track of the relative distance between differ-ent base stations. If this distance is less than a pre-definedthreshold, it assumes that the user is moving towards thatbase station and it can estimate when a handoff towardsthe new base station takes place. This information is then

Figure 5. MAP architecture, copied from [8]

used for resource reservation.

Dynamic Resource Reservation This model proposesa resource reservation designed for multicast. Users mustregister to a multicast group through a MAP. When theservice is delivered, the source sends packets to the MAP,which distributes them to the registered users in that area.

Output This scheme uses the information provided by theMAPs to improve the resource reservation process for usersthat are registered to use the multicast service. Becausethe MAP is aware of which users are going to use thisservice, and where these users are located in the network,it is able to estimate the bandwidth that will be needed bythe different parts of the network in order to support themulticast service. This can help improving the bandwidthavailability prediction.

4.2 Bandwidth availability predictive schemesusing current mobility factors

The scheme proposed by [11] uses the location and direc-tion of the user to estimate the next cells the user willvisit. It calculates the probability of a UE of moving to-wards a new cell depending on the direction that a UE ismoving within the coverage area of a cell. When the an-gle of direction towards a new cell is, for example, greaterthan 90 degrees, this probability will be almost zero. Inaddition to this, the algorithm takes also into account thetime that it is required by a UE to reach the new cell.

The scheme proposed by [10] operates in a similar wayas the scheme proposed in [11]. It takes, however, intoaccount that the number of channels that can be reservedin a given cell has a close relationship to the number ofusers located in neighboring cells via an influence curve.This scheme assumes that the number of the to reservecalls in cell j for all calls in cell i is proportional to thisinfluence curve. The scheme also differentiates betweenhigh- and low-speed users and adjusts their influence onneighboring cells accordingly.

The scheme proposed by [12] mostly operates in a simi-lar way, but it takes topology and road information intoaccount. The Base Station (BS) determines all possiblepaths a user can travel in time. If it is, for example, trav-elling on a road that is going to cross an intersection, thisinformation is used to determine with what probability theuser will go straight, right or left. This is accomplished byusing information about how many cars are moving in acertain direction on that intersection. It then builds apath tree for this, with the according probabilities. Theseprobabilities then determine the amount of bandwidth tobe reserved in neighboring cells.

The scheme provided in [13] uses the shadow cluster con-

cept for predictive resource allocation. BSs inform theirneighboring BSs about bandwidth requirements, positionand movement of users at call setup. Based on this in-formation, the BSs calculate and predict future demandsand use them to predict the required bandwidth. In ad-dition to this, the scheme dynamically adapts the amountof reserved bandwidth pool based on the current networkconditions in order to make the predictions more accurate.

The scheme proposed in [15] proposes a Smooth RandomMobility model that is enhanced with a technique basedon cell stay time and the direction and speed changes ofUEs. An equation calculates the probability that a userwill move to a certain neighboring cell and does this forall cells. If the probability that a user moves to a cer-tain neighboring cell is high enough, then the scheme willreserve bandwidth in the new neighboring cell for this spe-cific user.

4.3 Bandwidth availability predictive schemesusing previous movement history

A scheme proposed by [3], named PR-CAT4 (ProposedReservation-CAT4) is a scheme that consists of mainlytwo stages: Estimation of the amount of reserved band-width and the actual bandwidth reservation. The basestation of each cell will reserve fractional bandwidth onits neighboring cells. It does this by maintaining a Mobil-ity Pattern Profile (MPP) to estimate user mobility. ThisMPP contains 4 fields: CurrBSID, PrevBSID, NextBSID,NextCount which represent the current, previous and nextcell ID, and the number of times the user has moved fromthe current cell to another cell, respectively. The amountof bandwidth that will be reserved is then determined bya threshold of mobility probability, which simple deter-mines that if the probability of going to a cell is higherthan the threshold, more bandwidth will be reserved thanif it is below the threshold. To prevent the MPP of get-ting too large, probabilities below a certain threshold arediscarded.

The scheme proposed by [23] uses a mobility predictionscheme that is based on a character-based version of theZiv-Lempel algorithm. It maintains a sequence of eventsincluding new call, handoffs and terminations of calls. Itthen calculates the probability that a user will be locatedin a cell at a certain time-interval based on these events.The cells that are the most likely to be visited on a certaintime, then form a most likely cell-time (MLCT). Usingthese probabilities, the scheme then calculates the requiredbandwidth to be reserved in a cell as a sum of all usersand probabilities of being located in that cell. This schemeassumes relatively much history information, such as thefrequency that certain paths are used by users.

