Enhanced Resource Sharing Strategies for LTEPicocells with Heterogeneous Traffic Loads

Veronique Capdevielle, Afef Feki and Elias TemerAlcatel-Lucent Bell Labs, France

Email : {veronique.capdevielle, afef.feki, elias.temer}@alcatel-lucent.com

Abstract—In this paper, we address the problem of spectrumsharing for LTE pico cells (PCs) networks with heterogeneoustraffic loads. Distributed mechanisms are designed to steerthe PCs in selecting the most suitable resources so that theoverall throughput is maximized. The goal is to respond tothe real traffic needs, while ensuring users fairness. DynamicCooperative Algorithms (DCA) relying on information exchangebetween the PCs and Non Cooperative Algorithms (DNCA)relying only on local measurements are elaborated and comparedthrough extensive simulations. Different propagation conditionsand varied resource needs between the PCs are also considered.We demonstrate in this paper the performance gain brought bythe cooperative solution comparing to the non cooperative oneand we show the capability of the algorithm to dynamically adjustthe allocated resources to the real traffic loads.

Index Terms—Inter-Cell Interference (ICI), Pico Cell (PC),LTE, Distributed and Dynamic Algorithm.

I. INTRODUCTION

Today, it becomes a real challenge for industrials and mobileoperators to respond to the higher traffic demand and to ensuremore diverse services and higher quality for the end users.One of the most promising approaches is the deployment of3G/4G small radius cell networks. The main advantage ofsuch deployment is that it results in higher frequency reuse.This allows the increase of resources allocated to each userattached to a given pico cell. Nevertheless, the short distancesseparating the pico cells results in critical interference levels.Thus, the performances of such networks are highly impactedby Inter-Cell Interference (ICI) management mechanisms.In the literature, several techniques are proposed to handlethe ICI problem in the case of macro cells. One of the mostpopular is the frequency planning. In this case, the staticfrequency reuse (with for example a factor of 3) is not optimalas it doesn’t adapt to the real needs and traffic loads ofeach cell. In addition, the Fractional Frequency Reuse (FFR)scheme [1] results in improved static frequency usage. Infact, it differentiates the centre of the cell from the edge byallocating dissimilar resources. Unfortunately, this approach isnot applicable for pico cells due to their small radius.In addition, more advanced resource sharing mechanismsare depicted in the literature such as the schemes based onInterference graph [4] or game theory [2]. Game theory basedresource sharing models the resource allocation as the outcomeof a game. In [5], cooperative methods relying on informationexchange between the cells yield significant performanceimprovement comparing to non cooperative solutions, but at

the cost of high signaling overhead.To conclude, these approaches are difficult to implement inreal pico cells deployment due to their computational efforts.This paper is organized as follows. In section II, we describethe problem that we handle in this study. Section III describesdifferent approaches: cooperative, non-cooperative as well asa solution compliant with heterogeneous traffic loads. SectionIV presents the performance results and section V yieldsconcluding remarks.

II. PROBLEM FORMULATION

In this paper, we focus on the downlink of an OFDMAsystem that consists in a set of short radius LTE cells, calledPico Cells (PC). In this context of deployments densificationin a shared spectrum band, the main limiting factor to capacityincrease is interference. In this paper, we propose inter-cellinterference management schemes through optimized radioresource utilization.We focus on distributed mechanisms: each PC decides by itsown, in an autonomous way which resources it can transmiton. To take profit of higher spatial spectrum reuse, far-awayPCs that are not likely to interfere between each other must besteered towards reusing the same resources. To avoid resourcescollisions generating damaging interference and performancedegradation, close-by cells must be prohibited in reusing thesame resources.We distinguish between cooperative mechanisms for whichexplicit information exchange is enabled between PCs throughthe standardized X2 interface and non cooperative schemesaccording to which PCs decisions are taken only based onradio measurements only.

