throughput and power management of access link df type i relay nodes for lte...

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Throughput and Power Management of Access LinkDF Type I Relay Nodes for LTE-A Networks and

BeyondBilal Al-Doori, Xian Liu, and Ayad N. Bihnam

Abstract—The decode-and-forward (DF) relay based cellularnetwork is considered an efficient technique for solving thecoverage and improving system capacity of the current fourth-generation (4G) and the emerging fifth-generation (5G) mobiletelecommunication technologies. In this analysis, we control thecodeword’s transmission parameters by achieving a good trade-off between setting more resources with changing the modulationorder, hence reducing the relay’s access link transmitted power.We first address the calculations of codeword’s size in 3GPPspecification, where an unhealthy component carrier selectionmechanism is introduced for defining relay’s transmission spec-trum. Then, we present a novel 3GPP-compliant binary integeroptimization model subject to several practical constraints, suchas the requisite resources and throughput. Through systemlevel simulations, the proposed approach is compared with theconventional relaying systems. It is shown that our approach hasmade several improvements to the conventional schemes.

Index Terms—access link, codeword, component carrier,decode-and-forward, relay.

I. INTRODUCTION

Cooperative communication based on relaying nodes isone of the most promising techniques in the current fourth-generation (4G) and the emerging fifth-generation (5G) mobiletelecommunications systems [1-2]. For the time being, theLTE-advanced (LTE-A) standard has already supported basicsmall-scale networking within conventional macrocells. It isexpected that relays in the framework of 5G will be deployedwith more small-cell paradigms, such as picocells, femtocells,and device-to-device communications.

In wireless communications, the relay protocols have beendeveloped in two categories: amplify-and-forward (AF) anddecode-and-forward (DF) ([3], [4, Ch. 22]). In principle, therelay node (RN) in AF only amplifies the received signal andforwards it to the destination node, while RN in DF decodesthe received signal, re-encodes it, and forwards it to thedestination. Apparently, DF incurs more overhead. However,in general, the fidelity of the received signals in DF is betterthan AF, especially in the range of low signal-to-noise ratio(SNR). Therefore, DF is favored by LTE-A and will likely befavored by most paradigms in 5G.

Two types of DF relaying have been specified in the 3GPPdocuments [5]. The RN in type I holds a specific cell IDand has the authority to manage and schedule the resources.

The authors are with the Department of Systems Engineering, Universityof Arkansas-Little Rock (e-mail: [email protected], [email protected], [email protected]), USA. Also, B. Al-Doori is with the Department of Electronicsand Communication, University of Baghdad, Iraq.

In addition, DF type I RN could control the access linksynchronization of the attached users. In practice, type I RNmay be regarded as a simplified base station (BS). On theother hand, type II RN has no specific control signals. Itis transparent to the RN user’s equipment (RUE), with theobjective of enhancing the BS coverage area. Thereby fromthe functionality perspectives of RN types, type I could beused for covering the dead-spot cell-edge area and enhancingthe network spectral efficiency in a cost-efficient way, butthese profits come with the price of several aspects, suchas more power consumption, extra complicated algorithms,additional overhead, as well as more extra resources requiredfor maintaining the prerequisite throughput.

With respect to these aspects, we were motivated to explorethe access link power allocation and throughput-guaranteedissues of RN type I-based cellular network, where the featuresin the recent 3GPP standards are considered. Before presentingour approach, let us review some literature works as the stateof the art to highlight our contributions.

Several studies have investigated the relaying energy effi-ciency related techniques. In [6], the authors developed anenergy-aware metric termed survival probability to find anoptimal energy scheduling algorithms of energy-harvestingrelays. In [7], the authors studied the transmitting powerfor practical channel’s model. The total required consumedpower is used for maximizing energy efficiency problem,where outage probability constraints were considered. In [8],the authors used Nash bargaining approach to balance theinformation transmission ratio and the harvested energy of therelay in a wireless powered relay network.

On the other hand, the combination of OFDM and relaytechnology maximizes the network spectral efficiency andincreases cell coverage area. The latest researches on resourceallocation for OFDM relay networks are introduced in [9]-[11].In [9], the authors presented two joint optimization models forachieving large SNR gains over the equal resource allocation.While in [10] and [11], combinatorial optimization problemswere formulated for improving throughput and significantlyreducing the algorithms’ complexity.