A variant on the previous scheme is the use of the so-calledLeZi-Update scheme. This scheme is proposed by [13] andis mainly based on a Markov model, that uses the historyof how frequent a user follows travelling paths. In thisway, the movement prediction of a user will depend onthis history. The difference with the scheme given in [23]is that this LeZi-update variant also takes into account thesituation that the user is moving within a cell but it is notactively connected.

The scheme proposed in [22] combines location manage-ment and QoS provisioning for a better mobility and band-width prediction. The scheme uses mobility predictionschemes, such as prediction by partial matching (PPM)which differs from earlier used prediction techniques sinceit combines information from Out-of- and In-session mo-bility information in order to provide input for location

Figure 6. Bandwidth availability prediction modelflow

management and QoS provisioning.

The scheme proposed in [4] also uses mobility informationto predict bandwidth usage. Each BS caches informationabout the user (Time of the event, the previous cell, thenext cell, and the time the user spent in this BS). The BSthat is controlling a cell keeps track of each active UE in itscell via the mobile’s extant sojourn time. The amount ofreserved bandwidth is then calculated by calculating theprobability of a handoff towards a cell and by reservingbandwidth on this new cell.

The scheme proposed by [7] combines the distributed Hid-den Markov Chains (HMC) for local user movement andthe Mobile Resource Reservation Protocol (MRSVP), whichis a scheme proposed by [21] to make advance reservationsfor mobile hosts using active flows. Every cell has its ownHMC predictor which estimates future neighboring cells(which cells will be used by which users). Every cell pre-dicts user movement and forwards passive reservation mes-sages to predicted neighboring cells. The main differencefrom other schemes is that this scheme does not considerwhich transmission technology and which mobility modelsare used.

4.3.1 PCAC-RR (Predictive call admission controland resource reservation)

The scheme proposed by [17] works as follows. It uses in-puts as discussed below from the UEs or a cellular systemand uses a mobile data mining technique to acquire theuseful information. A resource reservation scheme thenuses this information to do the actual bandwidth reserva-tion in the cells.

Input This scheme collects data of the movement of theuser in the mobile handset. The mobile host(MH), whichis the UE, maintains local mobility profiles which containthe paths that the user has travelled. This informationcontains the following information:

• Spatial information: The location of the user

• Temporal information: The time and day at whichthe data is collected, where the day is divided in sev-eral time intervals.

At the same time, all BSs will collect this data from all theusers (UEs) that are located in their cells and maintainglobal mobility profiles. With these global profiles, thesame calculations can be made whenever the local profileassociated with a user proves to be insufficient.

Local mobility profiles These local mobility profiles aremaintained by every mobile user, i.e., UE. They contain aset of paths that are most likely visited by that UE. Thescheme operates as follows: Every time a UE enters a newcell, it saves the time at which the visit was started endended, as well as the ID of that particular cell, generatinga table similar to Table 1.

From Table 1, paths are generated by taking a timing in-terval and observe in which cell the user was located atthat moment in time. This is done by using the following

Table 1. Data saved by the UE, based on [17]Visited BS ID Visit start time Vist end time

BS1 1:00 1:01BS2 1:01 1:03BS3 1:03 1:07

Table 2. UE local mobility profile, based on [17]Time interval Visited paths Associated

Ti ProbabilityLP (T1) Path1 Passoc(pathn) = 1

Path2 ...... ... ...

LP (TN ) Pathi ...

equations (from [17]):

P (pathn,ml|Tj) =Num(pathn, Tj ,ml)∑Np(ml)

i=1 Num(pathi, Tj ,ml)(2)

P (pathn,ml) =Num(pathn, Tj ,ml)∑NT

j=1

∑Np(ml)i=1 Num(pathi, Tj ,ml)

(3)

Where: P (pathn,ml) is the probability that the mobileml follows pathn

P (pathn,ml|Tj) is the probability of the mobile ml to gothrough pathn the path given the interval Tj

Num(pathn, Tj ,ml) is the number of times mobile ml togo through pathn during the time interval Tj

Np(ml) is the number of stored paths for mobile ml

This information is used to generate the local mobility pro-files. For each of the generated path in that time interval,the probability is compared to a certain threshold LTH1

(say 0.8). If the probability is higher than the thresh-old, then the path is considered frequent and is saved asPassoc(pathn) = 1. If the probability is lower than thethreshold, the overall path probability P (pathn) is com-pared to another threshold LTH2 (say 0.6). If it is higherthan this threshold, it is saved as Passoc(pathn) = 1. Oth-erwise, it will be saved as P (pathn)|Ti. This will providea local profile like the one given in Table 2.

