Evaluation of Adaptive and Non Adaptive LTEFractional Frequency Reuse Mechanisms

Uttara SawantDepartment of Computer Science and

EngineeringUniversity of North Texas

Denton, Texas 76207Email:uttarasawant@my.unt.edu

Robert AklDepartment of Computer Science and

EngineeringUniversity of North Texas

Denton, Texas 76207Email:Robert.Akl@unt.edu

Abstract—LTE networks are deployed to increase capacity andcoverage especially for the indoor and cell-edge mobile users.However, such deployment comes with major challenges, radioresource management and inter-cell interferences. FractionalFrequency Reuse mechanism (FFR) is one of the most effectiveinterference avoidance techniques. In this paper, we evaluatean existing adaptation process that adjusts to better networkperformance as users move in the network. With adaptation inplace, we utilize our proposed metric to determine inner regionradius and frequency allocation which generates high total cellthroughput and serves maximum number of users in the network.The performance of static sectorized FFR with specific mobilitymodel using adaptation process is evaluated using proposedmetric and other metrics.

I. INTRODUCTION

Orthogonal Frequency Multiple Access (OFDMA) offersgreat spectrum efficiency and flexible frequency allocation tousers. However, in Long Term Evolution (LTE) networks thesystem performance is severely hampered by the Inter-CellInterference (ICI) due to the frequency reuse. For example,the cell edge users will experience high interferences fromneighbouring cells [1].

Fractional Frequency Reuse (FFR) is one of the intefer-ence management techniques which require minimal or nocoordination among the adjacent cells. In this paper, we willevaluate the performance of the sectorized FFR mechanismwith five metrics; four existing and one proposed metric whichdetermine the values for inner region radius and frequencyallocation for optimal results. For a specific mobility model,an adaptation process is applied to the FFR mechanism andits performance is evaluated for proposed metric and othermetrics. Results are also generated and evaluated for non-adaptation process for all metrics.

II. RELATED WORK

The basic mechanism of FFR is to partition the macrocellservice area into spatial regions and each sub-region is as-signed different frequency sub-bands for users. In [2], a fre-quency reuse technique is proposed which aims at maximizingthroughput via combinations of inner cell radius and frequencyallocation to the macrocell. The cell throughput is shown tobe further optimized with a position oriented approach of

frequency allocation of the femtocells. A similar performancestudy is done by authors in [3], [4], [5] where they analyzethe FFR scheme and propose a dyanmic FFR mechanism thatselects the optimal frequency allocation based on the cell totalthroughput and user satisfaction. In [1], the user satisfactionmetric introduced by the authors is evaluated for users’ mobil-ity and the performance is compared with other reuse schemes.The research is further enhanced in [6] where cell-edge reusefactor is set to 1.5 and results are generated to determinethe optimal inner radius and inner region bandwidth. In [7],authors present a fractional reuse optimization scheme basedon capacity density which show better performance comparedto conventional Reuse-1 and Reuse-3 schemes. Graph theoryand similar optimization techniques are presented in [8], [9],[10]. Authors in [11] use two-stage heuristic approach tofind optimal frequency partitioning. Work in [12], [13], [14]propose some promising flexible spectrum reuse schemes. Softfrequency reservation is employed in [15], [16] to providemore multi-user diversity gain and fairer resource allocation.

III. FFR MECHANISM

The topology of Fig. 1 consists of 16 cells with four non-overlapping resource sets. Each cell of the topology is dividedinto two regions; inner and outer region. The total availablebandwidth of the system is split into four uneven spectrums,denoted by A (blue), B (green), C (red) and D (yellow).Spectrums A, B, and C have equal bandwidth and are allocatedin outer regions with Frequency Reuse 3. On the other hand,spectrum D is allocated in all inner regions with FrequencyReuse 1. The frequency resources in all inner regions areuniversally used, since the inner region users are less exposedto ICI. From user’s perspective, Integer Frequency Reuse (IFR)can be regarded as a special case of FFR. In IFR, all RBsallocated to a cell can be used anywhere in the cell withoutany specification of user’s location. For comparison, the FFRscheme that is selected by adaptive mechanism is comparedwith variations of IFR.

