RESEARCH Open Access QoS-aware resource management for LTE

Góra EURASIP Journal on Wireless Communications and Networking 2014, 2014:178http://jwcn.eurasipjournals.com/content/2014/1/178

RESEARCH Open Access

QoS-aware resource management forLTE-Advanced relay-enhanced networkJacek Góra1,2

Abstract

Relaying is one of the major innovative concepts proposed in the recent years for cellular radio communicationsystems. It is a perfect solution for dealing with the issue of high variability of performance in cellular networks. Bycoordinated deployment at cell-edge or in shadowed areas, relay nodes can extend network coverage and increasethe low end-user performance. Considering the advantages, relaying is recently being included in the standards of thefourth generation systems such as the LTE-Advanced and the WiMAX. However, one major problem of relaying is stillto be resolved. Specifically, there are no concrete concepts for quality-of-service provisioning for relayedtransmissions. This paper investigates the case of packet delivery times over multi-hop links in relay-enhancednetworks. The discussion is specifically based on relaying implementation in the LTE-Advanced system. Thequality-of-service satisfaction and its fairness for the base station and relay-node-connected users are analyzedin the framework of the utility theory. For the purpose of this analysis, utility functions are proposed forreal-time traffic with minimum data rate and/or maximum packet delivery time requirements. Furthermore,several optimization concepts are proposed for managing multi-hop transmissions in a quality-of-service awaremanner. The included analysis based on the LTE-Advanced system level simulations shows that the proposedoptimizations have the potential to improve the overall quality-of-service satisfaction in a relay-enhancedsystem.

Keywords: LTE-Advanced; Relaying; Radio resource management; Utility theory; Quality-of-service; Delay

1 IntroductionIn the year 2008, the International TelecommunicationUnion, Radiocommunication Sector (ITU-R) issued theM.2134 report [1] specifying requirements for the nextgeneration of radio communication systems, the so-calledInternational Mobile Telecommunication-Advanced (IMT-A). Specification of those requirements started a stillongoing process of developing new solutions extendingcapabilities of the existing radio communication systems.The research and development process leads to the defin-ition of two major fourth generation (4G) systems, i.e., theLong Term Evolution-Advanced (LTE-A) [2,3] and theWorldwide Interoperability for Microwave Access (WiMAX)Release 2 [4,5].Both the LTE-A and the WiMAX include a similar set

of techniques to meet the IMT-A requirements [5-7].

Correspondence: [email protected] and Innovation Department, Nokia Networks, Pl. Gen. J. Bema 2,50-265 Wroclaw, Poland2Faculty of Electronics and Communication, Poznan University ofTechnology, Ul. Piotrowo 3A, 60-965 Poznań, Poland

© 2014 Góra; licensee Springer. This is an openLicense (http://creativecommons.org/licenses/bmedium, provided the original work is properly

The most significant building blocks considered are thefollowing:

� Advanced multi-antenna techniques (multiple-inputmultiple-output, MIMO) [8],

� Bandwidth extension in the form of, e.g., carrieraggregation (CA) [9],

� Heterogeneous networks (HetNets) [10], and� Improved interference mitigation techniques

including interference avoidance [11] andcoordinated multi-point (CoMP) transmissionschemes [12].

One of the novel techniques proposed for the two 4Gsystems is relaying (in the above listing classified as partof the HetNet concept). The baseline of this technologyis the introduction of a new type of access points, relaynodes (RNs), capable of dynamic setting-up its ownbackhaul (BH) link connection over a common radiointerface, i.e., the same that is used for serving users (e.g.,

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the LTE-A). Donor of the BH link connectivity is a stand-alone access point (donor node (DN)), or in other moreadvanced configurations, already operating, RN (i.e., thedonor RN (DRN)). The main benefits of relaying envi-sioned for cellular systems are the following:

� Accuracy in coverage provisioning - RNs, as lowpower nodes, can be deployed exactly at the locationwhere network coverage is required (includingindoor or strongly shadowed areas) [13,14]. RNsspecifically can be also deployed in locations wherewireline BH provision is not possible (e.g., in publictransport vehicles [15]).

� Low cost - RNs are commonly envisioned as lowpower and simple devices, in addition to notrequiring fixed wireline BH connection, this enablesfor network operator savings in both capacity andoperational expenditures [16].

� Flexibility of use - possibility to provide rapid and/orshort-term deployments of network infrastructure(e.g., for mass events or for disaster/networkmalfunction recovery) without earlier planning orinvestments [17].

The basic application scenario for relaying (as specifiedby the 3rd Generation Partnership Project (3GPP) forum[18]) is coverage extension. The coverage extension sce-nario assumes that the main purpose of RNs in a cellularnetwork is to provide additional system coverage and toenhance connection quality for the macro cell-edgeusers or the users located in macro coverage holes. Illus-tration of the relaying coverage extension scenario ispresented in Figure 1.The simplest application of relaying for coverage ex-

tension is deployment of RNs at the edge of a macro-cellcoverage [19,20]. In such a case, the RNs initiate theirown cells providing improved connection conditions forthe nearby users (access, AC, link connectivity) and es-tablish BH link connection to the overlaying macro basestation (BS). This is the so-called two-hop relaying top-ology [21] (see Figure 2).

Figure 1 Relaying coverage extension scenario.

In a more advanced implementation, RNs may also estab-lish BH link connection to other RNs. The topology is thencalled the multi-hop relaying [22] (see Figure 2). In generalcase, the multi-hop relaying topology may have the struc-ture of a tree or a mesh. From an implementation perspec-tive, however, the tree topology is generally preferred [23].On the current development stage, the relaying tech-

nique is considered to provide coverage extension onthe basic accessibility level. The LTE-A system specifica-tion does not provide yet any dedicated mechanisms forexplicit capacity enhancement or quality-of-service (QoS)management. This is often pointed out as the main short-coming of the existing relaying solutions [21,24].The most relevant problem of the relaying technique

regarding QoS provisioning is the introduction of add-itional delays to the packet delivery time in the radiointerface. The additional delays are related to the multi-hop transmission and the RN signal processing times ateach hop. Furthermore, the basic LTE-A relaying imple-mentation assumes time domain multiplexing (TDM) ofthe RN BH and AC links (i.e., the in-band operationscheme [3,22,25]), which even further increases the end-to-end transmission time.This paper investigates the QoS provisioning problem

for the RN-connected users. The main focus is put on thepacket delivery time issue, but the requirement of mini-mum data rate is also considered. Firstly, in Section 2, theQoS-provisioning problem for relayed transmissions isformulated. In Section 3, the weight of the problem is ana-lyzed in the context of the LTE-A standard specificationof relaying. This includes analysis of the available relaynode configurations with respect to the impact they haveon the end-user perceived packet delivery times. Next, inSection 4, resource management schemes based on theutility theory are proposed. The purpose of the proposedschemes is to optimize the resource allocation in arelay-enhanced network (REN) so to improve the gen-eral QoS satisfaction for all users. Efficiency of the pro-posed solutions is verified via LTE-A system-levelsimulations described in Section 5 of this paper. Finally,the work is concluded in Section 6.