Predictive resource reservation Resource reservationis then done in the following way: for a call in cell Ci

at time Tj , the model looks at LP (Tj) in the local mo-bility profile. If there is a frequent path, then calculatethe handoff time (Th) for every cell Ck in that path. Theamount of required bandwidth Breq is calculated as:

Breq = Bactual ∗ Pa(pathn) (4)

If the Breq is less than the amount of bandwidth availablein that cell, it is then simply used as input that will bethe amount of bandwidth to reserve. If it the amount ofavailable bandwidth is not enough and the call belongs toClass-I, then this call can borrow some amount of band-width from the ongoing Class-II calls, which is then regis-tered as Bborrow. If the call belongs to Class-II then theamount of bandwidth that can be reserved is equal to theavailable bandwidth plus the borrowed amount.

Output The calculations described above give a per cellamount of bandwidth that is estimated to be used. Thisinformation is gathered at the given cell and translatedinto the amount of bandwidth to be reserved.

4.4 Bandwidth availability predictive schemesusing both current mobility factors andprevious movement history

The PCAC-RR scheme proposed by [18] focuses on theproblem to effectively record and analyze previous behav-iors of users. This scheme is an enhancement of the schemeproposed [17], which maintains two types of user mobilityprofiles: local and global. The local mobility profile isgenerated for each individual user on the UE itself and isbased on the mobility of the user by saving the start andend time of a visit in any cell. The global mobility pro-file is generated by each base station. The BS gathers allmobility data of the users passing through the cell. Thepredictive resource reservation scheme uses these proba-bilities to calculate the reserved bandwidth. This occursbased on the local mobility profiles. When these prove in-sufficient, for example, no frequent path is estimated, theBS will use its global profiles in the required computations.

The scheme from [2] presents a novel location predictiontechnique based on Dynamic MobileSPADE which takesinto account the different behaviors of mobile users in weekdays and weekend days. It saves the Time id and thecells visited at that time. It is an Apriori-based sequentialpattern algorithm that mainly uses historic data to predictthe location of users at certain times.

The scheme provided by [19] is primarily based on the Pre-fixSpan scheme from [9] with a few slight changes. Mo-bilePrefixSpan is a data mining technique that analyzesthe information collected by the UEs to extract movementpatterns. The mobility models only contain Spatial andTemporal information. This algorithm then calculates thefrequently paths that are most likely travelled by usersfrom cell-to-cell and uses this to reserve bandwidth forthose users in these cells.

4.4.1 PAC-WHNA scheme proposed by [20] called PAC-WHN (Predictiveadmission control for Wireless heterogeneous networks) isa variant on the PCAC-RR scheme described in [18]. Ituses the data collected by the UE as input for the resourcereservation and uses this mobility information to reserveresources in the base stations. These phases are consideredin more detail:

Input: Network Architecture The proposed HWNs in-clude WLANs and cellular networks working on an ALL-IP wireless and mobile networks. The core IP networkswill serve as a backbone to which the WLANs and cel-lular systems are connected. As can be seen in Figure7, the architecture consists of 4 basic components: BaseStations (the fixed communication points for cellular net-works), Access Points (the fixed communication points forWLANs), Mobile Hosts (the users in the network) and theIP Core Network. A user is connected to the network viaone BS or one AP. When the user is moving, handoffs canoccur horizontally (from one AP to another AP, or one BSto another BS), or vertically (from a WLAN connectionto the cellular network and vice versa).

Input This scheme uses the enhanced MobilePrefixSpantechnique as developed in [19] with a slide modification.In the improved version used by this scheme, three typesof user information are used:

• Spatial information: The location of the user

• Temporal information: The time and day at whichthe data is collected where the day is divided in sev-eral time intervals.