The macrocell coverage area is partitioned into center-zoneand edge-zone. The entire frequency band is divided into twoparts one part is solely assigned to the center-zone (e.g., sub-band A in Fig. 1) and the other part is partitioned into three

978-1-5090-4909-7/17/$31.00 ©2017 IEEE

Fig. 1. Strict FFR Deployment in LTE Deployment [17]

subbands (e.g., sub-bands B, C, and D) and assigned to thethree edge-zones [17].

IV. SYSTEM MODEL

A. Description

A set of mulitcast users are uniformly distributed in thegrid of 16 macrocells. In order to find the optimal inner regionradius and frequency allocation in the deployment, the mecha-nism divides each cell into two regions and calculates the totalthroughput for the following 40 Frequency Allocations (FA),assuming Frequency Reuse 1 and x for inner and the outerregions respectively [18], [19], where x is the frequency reusefactor of 3. Each FA corresponds to paired value of fractionof inner region resource blocks and inner region radius.• FA1: All 25 resource blocks are allocated in inner region.

No resource blocks are allocated to the outer region.• FA2: 24 resource blocks are allocated in inner region.

1/x resource block allocated to the outer region....• FA39: 1 resource block allocated in inner region. 24/x

resource blocks allocated to the outer region.• FA40: No resource blocks are allocated in inner region.

25/x resource blocks allocated to the outer region.For each FA, the mechanism calculates the total throughput,

user satisfaction, user fairness, and weighted throughput valuesbased on new metrics [20]. This procedure is repeated forsuccessive inner cell radius (0 to R, where R is the cellradius). The mechanism selects the optimal FFR scheme thatmaximizes the cell total throughput. This procedure is repeatedperiodically in order to take into account users’ mobility.Therefore, the per-user throughput, the cell total throughput,User Satisfaction (US), and other metrics are calculated inperiodic time intervals (the exact time is beyond the scope

of this manuscript) and at each time interval, the FFR schemethat maximizes the above parameters is selected. This periodicprocess is called adaptation [1]. The system model describedabove can be used for supported LTE bandwidths ranging from1.4 MHz to 20 MHz.

The signal-to-interference-plus-noise (SINR) for downlinktransmission to macro user x on a subcarrier n can be expressedas [21]

SINRx,n =PM,nGx,M,n

N0∆f +∑M ′PM ′,nGx,M ′,n

(1)

where, PM,n and PM ′,n is transmit power of serving macrocellM and neighboring macrocell M ′ on subcarrier n, respectively.Gi,M,n is channel gain between macro user i and servingmacrocell M on subcarrier n and Gi,M ′,n corresponds tochannel gain from neighboring macrocell M ′. Finally, N0

is white noise power spectral density and ∆f is subcarrierspacing.

The channel gain G, given by the following equation [21]is dominantly affected by path loss, which is assumed to bemodeled based on urban path-loss PL as defined in [22].

G = 10−PL/10 (2)

Additionally, for the throughput calculation, we use thecapacity of a user i on a specific subcarrier n, which can beestimated via the SINR from the following equation [21]

Ci,n = W · log2(1 + α · SINRi,n) (3)

where W denotes the available bandwidth for each subcarrierdivided by the number of users that share the specific subcar-rier and is a constant for a target bit error rate (BER) definedby α = −1.5/ln(5 ·BER). Here we set BER to 10−6.

So the expression of the total throughput of the servingmacrocell M is [21]

Ti =∑n

βi,nCi,n (4)

where, βi,n represents the subcarrier assigned to user i. Whenβi,n=1, the subcarrier is assigned to user i. Otherwise, βi,n=0.

B. Mobility Model

In this paper, we evaluate network performance in a scenariowith mobile users in the highlighted cell of the topologypresented in Fig. 2. During the experiment that lasts for 217seconds, 24 users of the examined cell are moving randomlyinside the cell with speed 3 km/h, according to the PedestrianA channel model [23]. It is assured that all of them remaininto the cell’s area, ensuring that their total number will remainconstant. This corresponds to low mobility scenario with zeroto minimal handover.