There is no doubt that the power allocation across sub-carriers presents one of the most challenging issues in OFDM-based relay systems. Generally speaking, the resource allo-cation and power management scenarios can be categorizedinto two main strategies. The first one adopts enhancing thenetwork spectral efficacy by applying adaptive criteria of thetotal allocated resources (resource management) [12]. The sec-

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ond strategy focuses on reducing energy consumption, wherethe data rate requirement is considered as QoS constraint(energy efficiency)[13]. However, the both strategies stress ondetermining the solution and solving the problems, withoutessentially conforming the 3GPP standards for addressing thetransmission parameters and the required throughput.

In this paper, different than [6]-[13], we investigate both theconventional and proposed power allocation and throughputmanaging schemes for RN type I-based cellular network.We believe that there is a need to take the advantages ofusing the available unused physical resource blocks (PRBs)(to be referred to as the residual PRBs through this paper)for minimizing the access link transmitted power. This canbe achieved by adopting the principles of energy-bandwidthexpansion trade-off.

We make an effort in three aspects, selecting, modeling, andsolving. First, a new spectrum selection method is introducedfor addressing the range of resources to be used by RN foraccess link transmission. Second, we develop an optimizationmodel for choosing the optimal transmission parameters basedon setting more residual PRBs in the codeword (CW), whilemaintaining the RUEs’ prerequisite throughput. Thirdly, wepropose a solving strategy, to be referred to as the CWmanagement (CWM) scheme. One of the main objectives ofCWM algorithm is to assist RN in selecting an appropriatemodulation and coding scheme (MCS) for minimizing theaccess link downlink power. We will show that the proposedapproach outperforms the conventional methods. To our bestknowledge, the proposed approach here has not been men-tioned elsewhere.

II. SYSTEM MODEL

We consider RNs with DF-relay based OFDM within LTE-A network, as shown in Fig. 1. In the hexagonal macrocellof interest, there is a set of RNs s ∈ S ={1, 2, ... , S}, anda set of active online RUEs i ∈ Is ={1, 2, ... , I}, connectedwith RN s directly. For convenience, we have listed the mainsymbols used throughout the paper in Table I.

The 3GPP standards outline the downlink time-frame struc-ture, which is partitioned into access sub-frame (ASF) andbackhaul sub-frame (BSF) [5]. The ASF includes the accesslink, referring to the link between RN and RUE (RN→RUE),and the direct link, referring the link between BS and RUE

Fig. 1 Relaying based cellular landscape communication model.

TABLE IList of Symbols

Symbol Definition

i RUE index, i ∈ Is ={1, 2, ... , I}

n NPRB index, n ∈ Ni ={1, 2, ... , Ni}

k ITBS index, k ∈ Ki ={1, 2, ... , Ki}

r IMCS index, r ∈ Ri ={1, 2, ... , Ri}

s RN index, s ∈ S ={1, 2, ... , S}

wi,k,n TBS (in bits) of ITBS k, NPRB n for CW of RUE i

xi,k,n indicator variable for deciding if k and n are allocated for i

yi the assigned number of residual PRBs for RUE i

ψs total number of residual PRBs for RN s

(BS→RUE). The BSF includes the backhaul link, referring tothe link between BS and RN (BS→RN), and the backhaul-direct link, referring to (BS→RUE) link transmission in BSF[14]. Depending on the spectrum used for access and backhaullinks, relaying can be classified into out-band and in-bandtypes. In this work, the relaying systems are assumed tooperate with in-band half-duplex, where the links BS→RN,RN→RUE and BS→RUE work in the same frequency band.The operative spectrum is divided into a set of componentcarriers (CCs) within the operative licensed bands, wherecarrier aggregation (CA) is deployed [15].

