System Level Performance Evaluation of Inter-cell ...

System Level Performance Evaluation of Inter-cell Interference Coordination Schemes for Heterogeneous Networks in LTE-A System Young-Jun Hong, Namyoon Lee, and Bruno Clerckx Samsung Electronics Co., Ltd. San 14-1 Nongseo-dong, Giheung-gu, Yongin-si Gyeonggi-do 446-712, Republic of Korea Email: [email protected], [email protected], [email protected] Abstract—3GPP has recently approved a new work item for LTE-A to support enhanced inter-cell interference coordination (eICIC) for co-channel deployments of heterogeneous networks. The main focus is on the coordination of control and data channel interference between macro and low power nodes, e.g., relay, pico, and femto [1]. Since the femto cells are randomly deployed by consumers with closed access and X2 interface is not available [2], the femto cell may introduce some excessive interference to the macro cell on the downlink and/or the uplink. In this paper, we aim to clarify the ICIC triggering event to protect a victim user equipment (UE) in the vicinity of an aggressive femto cell and evaluate macro/femto system level performance to show the severity of the downlink dead-zone problem and the benefits of dynamic/static ICIC schemes based on time/frequency silencing.

I. I NTRODUCTION For next generation broadband wireless communication systems, various demands such as high data-rate transmission, high spectral/power efficiency, a wide range of quality of service (QoS), improved spectrum compatibility/flexibility, and enhanced cell average/edge performance are primarily considered as system design requirements. Recently, the International Telecommunication Union Radiocommunication Sector (ITU-R) has defined the key features and evaluation guidelines for International Mobile TelecommunicationsAdvanced (IMT-A) [3] and has issued a Circular Letter (CL) to invite submission of IMT-A proposals. To fulfill the baseline requirements of Radio Interface Technology (RIT) for IMT-A, the multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) based air interfaces have been introduced, e.g., 3GPP LTE-Advanced (LTE-A) and IEEE 802.16m (WiMAX). 3GPP has investigated several evolutionary technologies as part of the Study Item for LTEA, i.e., carrier aggregation, downlink/uplink enhanced MIMO, coordinated multi-point transmission and reception (CoMP), relaying, and heterogeneous networks (HetNet) [2]. In general, although a single-cell OFDM network is able to reject the intra-cell interference while maintaining the orthogonality among users, a multi-cell OFDM network is vulnerable to inter-cell interference. Therefore, inter-cell interference coordination (ICIC) technologies play an important role in any cellular system like LTE [4]. In the homogeneous networks, several ICIC schemes have been proposed to significantly enhance the cell edge performance, e.g., fractional

frequency reuse (FFR) [5], soft frequency reuse (SFR) [6], and wireless dynamic spectrum management (DSM). The intercell interference is controlled in a centralized or a distributed manner based on the cell planning and message exchanges, respectively. However, in the heterogeneous networks, since femto cells are randomly deployed by consumers without coordination and since they cannot rely on the X2 interface [2], the femto cell causes severe downlink and uplink interference to adjacent cells, i.e., the downlink dead-zone and uplink blocking problems [7]–[9]. Therefore, to make the inter-cell interference controllable, the femto should be able to recognize the interference level in the cognition phase and perform ICIC by adjusting the femto parameters in the optimization phase [10], [11]. In this paper, we aim to evaluate the severity of the downlink dead-zone problem and the benefit of dynamic/static ICIC scheme based on time/frequency silencing. The remainder of this paper is organized as follows. In Section II, we describe the system model for heterogeneous networks. In Section III, we clarify the ICIC triggering event to protect the victim UE and provide additional details on the information sharing procedure to support dynamic/static ICIC functions. Performance evaluation of the dynamic/static ICIC schemes is discussed in Section IV. Finally, concluding remarks are presented in Section V. II. S YSTEM M ODEL We assume that K user equipments (UEs) and nf femto cells, denoted as home eNBs (HeNBs) in LTE-A, are uniformly distributed in the coverage area of nm macro eNBs (MeNBs) where K , Km +Kf and nm , nc ns . Km and Kf denote the number of macro UE (MUE) and home UE (HUE), nc and ns denote the number of cell-sites and sectors for macro deployment, respectively. The femto deployment follows the apartment model for dense urban evaluation scenario as in 3GPP evaluation methodology. Details are listed in the Table I. The k-th user can be either MUE or HUE, depending on whether the user is a member of closed subscriber group (CSG) of HeNB or not, i.e., k ∈ Km (non-CSG) or Kf (CSG). For NT × NR MIMO channels, we consider a general system equipped with transmit beamforming (˜ xi = Fi xi ) and ˜ k ) where xi and yk denote the receive shaping (yk = Gk y transmitted and received symbols, and Fi and Gk denote the

TABLE I: Macro/Femto system model parameters.

precoding matrix and shaping matrix, respectively. Without loss of generality, we note that the i-th cell transmits Li layers with Nt transmit antennas and k-th user receives with Nr receive antennas. Then, the received signal of the k-th user from the i-th cell is expressed as: X 1/2 1/2 1/2 1/2 y ˜k,i = αk,i Hk,i Si x ˜i + αk,j Hk,j Sj x ˜j + n ˜ k (1)