V. PERFORMANCE METRICS

The paper evaluates the network performance for adaptiveand non-adaptive FFR mechanisms considering users’ mobil-ity. For comparison, the adaptive FFR scheme that is selectedby our mechanism is compared with three different cases.

The first case, where the optimal inner radius and frequencyallocation remain constant through the adaptation process, isreferred to FFR non-adaptive. The second case, where the cellbandwidth equals the whole network bandwidth, is called IFRwith frequency reuse 1 (IFR1). In the third case, the innerregion radius is zero and each cell uses one third of thenetworks bandwidth. This case is called IFR with frequencyreuse 3 (IFR3). The difference with the IFR1 case, lies in thefact that only co-channel BS are considered in interferencecalculation and as a consequence, the interference BS densityis divided by 3. The notation of IFR schemes is definedaccording to the work presented in [1], [16].

A. Definitions

1) Existing Metrics: We use a metric US defined by au-thors in [3]. It is calculated as the sum of the users’ throughputdivided by the product of the maximum user’s throughputand the number of users (X). US ranges between 0 and 1.When US approaches 1, all the users in the corresponding cellexperience similar throughput. However, when US approaches0, there are huge differences in throughput values across theusers in the cell. This metric will be utilized in scenarios wherefairness to the users is significant such as cell throughput andJain fairness index.

US =

X∑x=1

Tx

max user throughput ·X(5)

To obtain a metric of fairness for performance evaluationwe use the Jain fairness metric introduced in [20]. Assumingthe allocated throughput for user i is xi, then Jain fairnessindex for the cell is defined as

JI =

(X∑i=1

xi)2

X ·X∑i=1

x2i

(6)

This metric is interesting for the evaluation of the proposedmethod due to its properties. It is scale-independent, applicablefor different number of users and it is bounded between [0, 1],where 0 means “total unfairness” and 1 means “total fairness”in terms of throughput division among the users [17].

With metric WT defined by the authors in [17], the aim isto not only generate low variance of the per-user throughputvalues but also obtain higher values of the cell total throughput.

WT = JI · T (7)

where T is the cell mean throughput.2) Proposed Metric: We introduce a new metric, weighted

throughput based on user satisfaction WTUS , to add weightsto the cell throughput corresponding to specific inner radiusand inner bandwidth such that the resultant throughput ishigher than the corresponding throughput optimized at usersatisfaction alone and all the users in the corresponding cellexperience similar throughput.

WTUS = US · T (8)

Fig. 2. Strict FFR Uniform Deployment for Simulation

From Eqn. 7, 8, it is clear that the metrics are cell-based.

B. Mathematical Model

The mathematical model applied to the new metric isproduct expression of user satisfaction and cell throughput.This output of this model will reflect both pros and consof the individual metrics. We have applied the same productmodel utilized in WT definition. Since authors in [17] provedbenefits of WT over JI and T , the new metric is introducedby applying similar model over US and T , expecting theresultant metric WTUS to perform better than individualmetrics.

VI. PERFORMANCE EVALUATION

A. Simulation Parameters

Following Tab. I are the simulation parameters set forthe research. A sample uniform deployment considered in

TABLE ISIMULATION PARAMETERS

Parameter ValueSystem Bandwidth 5 MHzSubcarriers 300Subcarrier Bandwidth 180 KHzCell Radius 250 mInter eNodeB distance 500 mNoise Power Spectral Density -174 dBm/HzSubcarrier spacing 15 KHzChannel Model Typical UrbanCarrier Frequency 2000 MHzNumber of macrocells 16Macrocell Transmit Power 46 dBmPath Loss Cost 231 Hata ModelUsers’ speed 3 km/h PedA

simulation is shown in Fig. 2. Macrocells are located at cellcenters. Active users are randomly distributed and are shownwith white dots.

Fig. 3. Comparison of Variance in User Throughput

B. Simulation Guidelines

Note that we assume the simulation is run consideringstable downlink data traffic since the simulation frameworkis analyzing data for downlink traffic. A network snapshotwhere user association to macrocell remains stable for theduration of the simulation run is considered since the usersare associated to the base stations with maximum SINR. Afrequent handover will occur in high mobility environment andthat is not included in the scope of this paper. The simulationsoftware for our research is based on [24].