Figure 7. Architecture of the proposed PAC-WHNscheme, copied from [20]

Table 3. Data collected by the MH, copied from[20]

Visited Visit Visit Is this on Type ofBS/AP Start End Weekend Service

Time TimeL1 V ST1 V ET1 W1 ToS1

L2 V ST2 V ET2 W2 ToS2

... ... ... ... ...Ln V STn V ETn Wn ToSn

• Usage information: The type of service that isused (e.g. Class-I or Class-II)

This means that the model needs this information as in-put and assumes that the mobile hosts collect this data.This is done in a table similar to the one shown in Ta-ble 3. Here the Li represents the ID of the cell. V STi

is the time stamp when this MH entered Li. V ETi isthe time stamp when this MH exited Li. Wi describeswhether this information was gathered in weekends (thisscheme assumes the behaviour of users is significantly dif-ferent during the weekends). ToSi is the service providedto this MH (e.g. Class-I, Class-II or Idle). With this in-formation, the mobility paths are generated by the MH,which contains information of the estimated time at whicha user will move from one location to another.

Resource reservation The proposed resource reserva-tion technique uses the information provided by the Mo-bilePrefixSpan to predict the desired amount of resourcesin a certain cell. For each cell the proposed techniquecan estimate for the users located in the cell what willbe the next cell that they will going to be at a certainfuture moment in time. The resource reservation proce-dures differentiate between Class-I and Class-II services.The resource reservation for Class-I service types shouldsupport a guaranteed service continuity without interrup-tions when users are moving. Whereas this is not requiredfor Class-II services. Due to this differentiation in servicetype, a slight alteration to the Equation 1 has to be made:

Ra(Li, Tj): the available bandwidth of location Li at timeTj

Rt(Li, Tj): the total resources of location Li at time Tj

Ru(Li, Tj): the estimated resources used by users at loca-

tion Li at time Tj

Rh1(Li, Tj): the resources reserved by location Li for Class-I handoff calls at time Tj

Rh2(Li, Tj): the resources reserved by location Li for Class-2 handoff calls at time Tj

The available bandwidth is then calculated by the equa-tion from 5

Ra(Li, Tj) = Rt(Li, Tj)

−Ru(Li, Tj)−Rh1(Li, Tj)−Rh2(Li, Tj) (5)

In this scheme, Rh2 can be used to handle handoff callsfor both Class-I and Class-II services while Rh1 can onlybe used for Class-I handoff calls, so that Class-I handoffsalways have priority, providing the desired Quality of Ser-vice.

For every handoff, the scheme does the following by us-ing the sequential patterns stored in the MH: for everyestimated path (frequent sequence), estimate the handofftime (Tk) for a user and check if there is enough band-width available at that time in the cell the user is movingwithin (this can be calculated by the formula (5). If thereis enough bandwidth available, the scheme reserves a re-quired amount of bandwidth based on a portion of theactual required bandwidth according to when the MH willreach that cell.

If the local profile does not provide a frequent sequence(path), the BS/AP will use its global profile to accomplishthe same result. In addition to this, the BS/AP will alsomonitor the load on the network and force handoffs toother AP/BS if the load is expected to increase.

Output After performing these calculations, the mobilehosts will have a table that contains the probabilities ofreaching a certain cell. The scheme can then inform thenew cell about the estimated probabilities of users thatwill be going to enter the cell. The new cell collects thisdata and adjust its available bandwidth accordingly.

4.4.2 SmartMobiMineThe scheme proposed by [16] proposes a new Mobile datamining technique which can be used by bandwidth avail-ability prediction models. This scheme operates in a sim-ilar way as the one described in the previous section, seeFigure 6, but with a few alterations.

InputThis model needs the following information in order towork properly:

• Spatial information: the location of the MH

• Temporal information: the time and day infor-mation is collected

• Usage information: the type of services that havebeen used (e.g. Class-I or Class-II)

• Social information: the type of visited locations(e.g. location of friends, entertainment locations,etc.) This will help in personalizing these servicesfor groups of users.

The MH collects and maintains this information into atable, in a similar way as the techniques described in theprevious subsections. This information is then providedto the SmartMobiMine data mining technique.