A. System model and Spectrum organization

The system is modeled as following. The network consistsof L Pico-Cells and M =

∑Ll=1 Ml users, where Ml denotes

the number of users that are served by PCl.In the context of LTE, the elementary time/frequency resourceis called Resource Block (RB). One RB is defined as a blockof physical layer resources that spans over one slot (0.5 ms)in time and over a fixed number of adjacent OFDM sub-carriers in the frequency domain (12 sub-carriers). Schedulingdecisions are taken every 1ms. The smallest resource that canbe allocated to a cell is then a pair of RBs (2 slots).In this work, we propose to split the whole spectrum band intoa set of contiguous physical resource blocks, called B-Bands.

978-1-4244-8331-0/11/$26.00 ©2011 IEEE

The smallest radio resource that can be allocated to a PC is aB-Band.

B. Formulation of the Resource assignment problem

The resource assignment task is seen as a hierarchicalprocedure that consists in:

• dynamic cell resource assignment by the PC itself toselect the most suitable B-Bands it will transmit on

• Per-user scheduling, to distribute the resource blockscommitted to the PC among its attached users.

In this paper, we address the problem of dynamic and dis-tributed frequency sharing algorithms. The resource assign-ment problem can be formulated as finding the most suitableset Sl of B-Bands for the pico cell PCl in order to:

• Maximize the overall throughput,• Ensure throughput fairness between the users,• Respond to the variable traffic demands of the users.

The B-Bands are selected so that the overall throughput ismaximized (and the SINR (Signal to Interference plus NoiseRatio) experienced by the users is maximized). By operatingon these interference-free B-Bands, the SINR level is enhancedover the whole cell, including at cell edge. In this way,schedulers that favor users in good radio conditions doesnot sacrifice cell edge users. Fairness between the users isenhanced jointly with capacity.In the following, the objective function to maximize is set-up. Fairness index is formulated through the relevant Jain’sfairness index.Objective function

maxZl�A

Zl∑k=1

{ 1Ml

Ml∑m=1

Rm,l,k} (1)

where A denotes the total number of B-Bands and Zl thenumber of B-Bands allowed to the cell l.{∑A

i=1 zl,i = Zl l = 1, . . . , L

zl,i ∈ {0, 1} (2)

Rm,l,k denotes the achievable data rate of the user-m attachedto the PCl over the B-Band k. The calculation of the through-put is based on the Shannon’s formula [3] :

Rm,l,k =nk∑j=1

B log2(1 + αγkm,l,j) (3)

where B denotes the RB’s band size (180 KHz), nk thenumber of RBs in a B-Band k, α is a constant for capacity gapdetermined by target bit error rate (BER) [3]. In this paper,we take α = 0.25.γk

m,l,j denotes the Signal to Interference and Noise Ratio(SINR) of the user-m associated to the PCl over the RBj

within the B-Band k, which is expressed as follows :

γkm,l,j =

Pl,jGm,l∑Ll′=1 Pl′,jGm,l′I(l, l′) + σ2

(4)

where Pl,j is transmit power of the PCl to the user-m in theRBj , Gm,l is the channel gain between the user-m and thePCl, and σ2 is the variance of the Additive White GaussianNoise.

I(l, l′) =

{1, if PCl and PCl′ are transmitting over the same RB.

0, Otherwise.(5)

Throughput Fairness indexJain’s fairness is defined as follows:

J =1M

(∑M

m=1 Rm)2∑Mm=1 R

2

m

(6)

J ranges from 1M (worst case) to 1 (best case), and the

maximum is reached when all the users receive the sameamount of radio resources.