Figure 2 Two-hop and multi-hop relaying topologies.


2 Problem formulationQoS provisioning for real-time traffic involves two ele-ments [26]:

� Satisfaction of the minimum required data rate (i.e.,the guaranteed bit-rate (GBR)), and

� Satisfaction of the maximum packet delivery time (i.e.,the packet delay budget (PDB)).

Listing of the 3GPP standardized QoS classes with in-dication of the corresponding GBR and PDB require-ments is depicted in Table 1.To provide QoS satisfaction for a real-time traffic,

both the GBR and PDB requirements need to be satis-fied. If the GBR requirement is not met, it is not pos-sible to guarantee the packet delivery times for all datapackets. In such a case, the scheduling process in un-stable [27], i.e., new data packets are created by thesource node(s) faster than they are delivered to the tar-get node(s).On the other hand, even if the GBR requirement is satis-

fied, the PDB requirement cannot be assumed to be auto-matically satisfied [27]. It can be generally considered that

Table 1 Standardized QoS classes [26]

QoSclass

Priority Bit raterequirement

PDBa

(ms)Packeterror rate

Service example

1 2 GBR 100 10−2 Live voice streaming

2 4 150 10−3 Live video streaming

3 3 50 10−3 Real-time gaming

4 5 300 10−6 Buffered videostreaming

5 1 Non-GBR 100 10−6 IMS signalling

6 6 300 10−6 Web traffic forprivileged users

7 7 100 10−3 Interactive gaming

8 8 300 10−6 Web traffic forstandard users

9 9 Elastic trafficaIncluding on average 20 ms of delay in the core network.

the packet scheduling algorithm, if non-optimally imple-mented, may make some data packets wait in queue lon-ger than allowed by the PDB requirement while insteadscheduling for transmission packets with longer availabletime to drop.In the case of relayed connections, the same rules apply.

The task of the QoS satisfaction is, however, additionallycomplicated by the multi-hop nature of the transmissionprocess.First of all, in case of an H-hop connection, each data

packed is send over H component radio links. Thus, it is Htimes queued in buffers, processed for link adaptation, andtransmitted over the radio interface. At each transmission,hop errors might be introduced, which require additionalretransmissions and may cause further delays in the packetdelivery. Considering an H-hop connection, the end-to-enddelivery time (te2e) for a data packet can be generally esti-mated as the sum of the number of times required to per-form the three aforementioned operations at each of the Htransmission hops, i.e., as follows:

te2e ¼Xh¼1

H

tp;h þ tq;h þ tt;h� � ð1Þ

where tp,h is the packet processing time, tq,h is the packetqueuing time, and tt,h is the packet transmission timeover a single hop (including retransmissions, if any). Sub-script h indicates the number of a hop in the H-hop con-nection chain.Based on Equation (1), the first estimation of the

packet delivery time over an H-hop connection is that itis on average H times higher than the time expected fora single-hop connection. However, this simple estima-tion is not true as transmissions on the component linksof a relayed connection are not independent.The end-to-end packet transmission times over multi-

hop links are also impacted by the RN buffer capacityand its fill level at each of the relaying hops. This mightnot be critical nor even noticeable if the system load islow (i.e., RN buffers are never fully loaded). However, asindicated in the work of Vitiello et al. [28], if the system


load is high, RN buffers might get congested and be-come bottlenecks for multi-hop transmissions.As shown in the author’s earlier work [29], the im-

pact of limited capacity of the RN buffers is especiallynoticeable if capacities of the RN BH and AC links areimproperly balanced, e.g., as a result of sub-optimal re-source allocation. If capacity of the RN BH link islower than capacity of the RN AC link, data packetstransmitted downlink to the user will get congested atthe RN’s donor node buffer. If the capacity of the RNBH link is higher than the capacity of the RN AC link,data packets transmitted downlink to the user will getcongested at the RN buffer. For uplink transmissions,inverse process takes place.Overall, it can be stated that the QoS-aware resource

management for a multi-hop connection has to con-sider parameters of all its component links and in-volved RNs to secure the end-to-end QoS satisfaction.Such operation is beyond the existing LTE-A relayingspecification that considers an RN as an access pointwith an autonomous resource management and packetscheduling functionalities (i.e., the layer-3 RN model[3,22]). This paper describes a multi-node resource man-agement and scheduling scheme that can potentially solvethis problem.Before designing a resource management and sched-

uling procedure for relaying, it is curtail to get a fullunderstanding of the RN configuration schemes sup-ported by the LTE-A system. Therefore, in the nextsection of this paper, it is analyzed up to what extentthe LTE-A relaying configurations are able to satisfythe QoS requirements of real-time services and whatimpact they have on the packet delay budget.

3 LTE-A relaying implementationThe basic relaying mode of operation considered in theLTE-A system standard and in most of the other imple-mentations is the decode-and-forward (DF) approach[21]. In case of the DF relaying, a delay of at least oneradio sub-frame is introduced at each RN in the multi-hop connection. The delay relates to the DF signal pro-cessing time, i.e., decoding of the signals received on thefeeder link (i.e., RN BH in case of downlink transmis-sions) and encoding them again for transmission on theoutgoing sink link (i.e., RN AC in case of downlinktransmissions).As the result of the DF processing, the transmissions tak-

ing place on the RN BH and AC links are not correlated.Therefore, the transmissions outgoing from a RN generateinterference at the RN feeder link receiver [30]. To avoidthe RN self-interference, two options are available [3]:

� Separation of the RN BH and AC transmissions byeither allocation of orthogonal radio resources (e.g.,

frequency carriers as in the out-band relaying [3,22])or by separation of the RN AC and BH antennas(e.g., by usage of directional antennas or antennadisplacement).

� Time domain multiplexing of the RN BH and ACtransmissions so that they are not active at the sametime (i.e., the in-band relaying [3,22]).