SmartMobiMine In a mobile environment a user canmove fast. Therefore, the data mining techniques used

Figure 8. Inputs and ouputs for SmartMobiMinetechniques, copied from [16]

in such environments need to operate dynamically and befast. Most of the current data mining techniques cannotwork effectively in such environments. SmartMobiMine isdesigned to provide this by using three algorithms:

1. MobileCluster algorithm: to cluster mobile usersinto groups based on their performance and socialinformation

2. MobileAssociations algorithm: to generate theassociation rules to predict future locations of MHsand the resources they require in future communica-tions sessions

3. Dynamic-MobileSPADE: to generate all discov-ered frequent paths that meet a used-defined sup-port.

The SmartMobiMine technique is still in development, butdue to the detailed information it uses, it is quite promis-ing. A resource reservation technique will need to be de-signed in order to use this detailed information properly.It is expected that SmartMobiMine will be used to predictfuture locations of mobile users with high accuracy.

Output The output of this scheme uses the inputs fromthe mobile hosts to calculate the user frequent paths anddoes not need any resource reservation. It is mainly astorage of paths and their probabilities that can be usedby resource reservation schemes.

5. COMPARING BANDWIDTH AVAILABIL-ITY PREDICTION MODELS

This section provides a comparison between several band-width availability prediction models described in Section4 by using the requirements imposed by a virtualised LTEsystem described in Section 2.

5.1 Network resource pre-reservationUser Mobility This model, see Section 4.1.1, has no in-put for user mobility, not satisfying this requirement.

Traffic Type This model uses information about the mul-timedia services that are going to be used to predict theamount of used bandwidth, satisfying this requirement formultimedia services.

Coupling The model does not use mobility, therefore notsatisfying this requirement as well.

Individual users This model uses the information as-sociated with individual users to predict the amount ofbandwidth. This requirement is therefore satisfied.

Group Users This model has no specific support forgroups of users, meaning that this requirement is not sat-isfied.

Topology The scope of this model is only to deliver theamount of data a user will use at a certain moment in timefor multimedia services and therefore is not restricted toany topology. This means that this model can work withall types of topologies.

5.2 Dynamic resource reservation scheme forHMIPv6

User Mobility This model, see Section 4.1.2, maintainsonly very little information about the users movement inthat it knows a bit more about when a handoff is goingto take place for the next cell. This model therefore onlypartly satisfies this requirement.

Traffic Type This model uses information about the mul-timedia services that are going to be used to predict theamount of used bandwidth, satisfying this requirement formultimedia services.

Coupling Since this model only estimates when hand-offs are going to take place, and does not use any othermobility prediction, this scheme does not satisfy this re-quirement.

Individual users This model uses the information as-sociated with individual users to predict the amount ofbandwidth. This requirement is therefore satisfied.

Group Users This model has no specific support forgroups of users, meaning that this requirement is not sat-isfied.

Topology The scope of this model is only to deliver theamount of data a user will use at a certain moment intime for multimedia services. This model requires a newoverlay layer with MAPs to distribute multicast services,which can be deployed in any network. This model satisfiesthis requirement.

5.3 PCAC-RRUser Mobility As can be seen in section 4.3.1, this modeluses information about user movement as input. Thismodel satisfies the requirement.

Traffic Type This model does not use any informationabout the traffic type a user has used. Therefore, thismodel does not satisfy this requirement.

Coupling This model combines user mobility to estimatethe available bandwidth. This model therefore satisfiesthis requirement.

Individual users This model uses information about in-dividual users via the local profiles and uses this inputfor the calculations of the resource reservation. Therefore,this model satisfies this requirement.

Group Users The model only uses the local profiles ofthe mobile hosts. If however, the profiles would representa group of users, the model would be able to deal with thisat well. This would require an extra field to save in thelocal profile: the amount of users in that profile.

Topology The topology this model uses is not defined,and mainly focuses on the information that each cell has

over their neighboring cells. This model can be used inany part of the LTE system, satisfying this requirement.

5.4 PAC-WHNUser Mobility As stated before, the input for this scheme,see Section 4.4.1, requires three types of information, whichare used to predict the mobility and bandwidth usage ofthe user. Two of the inputs are based on mobility andmobility history. This requirement is therefore satisfied.

Traffic Type The other input is based on the type oftraffic the user was and is using. This model satisfies thisrequirement.

Coupling This model combines the user mobility andbandwidth usage to accurately estimate the bandwidthavailability. Therefore, this model also satisfies this re-quirement.