III. DESCRIPTION OF THE PROPOSED SCHEME

A. Cooperative vs Non Cooperative solutions

In this section, we elaborate the solutions to the optimizationproblem introduced here above. For this purpose, dynamic anddistributed processes are designed to steer the PC in the choiceof the most suitable B-Bands based on information reportedby the users. Per sub-band Channel Quality Information (CQI:an LTE terminology), similar to Per sub-band SINR is used.In addition, on top of this, scheduling the resource updatingbetween the contenting cells plays an important role in theglobal resource allocation process for a perfect and permanentadaptation to varying radio conditions and traffic demands.Indeed, when a cell is updating its resources, the neighboringcells have to keep their own ones before triggering their ownupdate to account for radio changes in its neighborhood (newinterfering bands).We distinguish two types of algorithms: non cooperative andcooperative algorithm. We point out here that the cooperationis used only for scheduling the resource updating instant sothat neighboring cells do not update their resource occupationsimultaneously. Adding to that, the cell resource update istaken into account by the other cells. With the non-cooperativescheme, updating instants are randomly set-up according to aback-off mechanism.

1) Non Cooperative algorithm: In a non cooperative con-text, each PC independently chooses its resources and decideswhen to update them based on local measurements only. Aback-off period is picked up by the PC to determine theupcoming updating instant (as illustrated in Figure 1). Thebackoff follows a uniform law, with the following probabilityfunction:

g(tb) ={ 1

Tback−off0 ≤ tb ≤ Tback−off

0 otherwise(7)

Where Tback−off is the maximum backoff.

Fig. 1. Update instants of PCs using the back-off mechanism.

2) Cooperative algorithm: The cooperative version of thealgorithm differs from the non cooperative one, in the deter-mination of updating instants. Here, PCs periodically updatetheir resource (period T ), in a sequential way.Cooperation enables the PCs to have a common time-basedresource updating mechanism so that contenting PCs don’t si-multaneously process a resource updating and so that resourcechange in a PC will be accounted for by its contenting PCs.The sequential process can be implemented thanks to tokenpassing like protocol. Both algorithms (Cooperative and NonCooperative ones) start with an initialization phase, so eachPCl selects randomly a set of Zl B-Bands from the wholespectrum. Periodically, when the updating instant for a PCl

is reached, the following steps are applied :

• Step 1 : According to specific indicators calculated frommeasurements reported by the attached users, PCl calcu-lates the objective function f for each B-Bands k usingthe following formula :

f(l, k) =1

Ml

Ml∑m=1

Rm,l,k (8)

• Step 2 : PCl ranks the B-Bands according to the calcu-lated cost function.

• Step 3 : Chooses the Zl best B-bands.• Step 4 : The PCl receives the averaged SINR on the Zl

chosen B-Bands from its attached users (γl), then : If theaveraged γl is greater than a threshold experimentally set-up γth−max (for our simulations γth−max = 20 dB), thenumber of B-Bands Zl is increased, else if γl is lowerthan γth−min , we decrease the number of allowed B-Bands.

Algorithm

L : total Number of PCs.l : the index of the PC.Ml : the number of users connected to the PCl.A : the total number of B-Bands.Zl : the number of the B-bands allowed to the cell PCl

(initialized value = 1).S : the set of all the B-Bands.Sl : the set of the B-Bands allowed to the cell PCl.tu(l) : the update instant.t : the current instant.

tb(l) : the back-off period.Tback−off : the maximum value of the back-off.T : the The waiting period in the cooperative case.Rm,l,k : the achievable throughput of the userm attached to the PCl over the B-Band kFor a given PCl :1: Initialization :2: % Random selection of Zl B-Bands from the set S.3: Sl ← rand(Zl,S)4: % the next updating instant tu.

% the non-cooperative case.5: tu(l) = t + tb(l) where tb(l) ∈ [0 Tback−off ]

% the cooperative case.tu(l) = t + T

6: Resource updating7: if t = tu(l) do

% the non-cooperative case.8: tu(l) = t + tb(l) where tb(l) ∈ [0 Tback−off ]

% the cooperative case.tu(l) = t + T

6: % Calculate the cost function in all the B-Bands.7: for k = 1 to A do8: f(l, k) = 1

Ml

∑Ml

m=1 Rm,l,k

9: end for10: % Choose the Zl best B-Bands.11: Sl ← argmaxk∈Af(l, k)12: Card(Sl) = Zl