Relaying implementations with the two self-interferenceavoidance options are analyzed next. The purpose of theanalysis is to define the lower bounds of the end-to-endtransmission delays ( tLBe2e ) for multi-hop connections in-volving RNs of either of the two configurations describedabove. The delay lower bounds are defined as the trans-mission times in an unloaded system, i.e., without thequeuing times considered, i.e.,

tLBe2e ¼ inf te2eð Þ ¼XHh¼1

tp;h þ tt;h� � ð2Þ

where inf(.) is the infinum function.

3.1 Full-duplex relayingIf sufficient separation is provided to the RN BH andAC links, the two links can be operated simultaneously(see Figure 3a), i.e., the RN can receive transmissions onthe feeder link at the same time as it transmits on thesink link (full-duplex (FD) operation). In such case, theRN can forward data to the target node as soon as it re-ceives and processes the transmission from the sourcenode.Based on the above characteristic, Equation (2) can be

reformulated for the full-duplex relaying as follows:

tLB−FDe2e ¼ maxh¼1 ::H

tt;h� �þXH

h¼1

tp;h ð3Þ

For a user data payload of size S, this is

tLB−FDe2e ¼ maxh¼1 ::H

⌈Syh ⌉!

þXHh¼1

tp;h ð4Þ

where yh is the data rate achieved by the user’s transmis-sion on the component link h, and ⌈.⌉ is the ceilingrounding function. Equation (4) can be next simplifiedas the following:

tLB−FDe2e ¼ ⌈S

minh¼1 ::H

yhð Þ ⌉þXHh¼1

tp;h ð5Þ

The minimum of the transmission data rates on thecomponent links is, due to the 'bottleneck' mechanism,

Figure 3 Full-duplex (a) and half-duplex (b) relaying operation.


the effective end-to-end data rate of the multi-hop con-nection (ye2e), thus

tLB−FDe2e ¼ ⌈Sye2e ⌉þ

XHh¼1

tp;h ð6Þ

which can be finally reformulated as

tLB−FDe2e ¼ ⌈Sye2e ⌉þ tBSp þ H−1ð ÞtRSp ð7Þ

where tpRN and tp

BS are the packet processing times atRNs and BSs, respectively.Compared to a single-hop transmission of the same

end-to-end data rate (ye2e), the H-hop FD relaying trans-mission time is longer by at least the (H −1) tp

RN elem-ent. Considering that the RN processing delay tp

RN

should be in the range of 1 up to few milliseconds, theadditional delay of a full-duplex multi-hop connectionshould not be critical considering the PDB requirementsof most service types (see Table 1). However, in loadedsystems, the queuing delay will be non-zero at some or alltransmission hops. This delay, if not properly handled, canmake the significant difference in the end-to-end packetdelivery time. Therefore, a QoS-aware packet schedulingis so crucial for relaying. Proposal of such a scheduling al-gorithm is given in Section 4 of this paper.

3.2 Half-duplex relayingIn case sufficient separation of the RN BH and AC is notprovided, the two links should be time domain multi-plexed (TDM, see Figure 3b). The TDM-based resourcepartitioning implies that a RN operates in a half-duplex(HD) mode, i.e., the RN does not transmit and receive atthe same time per transmission direction (downlink anduplink).

The TDM of RN BH and AC links provides protec-tion from the RN self-interference without the need ofeither advanced hardware solutions or additional fre-quency resources as in case of the FD relaying. Consid-ering the advantages, the TDM-based RN mode ofoperation is the main one currently considered in theLTE-A standardization [3,18].The LTE-A system standard implements the RN

TDM by reusing the LTE Release-8 multimedia broadcastover single frequency network (MBSFN) mechanism [31].It defines that certain time sub-frames (duration of oneLTE-A sub-frame is 1 ms) may be assigned for RN BH op-eration while the remaining sub-frames are used for theRN AC communication with users. Due to backward com-patibility reasons, selection of the BH-enabled sub-framesis not fully flexible. The restrictions in the sub-frame con-figuration and the TDM itself generate additional delayson top of the delays already identified for the FD relaying(see Section 3.1).The allowed BH MBSFN sub-frame patterns can be

characterized with the following statistics that directlyimpact the performance of the HD relaying:

� Number of the BH sub-frames (KBh). This statisticdetermines the capacity of the RN BH link. It alsocontrols the RN BH-AC capacity balancing foravoiding transmission bottlenecks [29]. The RN BHoperation time share (σ) related to the MBSFNconfiguration is as follows:

σ ¼ KBh

Kð8Þ

where K = 40 sub-frames is the period of the MBSFNconfiguration. According to the current LTE-A specifi-cation, the BH operation share σ can be controlled inrange 0% to 60% with a 7.5% resolution. With respect


to the packet transmission time, the number of BHsub-frames also determines the expected waiting timebetween two consecutive sub-frames supporting BHtransmission (tBh2Bh). This relation can be expressedwith the following formula:

E tBh2Bhð Þ ¼ 1σ

ð9Þ

Similarly, the average waiting time between two con-secutive sub-frames supporting AC transmission (tAc2Ac)can be expressed as follows:

E tAc2Acð Þ ¼ 11−σ

ð10Þ

� Concentration of the BH sub-frames. The lessconcentrated are the BH sub-frames (e.g., if they areevenly distributed in the 40 ms period), the lower isthe expected time until the first available BHtransmission event (t1Bh) occurs. For example, theminimal delay that is applied to a data packet thatbecomes available for BH transmission in sub-framek is as follows:

t1Bh kð Þ ¼ MINkBhf g

kBh−kð Þmod Kð Þ ð11Þ

where {kBh} is the set of indexes of the BH-assignedsub-frames, and mod is the modulo operation. The ex-pected value of the t1Bh delay is in such case

E t1Bhð Þ ¼ 1K

XKk¼1

MINkBhf g

kBh−kð Þmod Kð Þ ð12Þ

By analogy, the data packet received by an RN fromBH link in sub-frame kBh experiences delay (tBh2Ac) ofwaiting until AC sub-frame of at least

tBh2Ac kBhð Þ ¼ MINk∉ kBhf g

k−kBhð Þmod Kð Þ ð13Þ

and the expected value of this delay is

E tBh2Acð Þ ¼ 1σK

XkBh

MINk∉ kBhf g

k−kBhð Þmod Kð Þ ð14Þ

All the delays defined with the above Equations (9) to (14)are expressed in terms of radio interface sub-frames. Inthe case of the LTE-A system, this is equivalent tomilliseconds.Considering the characteristic times of the MBSFN

sub-frame configurations, it is possible to estimate theadditional transmission delay related to the TDM of HDRNs. The additional delay results from the temporaryunavailability of a specific RN link type at desired

transmission time. The expected value of the TDM delayfor two-hop relaying is as follows:

E tTDMð Þ ¼ E t1Bhð Þ þ E tBh2Acð ÞþþMAX L−1ð ÞE tBh2Bhð Þ; L1−σσ −1

� �E tAc2Acð Þ� � ð15Þ

where L is the expected number of sub-frames requiredto transmit the data packet over the RN BH link. L canbe considered as the 1-ms-resolution ceiling rounding ofthe time it takes to transmit the user data payload S withthe BH link data rate yBh, i.e.,

L ¼ ⌈S=yBh⌉ ð16Þ

To calculate the lower bound of the HD relaying, theTDM transmission delay formulated in Equation (15)should be added on top of the FD-relaying transmissiontime formulated in Equation (8), i.e.,

E tLB−HDe2e

� � ¼ ⌈Sye2e ⌉þ tBSp þ H−1ð ÞtRSp þ E tTDMð Þ ð17Þ

Comparison of the TDM delay overheads for variousMBSFN configurations and L values is depicted in Figure 4.The TDM delay overhead is especially high in case of lowσ configurations. This relates to high waiting times be-tween the packet generation event and the first availableBH sub-frame. Significance of this overhead is, however,decreasing with the expected number of active transmis-sion sub-frames L (i.e., with bigger data packets and/orlower data rates). This is because the t1Bh and tBh2Actimes are only applicable at the initial stage of a packettransmission.In case of more than two-hop connection, the estima-

tion of the TDM-related delay is less straightforward.This is because relations between the MBSFN configura-tions applied for two consecutive RN BH links need tobe considered. The summary of expected values for theTDM-related delay for a tree-hop relaying is depicted inFigure 5. The depicted distributions are based on a stat-istical analysis of MBSFN sub-frame sequences with theassumption that all possible MBSFN configurations areequal probable.Let us define an HD-relaying overhead as the ratio be-

tween the transmission time via TDM relays and thetransmission time of the same data payload over asingle-hop link. The collected data indicates that in thecase of a three-hop HD relaying the TDM delay over-head is typically (median) at the level of 10× and in ex-treme cases can reach up to 75×. Again, the highestTDM delay overheads correspond to the low σ MBSFNconfigurations. This means that the delay in transmissionover a three-hop HD-relaying connection is typically tentimes higher than the transmission time over a single-hoplink, but it can be also many times higher.

Figure 4 Expected TDM delay overhead for a two-hop relayed link for various MBSFN configurations.


Considering the typical values for the PDB require-ment (see Table 1), it should be stated that the MBSFNconfigurations with low σ are unable to satisfy the max-imum delay requirements. High σ, configurations providesignificantly higher probability of the PDB requirementsatisfaction, however, still do not guarantee it. Basing onthe presented estimations, it is recommended that the HDrelaying should be used only in two-hop topologies, whilethe multi-hop topologies should be based on the FD relay-ing only (e.g., out-band [3,22]).

Figure 5 Expected TDM delay distribution over three-hop relayed linksub-frames (L).

The lower bound delay values given above for both theFD and HD relay configurations related just to the na-ture of multi-hop transmission and the available RNconfigurations. In a realistic case of a network handlingsimultaneous transmissions of multiple users, the queuingdelays will need to be added on top of the delays estimatedin this section. Those delays, however, depend on thepacket scheduling algorithm implemented in the BS andin the RNs. The aspect of proper design of this algorithmis treated in the following section.

in relation to estimated number of effective transmission


4 QoS-aware resource managementIn modern telecommunication networks, various servicetypes may be used. This is a challenge for resource man-agement functionalities as the services may be character-ized with diverse QoS requirements. A feature of a goodresource management algorithm should be to adapt itsbehavior the QoS requirement mixture present in thenetwork at a given time. Examples of such algorithmsfor relay-less networks are described in [27,32,33].One approach to the QoS-aware resource manage-

ment problem is defined on the basis of the utility the-ory. The utility theory is a theory of economy that dealswith wealth redistribution and trade. In the context ofresource management, it is often considered as a methodfor optimizing resource allocation with respect to theuser-perceived performance fairness. As such, it hasbeen studied inter alia by Fishburn [34,35], Kelly [36,37],and Lan [38-40].In this section, firstly, the resource management

framework based on the utility theory is described withthe generic equations derived describing the procedure(Section 4.1). Secondly, the framework is adapted for thespecific case of multi-hop transmissions (Section 4.2). Fi-nally, proposals of generic utility functions for variousQoS-bounded traffic types are proposed (Section 4.3),and the overall resource management and schedulingmetrics are derived for the traffic types (Section 4.4).The formulas describing the proposed resource manage-ment and scheduling procedure are defined here in ageneric manner. Depending on specific QoS require-ments (GBR and delay) of a traffic type, one of the utilityfunctions proposed in Section 4.3 can be used to calcu-late appropriate resource allocation and scheduling met-rics for the transmission.

4.1 Principles of utility theoryThe system optimization with accordance to the utilitytheory is based on case-specific utility functions. Theutility functions are a quantitative description of one’spreferences and satisfaction. In the field of telecommuni-cations, the preferences may reflect the QoS require-ments of a service. In such case, the utility functionsrepresent the objective or subjective level of the user’sexperience of using the network.With respect to the above definition, each user j active

in a network can be characterized with a certain utilityuj related to its achieved performance. The achieved userperformance is the outcome of the applied resource allo-cation scheme x. In such case, the utility of the system isas follows:

USys xð Þ ¼Xj∈J

uj xð Þ ð18Þ

where J is the set of active users, and USys is the utilityof the whole system.
Target of the resource management optimization with
respect to the utility theory is to find the resource allo-cation scheme x. that maximizes the cumulated systemutility, i.e.,