Individual users In this model, the information associ-ated with a user is provided via the mobile host. Theseare individual users, and further calculations are done forthese individual users as well. Only in the resource reser-vation phase the profiles are combined and used for theprediction of the bandwidth usage and reservation. Thisrequirement is therefore satisfied as well.

Group Users As said above, the model is designed forprediction for individual users. The local profiles of usersare used as input for the resource reservation and, there-fore, groups of users are not supported by this model. Ifhowever, the input given by the local profile would be fora group of users, the model would be able to use this asinput for its calculations. This would mean an additionalfield in the profile for saving the amount of users presentedby that profile.

Topology For every user, the model saves the local pro-files. With these profiles, the model calculates the proba-bility that a user will be in a certain cell at a certain time.The model therefore requires to know the topology of thenetwork. The topology however is not specified. The men-tioned cells could be also e.g., inter-radio coverage areasor inter-S-GW service areas. The model therefore satisfiesthis requirement as well.

5.5 SmartMobiMineAlthough this model, see Section 4.4.2, mainly focuses onthe data mining technique, the model does use input vari-ables which can be used for bandwidth availability predic-tion.

User Mobility This model uses temporal and spatialinformation as input, and therefore satisfies this require-ment.

Traffic Type This model also uses usage information forprediction, therefore satisfying this requirement.

Coupling This model uses mobility information as input,satisfying this requirement.

Individual users The model is designed to be able to pre-dict bandwidth availability for individual users, satisfyingthis requirement.

Group Users Special algorithms are being designed tocope with groups of users in this model. Although thissolution was not yet described, it is promising and theproposed scheme will satisfy this requirement.

Topology This model does not take any topology intoaccount. This is, however, currently being designed, whichmeans this requirement will probably be satisfied in thefuture.

Table 4. Comparison of models in light of the LTEbased Requirements

Scheme 1 2 3 4 5 6Network resourcepre-reservation [1] n y n y n yDynamic resource reservationscheme for HMIPv6 [8] p y n y n yPCAC-RR [17] y n y y p yPAC-WHN [20] y p y y p ySmartMobiMine [16] y p y y n* n*

*This scheme is still in development and therefore, thesetwo requirements are not yet met, but it is currently indevelopment.

5.6 ConclusionCombining the information from the subsections above, weprovide an overview of the models and the requirements.This will be done by grading the models on these require-ments using: yes (y), no (n), or partly (p). The result canbe found in table 4.

6. CONCLUSION AND FUTURE WORKThis paper has given an overview and summary of existingbandwidth availability prediction models for cellular net-works. The most promising prediction models have beenstudied in more detail and compared to the requirementsimposed by virtualized LTE systems.

The models provided by [1], [8], [17], [20] and [16] are themost promising prediction models in their categories. Allof these models are not able to satisfy all the LTE basedrequirements.

The models described in [1] and [8] are primarily designedto improve the resource reservation by letting users reg-ister to multicast services in advance and using this in-formation to deduce which users will use this service andthe amount of bandwidth this service will require. Thesemodels do however, lack the ability to take user mobil-ity into account, but can, with modifications, be used byother bandwidth availability prediction models to improvetheir prediction.

The models described in [17] and [20] are able to take usermobility into account in order to improve their prediction.However, these models are not able to take the bandwidthusage of a service into account. Only the type of service(Class-I or Class-II) is used to improve the prediction.

The scheme described in [16] has the most potential tosatisfy the imposed virtualized LTE requirements. In par-ticular, this model can support prediction for individualand groups of users and uses more information about theused services into account. The scheme is however still indevelopment and is currently limited to the use of a datamining technique.

7. REFERENCES[1] A. Al-Hezmi and M. Corici. Network Resources

Pre-Reservation for Multimedia Content Delivery inHeterogeneous Wireless Networks. 11th EuropeanWireless Conference 2011 - Sustainable WirelessTechnologies (European Wireless), pages 515–521,2011.

[2] J. Bradley and S. Rashad. Emerging Trends inComputing, Informatics, Systems Sciences, andEngineering. 151, 2013.

[3] C. Choi, M. Kim, and S. Kim. Call admissioncontrol using the moving pattern of mobile user for

mobile multimedia networks. Proceedings LCN 2002.27th Annual IEEE Conference on Local ComputerNetworks, 2002.