13: % The other cells keep the old resources14: Sl′ �=l = the old Sl′ �=l

15: Update Zl :16: % γl : averaged SINR over all users

attached to PCl and over Sl

17: if γl > γth−max

18: Zl=Zl+1.19: elseif γl < γth−min

20: Zl=Zl-121: end if22: end if

B. Enhancements in the case of Heterogeneous Traffic Loads

In the realistic case of heterogeneous traffic loads, it is ofmajor importance to adjust the PCs resources to their actualloads. In this paper, the load is assimilated to the number ofusers with equivalent required data rate to be served by a PC.For this purpose, the procedure of updating the number ofB-Bands described in the preceding section is upgraded toaccount for the real PC loads. For this purpose, instead ofincreasing/decreasing the number of B-Bands devoted to PCl

by one unit, we increase/decrease with a step θ proportionalto the load of the PCl :

θ =[λ

Ml

M

](9)

where [.] is the function of rounding (to get integer values),Ml is the number of users attached to the PCl, M is the totalnumber of users in the scenario (each PC get the information

concerning the number of users in the neighboring cells bycooperation) and λ denotes the maximum number of B-Bands.So the last part of the algorithm becomes :

1: Update Zl :2: % γl : averaged SINR over all users attached toPCl and over Sl

3: if γl > γth−max

4: Zl=Zl+θ.5: elseif γl < γth−min

6: Zl=Zl-θ7: end if

In this way, PCs with high load will more rapidly preempt thespectrum resources necessary to respond to high level of trafficdemand while PCs with low loads will share the remainingspectrum resources.

IV. PERFORMANCE EVALUATION

A. Methodology

Performance is provided thanks to a complete simulationplatform that combines a physical layer 3D ray-tracing tool toanalyze propagation in complex environments including build-ings structures with an upper layer LTE compliant simulatorimplementing the resource allocation components: the spec-trum sharing algorithms here introduced and the proportionalfair users’ scheduling. The performance of the algorithmsdeveloped in this paper will be evaluated and compared interms of post-scheduling SINR, averaged user’s throughputor the aggregated throughput (the sum of the averaged user’sthroughput for all the attached users in the cell) and interms of fairness through the Jain’s Fairness index previouslyintroduced. Performances are investigated either in Free Spaceenvironments to simulate severe interference conditions with3 or 10 Pico Cells regularly deployed in a 350m x 150 m areaspace or in an urban-like environment with buildings separatedby streets. In this paper, we consider a scenario with 9 PCsdeployed in 550m x 300m area.

B. Results

1) Cooperative vs Static: The main outcomes of a compari-son of the Dynamic Cooperative Algorithm (DCA) to the staticreuse schemes can be summarized as following. DCA enablesself-configuration of the spectrum reuse. In the case of 3 PCsdeployed in Free Space, the optimum reuse 3 is reached in anautonomous way. In addition, , 5th percentile post schedulingSINR exceeds 5dB in all cases with DCA thus guaranteeinggood performance, including at cell edge (whereas it is lowerthan -4 dB in all the investigated cases with Reuse 1).Minimum grade of service can be guaranteed with DCA,including at cell edge whatever the scenario. Finally, theaggregate throughput is significantly improved wrt Reuse 1(as illustrated in Figure 2) by more than 50% with respect tothe static Reuse 1. In the same time, the users’ fairness index

is significantly increased by a factor 2.5 with DCA in thissame scenario.

Fig. 2. Aggregate cells throughput. 10 PCs in Free space.