As the synchronization’s mechanism presents one of thelargest problems of multi-hop links communication, the down-link signals transmitted by BS are used for ensuring symbol-level synchronization between signals transmitted [1]. In addi-tion, BS→RN and RN→RUE transmissions can not take placesimultaneously. Let n, k, and r be the indexes NPRB , ITBS ,and IMCS , respectively, where n ∈ Ni ={1, 2, ... , Ni}, k ∈ Ki

={1, 2, ... , Ki}, and a set of modulation and coding schemesr ∈ Ri ={1, 2, ... , Ri}. The set Ni collects all the possibleNPRB assigned by RN for RUE i at a time, where Ni is thelargest number of PRBs to be scheduled for RUE i.

In this paper, the capability of minimizing the power as-signed for downlink transmission and mitigating the inter-cellinterference are due to the fact that the whole residual PRBsin the system are equally distributed among the active RUEs ata time. Accordingly, the value of the total number of residualPRBs (ψs) of RN s at a specific time is calculated as follows:

ψs = Ns −

Is∑i=1

Ni, and ψs ∪

Is∑i=1

Ni ∈ Ni, (1)

where Ns is the largest number of PRBs in the RN’s systembandwidth. While the residual PRBs allocated for RUE i (yi )is considered, as shown below.

yi =ψs

Is. (2)


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Different from the majority of previous works that assumedequal partitioning of resources between ASF and BSF forin-band spectrum management, we introduce unhealthy CCselection mechanism that is used by BS for defining the relay’stransmission spectrum.

In details, an BS selects one CC that experiencing a highlevel of interference from the neighboring cells, to be usedfor RN’s access link transmission. As shown in Fig. 2, theBS allocates CC1 that has a high level of interference forRN. It’s important to point out that this partitioning schemecould improve the cellular network performance by removingthe unhealthy CC from BS direct link transmission. This willincrease spectral efficiency and guarantee a reliable transmis-sion. However, this mechanism has no effects on RN’s accesslink communication. This is due to the RN’s transmissionwithin a small coverage area, where the associated attachedusers are locating close to the RN’s active pattern and experi-encing good channel conditions. Therefore, the whole residualPRBs in the unhealthy allocated CC are distributed among theactive RUEs at a time. Accordingly, when the sector load islow, an RN could increase the probability of using the residualPRBs of unhealthy CCs, thus reducing the required modulationorder for downlink transmission.

III. CW’S CALCULATION IN 3GPP SPECIFICATION

For single RN’s access link transmission, only one CW isrequired to be transported by using single-input-single-output(SISO) system. Conceptually, a CW corresponds to a transportblock (TB) combined with 24 bits of cyclic redundancycheck (CRC). Over the radio interface, TB presents a way oforganizing the data in a bit form and ensuring the reliabilityof the scheduled transmitted data. Accordingly, each CW istransmitted with a specific size known as transport block size(TBS). For typical transmission, each CW is coupled with aspecific channel quality indicator (CQI) used to specify theMCS index for calculating TBS of a CW [15]. To calculatethe TBS, a mapping process for a particular RUE needs to bedetermined through two steps. First, a mapping between theMCS index (IMCS) and the transport block size index (ITBS) isapplied (IMCS 7→ ITBS) as presented in [15, Table 7.1.7.1-1].A mapping between the number of PRBs (NPRBs) and ITBS

is then used to calculate the TBS ((NPRB , ITBS)7→ TBS) aspresented in [15, Table 7.1.7.2.1-1], and explained in Fig. 3.

In practice, there is a scenario that RN allocates only a smallnumber of PRBs at a time for a particular subset of RUEs.

Fig. 2 In-band relay CCs allocation and frame structure model.

Fig. 3 Conventional TBS calculation procedure.

Accordingly, when the system-load level allows, it is desirableto reassign the residual PRBs to minimize the total access linktransmitted power. This could be done by decreasing ITBS thatsequentially decreases IMCS . Furthermore, it is possible tomitigate the interference from the adjacent BSs and RNs. Thus,the proposed approach could increase the energy efficiency byforming a CW with more PRBs and has the same TBS.

It’s noted that one of the main debates in energy bandwidth-expansion lies in the efficiency of adding more resources toreduce the modulation order, making potential controversialeffects on the required air-time for transmission and thecode rate. To overcome this dilemma, an efficient bandwidth-expansion technique was proposed to reach energy savings ofabout 93 % [16]. This improvement was done by adjusting theadded PRBs with a trading factor α during low traffic periods.Therefore, assigning more than one specified thresholds ofPRBs per CW will lead to a restrictive reduction in the datarates transmission and energy efficiency.