[4] S. Choi and K. Shin. Predictive and adaptivebandwidth reservation for hand-offs in QoS-sensitivecellular networks. ACM SIGCOMM ComputerCommunication Review, pages 155–166, 1998.

[5] M. Ei-Kadi. Rate-based borrowing scheme for QoSprovisioning in multimedia wireless networks. IEEETransactions on Parallel and Distributed Systems(TPDS), 13(2):156–166, 2002.

[6] European FP7 Integrated Project. ”Mobile CloudNetworking”, FP7-ICT- 2011-8, No. 318109, startedin November 2012.

[7] P. Fazio and S. Marano. Mobility prediction andresource reservation in cellular networks withdistributed Markov chains. 2012 8th InternationalWireless Communications and Mobile ComputingConference (IWCMC), pages 878–882, Aug. 2012.

[8] H. Ferng and W. Kao. A dynamic resourcereservation scheme designed for improving multicastprotocols in HMIPv6-based networks. IEEE 63rdVehicular Technology Conference, 2006. VTC2006-Spring. , 00(c):961–965, 2006.

[9] J. Han, J. Pei, and B. Mortazavi-Asl. PrefixSpan:Mining sequential patterns efficiently byprefix-projected pattern growth. Proceedings of the. . . , 2001.

[10] J. Hou and Y. Fang. Mobility-based call admissioncontrol schemes for wireless mobile networks.Wireless Communications and Mobile Computing,1(3):269–282, July 2001.

[11] M. Islam, M. Murshed, and L. Dooley. New mobilitybased call admission control with on-demandborrowing scheme for QOS provisioning. ProceedingsITCC 2003. International Conference onInformation Technology: Coding and Computing,pages 263–267, 2003.

[12] D.-S. Lee and Y.-H. Hsueh. Bandwidth-ReservationScheme Based on Road Information forNext-Generation Cellular Networks. IEEETransactions on Vehicular Technology,53(1):243–252, Jan. 2004.

[13] V. Leung. A framework of combining mobilitymanagement and connection admission control inwireless cellular networks. ICC 2001. IEEEInternational Conference on Communications.Conference Record (Cat. No.01CH37240),7:2286–2290, 2001.

[14] C. Oliveira, J. Kim, and T. Suda. An adaptivebandwidth reservation scheme for high-speedmultimedia wireless networks. Selected Areas in . . . ,16(6):858–874, 1998.

[15] F. Rango, P. Fazio, and S. Marano. MobilityPrediction and Resources Reservation in WLANNetworks Under a 2D Mobility Model. IEEEVehicular Technology Conference, pages 1–5, Sept.2006.

[16] S. Rashad and J. Bradley. SmartMobiMine: Smartmobile data mining techniques to support 4G mobilenetworks. 2011 IEEE Consumer Communicationsand Networking Conference (CCNC), pages703–704, Jan. 2011.

[17] S. Rashad, M. Kantardzic, and A. Kumar. A datamining approach for call admission control andresource reservation in wireless mobile networks.Advances in Data Mining, pages 134–143, 2005.

[18] S. Rashad, M. Kantardzic, and A. Kumar. Usermobility oriented predictive call admission controland resource reservation for next-generation mobilenetworks. Journal of Parallel and DistributedComputing, 66(7):971–988, July 2006.

[19] S. Rashad, M. Kantardzic, and A. Kumar.MSP-CACRR: Multidimensional SequentialPatterns Based Call Admission Control andResource Reservation for Next-Generation WirelessCellular Networks. 2007 IEEE Symposium onComputational Intelligence and Data Mining,(Cidm):552–559, 2007.

[20] S. Rashad, M. Kantardzic, and A. Kumar.PAC-WHN: Predictive Admission Control forWireless Heterogeneous Networks. 2007 IEEESymposium on Computers and Communications,pages 139–144, July 2007.

[21] A. Talukdar, B. Badrinath, and A. Acharya.MRSVP: A resource reservation protocol for anintegrated services network with mobile hosts.Wireless Networks, pages 5–19, 2001.

[22] V. Wong and V. Leung. Performance enhancementof combining QoS provisioning and locationmanagement in wireless cellular networks. IEEETransactions on Wireless Communications,4(3):943–953, May 2005.

[23] F. Yu and V. Leung. Mobility-based predictive calladmission control and bandwidth reservation inwireless cellular networks. Computer Networks,38(5):577–589, Apr. 2002.