_x ¼ argmaxx∈X

USys xð Þ� � ð19Þ

where X is the set of all possible resource allocationschemes.The optimization problem (19) can be solved, e.g.,

by means of the Lagrange multipliers method. Theproblem (19) is bounded with respect to the maximumresource availability

x ¼ xj;r� �

⇔ ∀r∈R

Xj∈J

xj;r ≤ 1; ð20Þ

i.e., where the resource allocation scheme x can be de-fined as a set of factors xj,r, each denoting allocation ofresource element r (r ∈R) to user j. When considering aninstantaneous resource allocation, xj,r takes {0,1} valuesfor all r and j.The resource allocation x = {xj,r} has a direct impact on

the transmission data rates of users by granting them ac-cess to channel capacities per resource element

yj ¼Xr∈R

Cj;rxj;r ð21Þ

where Cj,r is capacity of the radio link of the user j onthe resource element r.Considering Equations (20) and (21), the Lagrange

function for the problem (19) is as follows:

L y; x; z; λ; μð Þ ¼¼Xj∈J

uj yj� �

−λj yj−Xr∈R

Cj;rxj;r

! !þ

þXr∈R

μr 1−zr−Xj∈J

xj;r

! ! ð22Þ

where λj and μr are the Lagrange multipliers, and zr is afactor balancing inequality in Equation (20).Solution to the problem (19) is a stationary point of

the Lagrange function (22). To find it, partial derivativesof the Lagrange function (22) are calculated as follows:

∂L∂yj

¼∂uj yj� �∂yj

−λj ð23Þ

∂L∂xj;r

¼ λjCj;r−μr ð24Þ


∂L∂zr

¼ −μr ð25Þ

∂L∂λj

¼ yj−Xr∈R

Cj;rxj;r ð26Þ

∂L∂μr

¼ zr−1þXj∈J

xj;r ð27Þ

and they are compared to zero, thus

λj ¼∂uj yj� �∂yj

if yj > 0

λj ≥∂uj yj� �∂yj

if yj ¼ 0

8>>>>><>>>>>:

ð28Þ

μr ¼ λjCj;r if xj;r > 0μr ≥ λjCj;r if xj;r ¼ 0

�ð29Þ

μr ¼ 0 if zr > 0μr ≥ 0 if zr ¼ 0

�ð30Þ

Based on the above equations, the following interpret-ation of the factors λj, μr, and zr can be stated:

� λj is a marginal cost of utility, i.e., the price ofchanging value of the user’s j utility function.

� μr is the systems cost of using resource element r.� zr indicates if the resource element r is available for

assignment (i.e., is not assigned to any of the users).

The above set of equations cannot be solved directlywithout using concrete utility functions for the users.A generic solution is to use an iterative resource allo-cation with priority metric Mjr derived by combiningEquations (27) and (28)

Mj;r ¼ Cj;rλj ¼ Cjr

∂uj yj� �∂yj

ð31Þ

The priority metric indicates what increase of the user’s(and system’s) utility (aka the marginal utility [41]) can beexpected when allocating resource element r to user j.Of course, the iterative implementation of the problem

is sub-optimal. It is convergent to the optimal state,however, the resource allocation decisions are done ineach iteration based on the past state of the system (λj)and estimates of the future state of the radio links (Cj,r).The optimality of the iterative implementation is as goodas the knowledge of the conditions that will occur whenthe scheduled transmission will be executed. On theother hand, advantage of the iterative solution is thepossibility of its direct implementation in a real system.The system can calculate the priority metric for each

user and resource and use it to assign resources for thenext transmission time interval.The above derivation corresponds to the optimization

approach focused on the maximization of the systemutility, i.e., the best effort (BE) approach. The approachallocates resources always to the user that can providethe highest marginal utility for the system. As a result,users in poor radio conditions (i.e., with low Cjr, e.g., atcell-edge) may never be granted resources. From an indi-vidual user perspective, such variation in the achievableperformance indicates low reliability of the transmissionquality and is typically inacceptable.When performance fairness in the network is more de-

sired than the total system performance, the so-called α-fairness utility can be used. The α-fairness utility is autility recalculation function that corresponds to a cer-tain fairness parameter α. The α-fairness function is de-fined as [42] follows:

uαj ¼uj� �α−1

α−1 for α≠1ln uj� �

for α ¼ 1

8<: ð32Þ

With α =1, the α-fairness optimal solution correspondsto the traditional proportional fair (PF) resource alloca-tion satisfying the Nash’s definition of fairness [43].With respect to the α-fairness utility, the resource allo-

cation priority metric can be redefined as the following:

Mαjr ¼ Cjrλ

αj ¼ Cjr uj

� �α−2 ∂uj yj� �∂yj

ð33Þ

where λjα is the α-fair marginal cost of utility for the user j.

Further in this paper, only the PF approach (α =1) isconsidered as the one providing significantly higher ubi-quity of performance compared to the BE approach.

4.2 Utility-based resource management for relayingSystems enhanced with RNs can use a resource manage-ment approach similar to the one described in the previ-ous section. The utility-theory-based description of theoptimization process can be extended over multi-hop con-nections, but additional constraints need to be introduced.The additional constraints correspond to the interdepend-encies of the consecutive relay links in a multi-hop con-nection. Specifically, the additional constraints are [29] thefollowing:

� The data rate on the RN BH should be equal to thecumulated data rates on the RN AC links to usersand to subordinate RNs

yBHn ¼Xj∈Jn

yACj;n

!þ

Xk∈Nn

yBHk;n

!ð34Þ


where ynBH is the BH data rate of the RN n, yj,n

AC is thedata rate of user j on the AC of the RN n, yk,n

BH is the
BH data rate of a subordinate RN k connected to the RNn, and Jn and Nn are the sets of users and RNs connectedto the RN n, respectively.
� In case of HD RNs and FD RNs with frequencydomain resource partitioning, the same resourcescannot be assigned to the BH and AC of an RN

xBHn;r xACn;r ¼ 0;∀r ∈ R; n ∈ N ð35Þ

where xn,rBH and xn,r

AC are the resource element r allo-cation factors to BH and AC of the RN n, respectively,and R is the full set of system resources.In the LTE-A standardization, the DF RNs are equipped

with similar resource management capabilities as trad-itional BSs. This enables implementation of the distributedresource management schemes (see Figure 6). In this ap-proach, each access point (BS or RN) decides about theconfiguration of only the links it directly supports. Specif-ically, a BS controls resource allocation only to the directlinks to users and first-hop RN BH. By analogy, a RN con-trols AC communication with the users connected to itand BH links of the next-hop RNs, if any. The distributedmanagement scheme is based on local system status infor-mation, thus, in some cases may make sub-optimal deci-sions. It is, however, also significantly less burdened withthe control information provisioning overheads.With respect to the utility theory, the distributed re-

source management process considers RNs as typicalusers. Based on Equation (34), the RN utility functioncan be defined as a combination of the utilities of theusers connected to the RN, however, related to the RNBH link capacity, not their respective AC link capacities

un xð Þ ¼Xj∈Jn

uj xð Þ !