In general, the issue of how to concurrently minimizing theRN’s access link transmitted power and adjusting the numberof PRBs corresponding to one CW using the 3GPP standardTables has not been appropriately addressed. Therefore, andwith respect to the aforementioned issues, an optimizationmodel will be introduced in the following Section, wherethe transmission parameters ITBS , NPRBs , and the throughputare used as design parameters (k,n,TBS) for RN DF-type Iaccess link transmission. The proposed model clarifies therelationship between these design parameters and other per-formance’s measurements, such as maintaining the prerequisitethroughput, reducing the power consumption, and confirmingthe latest 3GPP standard Tables.

IV. PROBLEM FORMULATION

In this section, an optimization model for solving the alloca-tion of PRBs of each RUE, incorporating TBS, is presented.The objective is to consider different metrics as constraintsto reduce ITBS that eventually affects on the scheduling ofthe RN’s access link transmitted power in each sub-frame.For each CW, there is a specific prerequisite TBS calculatedregarding NPRB and ITBS . Accordingly, the prerequisite TBSis used as a benchmark for finding a new lower ITBS regardingthe added residual PRBs of each RUE. Therefore, computing


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the TBS of all the RUEs before the allocation is constrained bythe model and the CWM scheme. From the above mentioned,we optimize the problem of minimizing the RN’s accesslink transmitted power by choosing the maximum numberof residual PRBs to reduce ITBS , while maximizing the pre-calculated TBS value that maintains the throughput requiredof each RUE. The optimization model is formulated as solvingthe following objective function:

maximizeI∑

i=1

Ki∑k=1

Ni∑n=1

xi,k,n wi,k,n, (3)

andxi,k,n ∈ {0, 1}, (4)

where xi,k,n is a binary decision variable to indicate the as-signment of the access link downlink transmission parameters;xi,k,n = 1 if NPRB n of ITBS k is selected for calculating TBSof RUE i, otherwise xi,k,n = 0. wi,k,n is the TBS (in bits)equivalent to ITBS k and NPRB n that is accredited for CWof RUE i.

The QoS requirements of RUEs should be guaranteed formaintaining reliable communication. Based on 3GPP Tables,if NPRB and ITBS are both known then the prerequisite TBSof TB denoted as w

req .i can be found. In view of that, (5) is

used for describing the restrictions allowed on TBS, i.e., useswreq .i to express TBS lower band value, as given by

wi,k,n

, 0 if wi,k,n ≥ wreq .i ,

= 0 otherwise.(5)

According to (5), we propose two constraints for assigning theoptimal value of both NPRB and ITBS that are given by

Ki∑k=1

maxn∈Ni

xi,k,n ≤ 1, (∀i) (6)

Ni∑n=1

mink ∈Ki

xi,k,n ≤ 1. (∀i) (7)

In (6) and (7), "max" and "min" stand for "maximum" and"minimum", respectively. Regarding (6), all the allocatedPRBs for RUE i are transmitted with one ITBS k. Fordetermining the required NPRB and to provide each RUE iwith the prerequisite throughput, constraint in (7) is applied.Furthermore, constraint (8) is necessary for ensuring that theconstraints in (6) and (7) select only one optimal decisionvariable, as stated

Ki∑k=1

Ni∑n=1

xi,k,n = 1. (∀i) (8)

In addition, another constraint is necessary to guarantee theselection of only the assigned number of residual PRBs (yi)for RUE i, as stated

N∑n> byi c+Ni

xi,k,n = 0. (∀i,∀k) (9)

It’s noteworthy to mention that the number of residual PRBsadded for each CW is bounded such that yi ≤ α (to preserve

target data rates and energy efficiency in [16]). Since theresidual PRBs are assumed to be distributed among RUEs,byic is used to ensure the integer assignments of PRBs.

Accordingly, we formulate the complete optimization modelas the objective function in (3) incorporated with the abovestated constraints in (4) and (6) through (9).