2) Cooperative vs Non Cooperative: In this paragraph, theperformances of DCA are compared with DNCA in order toquantify the gain brought by cooperation. Figure 3 and 4 re-spectively represents the CDF of the average users’ throughputfor respectively 3 and 10 PCs in Free Space.We observe that the 5th percentile of the average throughput(which corresponds to cell edge users), significantly increasesfrom 0.15 Mbps (with DNCA-Tback−off =50ms) up to 1.43Mbps with the cooperative mechanism in the case of 3contenting PCs scenario and from 6.76 kbps up to 162 kbpsin the dense scenario case of 10 PCs. Cooperative algorithmoutperforms the non cooperative one in most cases. Manysimulations have been conducted to assess the impact ofTback−off . As illustrated by Figure 5 comparing the perfor-mance of DNCA for two backoff values (50ms and 150ms)in the urban scenario case, long backoff windows degrade theperformance. Indeed, the higher Tback−off , the higher is theprobability that some PCs update their resources too manytimes while others operate on their old, sub-optimal resourcesfor a long time, which degrades their performances. Thus,DNCA has to be configured properly in order to reach theDCA performances.

Fig. 3. CDF of average user’s throughput. 3PCs in Free Space.Tback−off =100ms.

3) Load-Aware in heterogeneous Traffic Loads: In thissection, we analyze the performance of the load-awareresource allocation algorithm with heterogeneous traffic loadsof 3 PCs in Free Space. The traffic load is distributed in thisway: 7.7%, 83.3% and 8.8% for respectively PC1, PC2 andPC3. The Increasing/decreasing step in B-bands is set upaccordingly with λ = 3 B-Bands. The spectrum plan obtainedafter about 100 slots is reported in Figure 6.As expected, PC2 with highest traffic load has been allocated

80% available resources while PC1 and 3 that are lessdemanding share the remaining resources.

Fig. 4. CDF of average user’s throughput. 10PCs in Free Space.Tback−off =50ms.

Fig. 5. CDF of average user’s throughput. 9 PCs in Urban scenario.Tback−off =50ms (First) and 150ms (Second).

Besides, the benefits of the adaptive increment-ing/decrementing process are assessed in Figure 7 illustratingthe average user’s throughput obtained with Adaptive and NonAdaptive DCA. It can be seen that more than 50% of usershave an averaged throughput exceeding 2 Mbps, 1.155 Mbpsand 0.59 Mbps respectively with the adaptive DCA, non-adaptive DCA and with a static Reuse 1 scheme. Regardingthe performances of the edge users (5th percentile of theaveraged throughput CDF), the adaptive DCA enhances theaveraged throughput from 72.07 Kbps (with the non-adaptiveDCA) up to 342 Kbps.

V. CONCLUSION

In this paper, we propose an innovative approach forspectrum sharing in dense deployments of short radius cells:Pico Cells. Resources are selected and updated by each PCin an autonomous way. The goal is to optimize the overallthroughput and respond to the variable cells resource needs,while ensuring users fairness. We highlight the necessity toschedule the updating instants between the cells so that eachresource selection change is taken into account by surroundingPCs. This guarantees a steady up to date resources allo-cation. In addition, we take care that contending cells donot perform resource allocation decision simultaneously, toenhance convergence issues. For this purpose, two procedures

Fig. 6. Spectrum planning in heterogeneous traffic load.

Fig. 7. CDF of the average user’s throughput (3PCs Free Space).

are proposed. The non cooperative algorithm only relies onlocal measurements: it implements back-off like mechanism todetermine updating times. The cooperative solution relies oncapability exchange between the cells through a token passinglike procedure for triggering updating operation. We showthat the cooperative solution significantly outperforms the noncooperative one. However, it is important to point out that theperformance gain of cooperative implementation is counter-balanced by extra signaling between the PCs. Furthermore,we elaborate a self-adaptive spectrum reuse procedure throughan increasing/decreasing spectrum allocation mechanism thatdynamically adjusts the selected resources to the real needs.Thanks to this load-aware resource allocation process, wedemonstrate that a high proportion of resources is devotedto the most demanding cells. The other ones fairly share theremaining resources.

ACKNOWLEDGMENTS

This work was supported by the CELTIC project HOME-SNET. The authors would like to thank Matthew Andrews(Alcatel-Lucent Bell Labs USA) for his helpful comments.

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