þXk∈Nn

uk xð Þ !

ð36Þ

Figure 6 Distributed resource management scheme for multi-hop rel

The Lagrange function for the relay-enhanced networkis thus

L y; x; z; λ; μð Þ ¼¼Xj∈J

uj yj� �

−λj yj−Xr∈R

Cj;rxj;r

! !þ

þXn∈N

un yBHn� �

−λn yBHn −Xr∈R

Cn;rxn;r

! !þ

þXr∈R

μr 1−zr−Xj∈J

xj;r−Xn∈N

xn;r

! !þ

þXn∈N

Xr∈R

ϕn;rxn;rXj∈Jn

xj;r þXk∈Nn

xk;r

! !ð37Þ

Again, solution to the optimization problem (19) canbe found by calculating partial derivatives of the La-grange function (37) and comparing them to zeros (thederivation is to big extent the same as for relay-less net-work, thus, not repeated here). From the derivation, thePF marginal cost of utility for a RN is as follows:

λPFn ¼Xj∈Jn

1

uj yj� � ∂uj yj

� �∂yj

0@

1Aþ

Xk∈Nn

λPFk

!24

35 ð38Þ

and as in (31), the resource allocation priority metric fora RN (Mn,r

PF) is

MPFn;r ¼ Cn;rλ

PFn ð39Þ

Further in this paper, a distributed iterative resourceallocation procedure is considered. The procedure isbased on the above definition of marginal cost of util-ity for RNs. The procedure is described in details inAlgorithm 1.

ay-enhanced networks.


4.3 Proposal of utility function for real-time trafficIn the state-of-the-art literature, various proposals ofutility functions can be found. The described utilityfunctions are, however, not always realistic. For example,it is often forced to make the utility functions concave toguarantee that there always is a unique solution to theresource assignment optimization problem. In this section,the utility functions for real-time services are proposedwith consideration of the standardized QoS requirementsdefined in [26] (see also Table 1).As explained in Section 2, the two main QoS require-

ments for real-time traffic are the GBR and PDB. A utilityfunction for a real-time traffic should, therefore, reflectthe two requirements and, thus, it is proposed here to beformulated as

uj xð Þ ¼ wjuGBRj yj� �

uPDBj tj� � ð40Þ

where yj is the data rate of the user j, tj is its packet de-livery time, uj

GBR is the utility related to the GBR re-quirement satisfaction, uj

PDB is the utility related to thePDB requirement satisfaction, and wj is priority weightof the service type related to the GBR of the service.With respect to the resource allocation scheme x, the

formula (40) is subject to Equation (21) and

E tj� � ¼ S=yj ð41Þ

In case of a GBR traffic as, e.g., audio and/or video livestreaming, data is consumed by the receiving applicationat a specific rate matching the rate in which the data isgenerated by the source application. If the availabletransmission data rate is above the data generation rate,

the excessive link capacity will be unused and the utilityof the transmission will not increase above a certainmaximum level. On the other hand, if the availabletransmission data rate is below the data generation rateat the source application, the target application willshow loss of data, e.g., breaks in audio/video streaming.Therefore, the GBR utility function should have a formof a step function, with the step steepness depending onthe acceptable level of packet loss. In line with this char-acteristic, it is proposed here to define the GBR utilityfunction as the following modified logistic function:

uGBRj yj� �

¼ 1

1þ expaGBRj −yjbGBR

� � ð42Þ

where ajGBR is a parameter related to the GBR value of

service used by the user j, and bGBR is a parameter con-trolling the steepness of the GBR utility function inproximity of yj = aj

GBR. The shape of the GBR utilityfunction and its PF modification for various traffic typesare depicted in Figure 7.The PBD utility, on the other hand, should have the

form of a step-down function, i.e., with the highest util-ity value available for delays shorter than the maximumpacket delivery time and zero utility for higher delays.This is because in a real-time traffic, data packets areutilized by the receiving application in a certain se-quence. If a data packet is not delivered on time beforethe receiving application expects it, the packet is of nouse even if fully delivered (e.g., audio frame is of no use,if its emission event is passed). Likewise, if the datapacket is delivered ahead of the expected time, it will

Figure 7 GBR utility function for common traffic types.


wait in the application buffer and not increase the QoS.A function meeting this description is as follows:

uPDBj tj� � ¼ 1− exp −

tMaxj −tjbPDB

� �1þ exp −

tMaxj −tjbPDB

� � ð43Þ

where tjMax is the maximum packet delivery time for the

user j, and bPDB is a parameter controlling the steepnessof the PDB utility function. Impact of the PDB utility onthe overall utility function for real-time traffic is illus-trated in Figure 8.

Figure 8 Impact of packet delay on utility of a 2.5 Mbit/s SD IPTV ser

4.4 Packet scheduling algorithmsBased on the above proposed utility functions for real-time traffic, resource management can be done in a cellu-lar system in a QoS-aware manner. Next, several optionsfor the QoS-aware resource allocation are defined. The ap-proaches differ in terms of the criteria they aim atoptimizing.

4.4.1 GBR-aware schedulingThe basic and the most common approach to resourcemanagement considers only optimization with respect tothe required data rates. The GBR-aware scheduling

vice (a-b).


algorithm tries to achieve a certain distribution of data ratesto users with respect to their requirements. With respect tothe utility theory framework described earlier, by inputtingthe GBR utility function to the general formula for the PFmarginal cost of utility, the GBR-aware marginal cost ofutility for the PF resource management is as follows:

λGBRj ¼ 1

uGBRj yj� � ∂uGBRj yj

� �∂yj

; ð44Þ

and next by solving the derivative in (44) with respect to(42), there is

λGBRj ¼exp

aGBRj −yjbGBR

� �bGBR 1þ exp

aGBRj −yjbGBR

� �� ð45Þ

4.4.2 PDB-aware schedulingIn case of traffic with specified requirements on the max-imum packet delivery time, the PDB-aware scheduling al-gorithm can be used. The PDB-aware resource allocationprioritizes the delay optimization criteria; however, it alsoconsiders the minimum data rate requirement. This is be-cause, as stated earlier, it is not possible to provide stablePDB satisfaction without satisfaction of the minimum datarate. By inputting the PDB utility function to the generalformula for the PF marginal cost of utility, the PDB mar-ginal cost of utility for the PF resource management is