V. CWM ALGORITHM

In this Section, CWM algorithm is used for reducing ITBS

of RUE i, as shown in Fig. 4. The idea is to consider theprerequisite PRBs with the added residual PRBs (yi) as abitmap to be allocated for each RUE. Basically, RN determinesthe optimal IMCS of each RUE (from the backhaul link). Then,the conventional TBS calculation procedure is performed, asshown in Fig. 2. As the residual PRBs are equally distributed,a new ITBS is found and mapped to a new IMCS .

In details, for Lines (1-4), the CWM algorithm initializesthe startup parameters of all the online RUEs. Lines (5-6),CWM considers IMCS of r for mapping and finding ITBS ofk, hence determining TBS (wi,k,n). In Lines (7-10), CWMscheme adds yi to find new ITBS of k∗(i) and uses the Tablesin [15] for defining a new IMCS of r∗(i) . This procedure is thensequenced to the other RUEs. Consistent with the new IMCS

of r∗(i) , the approach in [17] is then used for calculating theaccess link downlink power required for PRBs.

It’s noteworthy to mention that the CWM representation ofthe optimization problem includes many variables that from acomplexity point of view may take too much time for findingthe solution. However, it’s reached the required transmissionparameters in a small time. This is due to the idea of searchingover the 3GPP standard Tables described in [15] for finding anallocation solution. In the other words, the number of decisionvariables is small and known. Let us explain the worst com-plexness case, which could sufficiently confirm the powerfulof CWM method in solving the optimization model. In theconstructed approach, there are Is RUEs attached to RN s and4M mapping processes, where M is the mapping allocationtime regarding the Tables in [15]. The first two mappingprocedures present (ri 7→ ki) and ((ni , ki)7→ wi,k,n), in whichthe conventional operation is applied. However, we set moretwo mapping processes (wi,k,n, (ni + yi ) 7→ k∗i ) and ((k∗i 7→ r∗i )for addressing the transmission parameters and maintaining theprerequisite throughput. Thus, the upper bound computationalcomplexity (CX) of CWM can be derived as

Fig. 4. CWM Greedy algorithm.


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TABLE IIAnalytical CWM Evaluation.

RUE ITBS NPRB TBSC

onve

ntio

nal

RUE1 10 (16-QAM) 5 872 bitsRUE2 16 (64-QAM) 7 2280 bitsRUE3 25 (256-QAM) 13 8248 bits

CW

M

RUE1 6 (QPSK) 9 936 bitsRUE2 12 (16 QAM) 11 2472 bitsRUE3 21 (64-QAM) 17 8504 bits

CX = O(

Is︸︷︷︸onlineRUEs

( 3︸︷︷︸update transmissionparameters (i,k,n)

+ 4M︸︷︷︸mappingprocesses

+ byic + Ni︸︷︷︸maximum NPRBs

allocated for RUE i

)),

(10)where we treat the time complexity of a constant number asO(1), and ignore the lower order terms. As the number ofresidual PRBs added for each RUE is controlled by α andthe number of mapping operations is only four, then we canconclude that Ni � byic > Is > M. Thus CX = O (Is (4M +(byic + Ni))) ⇒ CX= O(Ni).

VI. PERFORMANCE EVALUATION

In this work, the simulation is set up to comply with thecontext of LTE-A. Basically, the operations of BSs, RNs,and RUEs follow the LTE-A physical layer protocols, wherethe system level simulations are implemented by using LTE-Sim [18]. Accordingly, the involved units and parameters areconsistent with those in the LTE-A system. The performanceevaluation of this work is compared with the schemes proposedin [7] and [13], (to be referred to as the conventional throughthis paper). The conventional scheme involves assessment ofpower minimization based resource allocation for OFDMAnetwork that is implemented on typical downlink transmission.

A. Numerical Example

The performance of the proposed scheme has been assessedby numerical tests. This is confirmed by calculating theoptimal trade-off between NPRB and IMCS , while maintainingthe CW’s requested throughput, as shown in Table II. Theseresults indicate that the prerequisite TBS in bits is guaranteedwhen CWM scheme is applied. For example, if there arethree online RUEs and the total number of residual PRBs ψs

=12, then each RUE can elaborate and add more 4 PRBs.Accordingly, when RUE1 is transmitting using conventionalscheme with NPRB = 5 and ITBS = (16-QAM), then the TBS= 872 bits. However, when CWM is applied, the new NPRB

= 9 with lower ITBS (QPSK) and TBS = 936 bits.