C. Performance Analysis

1) Comparison of New Metric to Other Metrics: We in-troduced the new metric due to the following reasons, betterperformance in terms of average user throughput and effectiveuser fairness in terms of variance in user throughput. The resultshown in Fig. 3 prove the user throughput optimized with newmetric shows better performance and low variance.

2) Adaptive versus Non-adaptive mechanisms - Metric Per-formance: As the users’ mobility and new positions aredetermined, the simulation calculates metrics and optimalinner radius and subcarrier allocation for adaptive and non-adaptive variations for each FFR mechanism. For non-adaptiveFFR mechanism, optimal inner radius and subcarrier allo-cation remain constant even after the user position changesand performance metrics are calculated. For adaptive FFRmechanism, new values of optimal inner radius and subcarrierallocation are determined based on the new user positionand optimal FFR performance. Therefore, the adaptive FFRmechanism reacts to user mobility better than non-adaptiveFFR mechanism. Figs. 4 - 7 show FFR performance for allmetrics with adaptive and non-adaptive mechanisms applied.In all the adaptive versus non-adaptive mechanisms compar-ison results, the adaptive mechanism applied on the metricsperform better than non-adaptive mechanism. Particularly, theproposed metric performance Fig. 6 shows higher throughputthan other metrics and better performance versus non-adaptiveand IFR variations. For both weighted user satisfaction (pro-posed metric) Fig. 6 and weighted fairness (existing metric)Fig. 7, the adaptation process is visible between 50 to 200

Fig. 4. Comparison of adaptive versus non-adaptive mechanisms for UserSatisfaction

Fig. 5. Comparison of adaptive versus non-adaptive mechanisms for FairnessIndex

seconds. However, despite common benefit of better reactionto the adaptation process, weighted user satisfaction performsbetter with higher throughput compared to weighted fairness.There is a co-relation between adaptation trend and inner-cellradius as shown in Sec. VI-C3.

3) Adaptive versus Non-adaptive mechanisms - SubcarrierAllocation and Inner-Cell Radius: Figs. 8 - 11 show effect ofadaptation process on subcarrier allocation to inner region forall metrics. Figs. 12 - 15 show effect of adaptation process on

Fig. 6. Comparison of adaptive versus non-adaptive mechanisms for WeightedUser Satisfaction

Fig. 7. Comparison of adaptive versus non-adaptive mechanisms for WeightedFairness

Fig. 8. Comparison of Subcarrier Allocation for User Satisfaction

inner region radius for all metrics Both subcarrier allocationand inner-cell radius show active response to the adaptationprocess applied to the network during the simulation time. Forweighted user satisfaction Fig. 10 and weighted fairness Fig.11, the subcarrier allocation reacts positively to the adaptationprocess between 50 and 200 seconds. High throughput trendduring this time interval can be explained due to 25 subcarrierallocation. Similarly, the range of inner-cell radius changeswith the adaptation process during the simulation time com-pared to its static trend in non-adaptive mechanism Figs. 14,15.

VII. CONCLUSION

In this paper, we evaluated the adaptation process inLTE FFR mechanism. The selected FFR mechanism basedon the optimal inner region radius and frequency alloca-tion performed better than other interference co-ordinationmechanisms. The FFR mechanism optimized using weightedthroughput user satisfaction made a positive trade-off betweenthe existing metrics, as it increased the cell total throughputand reduced the variance of per-user throughput values. Theproposed metric reacted quicker to the adaptation process inmobility scenarios and generated higher throughput comparedto the other metrics. With extreme densification in futuremobility, femtocells will be prevalent in wireless networks. A

Fig. 9. Comparison of Subcarrier Allocation for Fairness Index

Fig. 10. Comparison of Subcarrier Allocation for Weighted User Satisfaction

Fig. 11. Comparison of Subcarrier Allocation for Weighted Fairness

Fig. 12. Comparison of Range of Inner-cell for User Satisfaction

Fig. 13. Comparison of Range of Inner-cell for Fairness Index

Fig. 14. Comparison of Range of Inner-cell for Weighted User Satisfaction

good research extension would be to review femtocell impacton the adaptation process with the proposed metric.

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