λPDBj ¼ 1

uPDBj tj� � ∂uPDBj tj

� �∂yj

ð46Þ

where considering the transmission and queuing times

tj ¼ Syjþ tq;j ð47Þ

thus, by solving the derivative in (46) with respect to(43), there is

λPDBj ¼2 exp −

tMaxj −tjbPDB

� �bPDB 1− exp −2

tMaxj −tjbPDB

� �� Sy2j

ð48Þ

4.4.3 Full QoS-aware schedulingIn the most complex approach, radio resources are
assigned with respect to both the GBR and PDB require-ments. In this case, the marginal cost of utility is calcu-lated as the combination of the formulas (45) and (48)
λQoSj ¼ λGBRj þ λPDBj ð49Þ

5 Performance evaluationThis section covers simulation-based evaluations of theconcepts described in the former sections of this paper.Firstly, simulation assumptions used for the analysis aredescribed. Later, the generated results are presented anddiscussed.

5.1 Simulation assumptionsThe data presented further in this paper is generated viacomputer simulations based on the widely acceptedmethodology for the LTE system evaluations specified bythe 3GPP forum in [3]. For aspects not covered by the3GPP recommendation (e.g., multi-hop relaying), sup-plementary assumptions are taken from [44].The tool used to perform the evaluations presented

further in this paper is a dynamic system level simula-tor created by the author on the MATLAB platform. Itmodels operation of an LTE-A network includinglayers 1 to 3 of the protocol stack. All relevant networknodes (BSs, RNs, and UEs) and radio interfaces aremodeled explicitly. The modeling is dynamic, whichmeans that there is a timeline simulated that drives dy-namic mechanisms such as user mobility, fast fading,resource allocation, traffic dynamics (data packets cre-ation, transmission, reception and dropping), etc. Op-eration of the tool has be verified and calibrated vs.multiple other similar tools as part of the EuropeanUnion founded project ARTIST4G [44].The simulation assumptions that are clearly defined in

[3] and [44] (e.g., radio propagation and network nodemodels) are not repeated here as the two documents arepublically available. With respect to the parameters thatare ambiguous in [3] and [44], in this paper, it is specific-ally assumed that

� Only downlink transmission direction is simulated.� Total system bandwidth is 20 MHz with full

resource reuse at each cell.� Macro BSs are deployed on a hexagon grid with

1,732 m inter-site distance (ISD).� In relay-enhanced network scenarios, ten RNs are

used per macro BS sector. The RNs are deployedin two tiers along the edges of macro sectors(for details, see [44]).

� RNs utilize the standardized LTE cell-selectionprocedure [45] (i.e., based on the higher receivedsignal power) to select their respective donor cells.The selection is, however, restricted with respect tothe capability of forming multi-hop topologies. Themulti-hop topology support is a parameter of thesimulations.

� MBSFN configuration for HD RNs is adapted toprovide BH/AC capacity balancing [27] (typicallyhigh σ configurations are used), and the MBSFN


patterns with low BH sub-frame concentration areused where possible.

� Users are deployed uniformly in each macro BSsector, and they utilize the LTE cell selectionprocedure to attach to BSs or RNs.

The assumptions defined explicitly for this paper coveralso traffic models. Specifically, two scenarios are as-sumed here: low and high load. In the low load scenario,there are on average ten users per macro BS sector, andin the high load scenario, the average number of usersper sector is 20. In both load scenarios, it is assumedthat all users use constant bit-rate real-time traffic ser-vices with the following distribution:

� Two thirds of all users request an audio streamingtransmission with 320 kbit/s GBR, and

� One third of all users request a standard definition(SD) IP television (IPTV) transmission with 2.5Mbit/s GBR.

For all considered traffic types, the additional assump-tions are as follows:

� Data packet size is 1,374 bytes [46], and� Maximum packet delivery time in radio interface is

130 ms (150 ms PDB with 20 ms delay assumed forcore network [26]).

All the users are active all the time continuously gen-erating data packets according to their respective servicedata rates and packet sizes.The traffic assumptions correspond to the following

average load conditions:

� Low load scenario:
Relay-less system: 75% loadRelay-enhanced system: 50% load
� High load scenario:
Relay-less system: 95% to 100% loadRelay-enhanced system: 80% load
With respect to the assumed traffic types, the systemefficiency is assessed in terms of the following:

� GBR satisfaction, i.e., ratio between the data rateachieved by a user and its GBR setting,

� Packet dropping rate (PDR), i.e., percentage ofpackets that are not fully delivered to the target userwithin the 120 ms time limit, and

� Average and guaranteed (i.e., 95th percentile) packetdelivery times.

5.2 Results and discussionIn Section 4.4, three approaches to resource managementare proposed. Those management schemes are comparednext on the basis of relay-less and relay-enhanced net-works. Figure 9 depicts statistics of the GBR satisfactionand average packed drop rate (PDR) for macro BS con-nected users in a relay-less system. Figure 10, on the otherhand, presents the distributions of the packet deliverytimes for the relay connected users in the presence of athree-hop HD relaying topology.The PDB-aware scheduling focuses on satisfying the

packet delivery time requirement for all users resultingin the lowest PDR at the initial stage of the system oper-ation (see Figure 9b). The approach, however, does notconsider directly the data rate requirement, thus it pro-vides relatively low GBR satisfaction (on average 84% inthe high load scenario). As result, many users do notachieve their required minimum data rate (even in thelow load scenario). For such users, the PDR increasessignificantly over time (up to average 6% PDR in the lowload scenario and 21% PDR in the high load scenario).As illustrated in Figure 10, the PDB-aware schedulingtries to deliver as many packets as possible by increasingscheduling priority for the packets with short times todrop (visible as increased probability for the highestpacket delivery times). The actions are, however, insuffi-cient considering the unsatisfied GBR requirement.The GBR-aware approach, on the other hand, focuses

on satisfying the data rate requirement and does notconsider the packet delivery time requirement. This ap-proach results in higher average GBR satisfaction level(88% in the high load scenario) and improved long-termPDR statistics (on average 5.5% PDR in the low load sce-nario and 17.5% PDR in the high load scenario). The ap-proach, however, does not take any actions with respectto the packets with short times to drop (see right-handpart of Figure 10), thus some packets are dropped eventhough they could be delivered on time.Finally, the full QoS-aware approach considers both

the GBR and PDB requirements. Such dual approach re-sults in the highest performance statistics. In terms ofthe GBR satisfaction, it provides similar average satisfac-tion as the GBR-aware approach (87% in the high loadscenario) but improved performance for users in theworst traffic conditions (10% gain at 5th percentile GBRsatisfaction level). It terms of the PDB requirement, italso results in the lowest PER levels (on average 4.5%PDR in the low load scenario and 14% PDR in the highload scenario).Figure 10 presents also results for a modified (multi-

hop) QoS-aware scheduling. The modified QoS-awareapproach considers both the GBR and PDB require-ments, but in case of multi-hop connection, it assumesreduction of the maximum packet delivery time