B. Simulation Results

In the present simulation, RN is deployed over an areain which the cell’s radius of BS is 1 km, where the mainsimulation settings are summarized in Table III. In the sim-ulation, we firstly consider the average IMCS for comparingthe performance of the conventional model with the CWMscheme. The results are shown in Fig. 5. It is observed thatthe CWM scheme has shown an improvement in reducingIMCS , where two reasons are notable: (i) properly optimizesthe TBS and the added residual PRBs for reducing ITBS , and(ii) correctly using Tables in [15] to update and reassign NPRB .

TABLE IIISimulation Settings

Parameter Value Parameter Value

Simulation Time 30 s RUE’s Service Infinite Buffer

Active RUEs per RN 3 Shadowing 8 dB

Bandwidth per CC 5 MHz RUE’s speed 3 Km/Hr

Max. RN’s AccessPower Transmission 43 dB

RUE’sDistribution Uniform

Cell Radius 1 km Propagation Jakes’Model

Path Loss ModelL = 128.1+37.6log(R),(R in km)

Noise SpectralDensity -147 dBm

Fig. 5. Average IMCSicomparison.

On the other hand, the scheduler efficiency regarding thetransmitted power of PRB j at MCS r for RUE i (Ps

i, j,r ) isevaluated, as shown in (11), where ϕr is the threshold SINRchosen for a particular MCS index (I ∗MCSi

), gs, i is the channelgain between RN s and RUE i, and σ is the noise density. It’snoteworthy to mention that the new lower I ∗MCSi

is associatedwith a lower SINR threshold used for power calculation [15],therefore, Fig. 6 illustrates this relational mapping process(I ∗MCSi

7−→ r 7−→ ϕr ).

Psi, j,r = ϕr

S∑̃s,s

P s̃i, j,r gs̃, i + σ

2

gs, i. (11)

Fig. 6. average SINR threshold (ϕr ).


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As expected, Fig. 7 shows that the access link powertransmitted by RN is decremented with CWM approach ascompared to the conventional scheme, where three RUEs areexaminied for addressing the effect of the new lower ϕr .

Fig. 7. RN downlink access link power.One can observe that the proposed scheme has a significant

improvement in the access link downlink transmitted power,achieving nearly 15%-35% better performance than the tra-ditional approaches. This merit would help to improve theaccuracy impacted by increasing the total power required byRUEs. Also, it could enhance the moving effects such as themobile path loss. In addition, consistent with the employmentof 3GPP standard Tables as constraints (searching over theTBS Tables, where the number of decision variables is small),the latency restriction, the efficiency of bandwidth expansion,and the fair time required to reach the solution are guaranteed.

Finally, the CC unhealthy proposed scheme is examinedas shown in Fig. 8 below. This measurement is based oncollecting the full-band feedback channel conditions fromnine users distributed uniformly in the cell coverage area fordetermining the interference on each CC. Then, applying theproposed resource management. It’s shown that the CC inwhich a high level of interference depicted is selected forRN’s access link transmission. Therefore, CC1 is chosen asthe most unhealthy CC in this examination to be used by RN.On the other hand, CC4, CC2, and CC3 are used for backhaul(lower interference level for exchanging control signals), direct(used as primary CC), and direct (used as secondary CC) links,respectively.

Fig. 8. Interference level per PRB.

VII. CONCLUSION

In this paper, we conducted the analysis for the accesslink transmitted power of DF relay I based cellular network.Unlike most reported studies, in the present work, the notion ofbandwidth-expansion is realized incorporating the recent 3GPPstandards. We made efforts to formulate an optimization modelto be used for finding a lower modulation order and hencereducing the downlink access link transmitted power requiredfor each PRB of each user. The availability of such scenarioexpedites the qualitative assessment as well as quantitativeanalysis since no extra integration or series truncations isneeded.

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