Figure 9 Comparison of resource management schemes in relay-less system (a-b).


proportionally to the number of end-to-end relayinghops. Specifically, in the conducted simulations, it is as-sumed that the maximum packet delivery time for ahop h in an H-hop connection is

tMaxj;h ¼ tMax

j −H−hH

30 ms ð50Þ

The purpose of the modification is to improve the on-time packet delivery for the relay-connected users. The ef-fect of the modification is illustrated in Figure 10. Trans-missions to users connected over three-hop and two-hoprelaying connections have distinctive peaks in packet de-livery times at 80 and 110 ms, respectively. The peaks cor-respond to the reduced maximum packet delivery times of70 and 100 ms, respectively. As a result, the average PDRfor the relay-connected users is reduced from 16% whenusing the basic QoS-aware approach to 14% PDR whenusing the multi-hop QoS-aware approach.Basing on the data presented in Figures 9 and 10, it

can be concluded that the joint GBR- and PDB-aware

approach is the most effective in providing the overallsatisfaction for users using real-time traffic. This methodis used next, to analyze packet delivery times for variousrelaying topologies. Specifically, delays related to the FDand HD RN operations in two- and three-hop topologiesare compared. In this analysis, only the low load sce-nario is considered as it gives a clear picture of therelaying-originated delays without the impact of macroBS overload. Figure 11 depicts distributions of packetdelivery times for the considered scenarios and corre-sponding statistics are summarized in Table 2.The packet delivery time statistics confirms the ana-

lysis conducted in Section 3. The FD relaying has aminor impact on the end-to-end delay. On average, theFD two-hop relaying generates 1.5 ms additional delay.The FD three-hop relaying introduces on average 2.3 msadditional delay, however, it also increases the maximumdelay by 11 ms (compared to 7 ms for macro BS-connected users). The results are in line with the analysisincluded in Section 3.1 and especially with formula (4).

Figure 11 Packet delivery times for low load relaying scenarios.

Figure 10 Comparison of resource management schemes in relay-enhanced system, high load scenario (a-b).


Table 2 Summary of packet delivery statistics for macroBS and relay-connected users in the low load scenario

Macro BS users Relay users

Relay-less system Average: 10.6 ms -

95th percentile: 122 ms

FD two-hop relaying Average: 1.1 ms95th percentile: 7 ms

Average: 2.6 ms


FD three-hop relaying Average: 3.4 ms


HD two-hop relaying Average: 4.5 ms


HD three-hop relaying Average: 7.2 ms



In case of HD relaying, the impact on packet deliverytimes is higher than in case of FD relaying. The averagepacket delivery time for two-hop relaying is 4.5 ms andfor three-hop relaying, it is 7.2 ms. Again, the simulationresults are in line with theoretical predictions presentedin Figures 4 and 5 (see results for high σ and L =1). In caseof HD relaying, the highest delays are significantly in-creased in comparison to the direct BS-user transmission(up to 85 ms in case of three-hop connection). This indi-cates that in case of bigger packet sizes and/or lower datarates available for the multi-hop connection, problemswith satisfying the packet delivery time might occur onwide scale even for users with satisfied GBR requirement.

6 ConclusionsThis paper deals with the topic of satisfying packet deliv-ery time requirement for multi-hop relayed transmis-sions. The issue is a highly relevant topic in the contextof recent development of cellular systems. This is be-cause the relaying concept is included in the standardsof fourth generation systems; however, the relevantstandardization bodies do not provide dedicated mecha-nisms for QoS provisioning over relayed links.The presented discussion of the topic focuses first on

the analysis of the two available relaying configurations:full-duplex and half-duplex operation. The full-duplexrelaying is characterized with a small additional delayoverhead related to the RN processing time. The theor-etical analysis and the conducted simulations show thatthe lower bound of the additional delay is in the rangeof few milliseconds and proportional to the number oftransmission hops. The statistically highest delays are,however, more than doubled when comparing a three-hop FD topology with a direct BS-user transmission.The relaying delay overhead is a more significant issue

in case of HD RNs. In this case, the delay dependsstrongly on the configuration of time-domain multiplex-ing of RN BH and AC sub-frames. In the worst case, the

end-to-end transmission time over an FD two-hop con-nection can be even 20 times higher than for the directconnection. The situation can be, however, improvedwith proper sub-frame configuration.In the second part of this paper, resource management

schemes based on the utility theory are proposed formanaging relay-enhanced networks. The proposed con-cepts take into consideration the minimum data rate and/or the maximum packet delivery times to provide QoS sat-isfaction for users. From the considered schemes, the onebased on a complex data rate and transmission time showsto provide the best results. The scheme can be furtheroptimize for operation over multi-hop relayed links.The overall conclusions from the study are that the

full-duplex relaying is characterized with significantlylower delay overheads than the half-duplex relaying, andthus the full-duplex relaying is generally recommended.As for the half-duplex relaying, it is recommended to beused with sub-frame configurations characterized withhigh percentage of time available for backhaul link oper-ation and low concentration of the backhaul sub-frames.It is also recommended to use the half-duplex relayingonly with low number of end-to-end hops (maximum ofthree).

Competing interestsThe author declares that he has no competing interests.

AcknowledgementThe author is a scholar within Sub-measure 8.2.2 Regional InnovationStrategies, Measure 8.2 Transfer of knowledge, Priority VIII regional humanresources for the economy, Human Capital Operational Programmeco-financed by European Social Fund and state budget.

Received: 1 February 2013 Accepted: 13 October 2014Published: 29 October 2014

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doi:10.1186/1687-1499-2014-178Cite this article as: Góra: QoS-aware resource management forLTE-Advanced relay-enhanced network. EURASIP Journal on WirelessCommunications and Networking 2014 2014:178.

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