Parameter Macro cell layout Femto cell deployment Carrier frequency System bandwidth Inter-site distance Transmit power

j6=i

where Hk,i ∈ C Nr ×Nt and n ˜ k ∈ C Nr represent the discrete MIMO fading channel and complex Gaussian noise with variance N0 , respectively, and we omit the time and frequency reference (s, c) for the given s-th symbol and c-th subcarrier relative to yk,i , xi , Fi , Gk , Si , and Hk,i for notational brevity. A large-scale fading model is defined as αk,i , L(k, i)10χk,i /10 βk,i where L(k, i) ∼ K0 d−µ is a distance dependent path loss model, determined by the locations of the k-th user (outdoor/indoor) and the i-th cell (macro/femto) from the dual-strip model [12], K0 is a constant factor, d is a distance, µ is a path loss exponent, χk,i ∼ N (0, σp2 ) is the log-normal shadowing with shadowing deviation σp [dB], and βk,i is the additional loss factor, considering the antenna gain and the cable loss. In addition, there is a total power budget Pi for the i-th cell, either MeNB or HeNB, and Ei denotes the corresponding transmitted energy per symbol duration. The power allocation matrix is denoted by Si = diag(si,1 , si,2 , · · · , si,Li ) ∈ RLi ×Li , satisfying tr(Si ) ≤ 1 and each transmitted symbol has equal symbol energy and covariance matrix Rxi = E[xi xH i ] = Ei ILi . Assuming the uniform power allocation among Li layers, Ns symbols and Nc carriers in time/frequency/spatial domains, i.e, Si (s, c) = 1/Li · ILi , ∀s and ∀c, the receive signal-to-interference-and-noise ratio (SINR) for the n-th layer yields to Γnk,i = P m6=n

2 |hn,n k,i | Ei /Li P 2 2 |hn,m |hn,m k,j | Ej /Lj + N0 k,i | Ei /Li +

(2)

∀m j6=i

T Nt where hn,m and gkT n ∈ k,i , gk n αk,i Hk,i fk,i m , fk,i n ∈ C Nr C are transmit beamforming and receive shaping vectors with unit norm, i.e., kfk,i n k = 1 and kgkT n k = 1, respectively. The first and second terms in the denominator of (2) represent the intra-cell interference from multi-layer transmission and the inter-cell interference from macro/femto deployment. Finally, the achieved data rate for the k-th user writes as à à !! X −1 n J (Γk,i (s, c)) (3) rk = B log2 1 + J n,s,c

where B denotes the number of channel use in the scheduled subframe and resource block (RB) and J (·) and J −1 (·) represent the effective SINR mapping function for link abstraction. III. E NHANCED I NTER - CELL I NTERFERENCE C OORDINATION FOR F EMTO C ELLS A. HeNB ICIC triggering condition: Victim UE awareness When a non-CSG UE is located in the vicinity of HeNB, the harsh interference from HeNB will block both data transmis-

Penetration loss Thermal noise density Path loss model1 Fading model Shadowing model Standard deviation Correlation Antenna model2 Antenna gain Antenna height UE noise figure UE distribution3 Minimum separation Mobility Network synchronization Handover margin TTI (subframe) length 1 2 3

Explanation/Assumption 2-tier cellular system with wrap-around Hexagonal grid, 3-sector site (19 sites) Bore-sight points toward flat side Frequency reuse 1 with macro No X2 interface, closed subscriber group Placed indoor, consumer deployed 2 GHz FDD: 10 MHz (downlink only) 500 m eNB: 46 dBm, HeNB: 20 dBm 20 dB (outdoor) 20/5 dB (exterior/interior walls) -174 dBm/Hz Dual-strip model Spatial channel model (urban macro and low angular spread) Log-normal shadowing 4 dB (HeNB and CSG UE), 8 dB (others) eNB: 0.5/1.0 (site/sector), HeNB: 0 eNB: 3-sector, HeNB/UE: omni-directional eNB: 14 dBi, HeNB: 5 dBi, UE: 0 dBi eNB: 32 m, HeNB: 3.5 m, UE: 1.5 m 9 dB Uniform distribution (eNB: 10 UE, HeNB: 1 UE) 35 m (eNB and HeNB/UE) 3 m (HeNB and UE) 3 km/h Synchronized 1 dB 1 ms

Indoor femto channel models: dense urban deployment [12]. 3D antenna pattern for 3GPP case 1 [2]. Macro UE: dropped within the indoor/outdoor macro coverage area with the probability of being placed indoor 6% approximately. Home UE: dropped within the indoor femto coverage area.

sion and control signalling, and the resulting outage area due to the excessive interference from HeNB is generally known as the downlink dead-zone (or the coverage hole of macro cell). In order to address this problem, we need to clarify the HeNB ICIC function triggering condition first. Without any knowledge on the existence of a victim UE, the HeNB ICIC function may inappropriately sacrifice the HeNB performance. Since the femto cells are deployed by consumers for their own sake, the HUE will have a high tendency to request high performance. In the absence of a victim UE, sacrificing HeNB resources would therefore not be acceptable. On the other hand, macro/femto cells have to be aware of the presence of a victim UE. Once the UE is in a dead-zone, either MeNB or HeNB cannot easily rescue the victim UE without any pre-defined evacuation procedure. For this reason, both MeNB and HeNB should ensure that the UE is able to communicate and whenever the UE approaches the dead-zone, they should immediately recognize the event to trigger the enhanced ICIC function. The aggressive HeNB set Fk for the k-th user is represented as follows: •

Static ICIC triggering condition ¾ ½ ¯ . ¯ αk,i Ei < δ, ∀j 6= i Fk = j ¯ αk,j Ej

(4)

•

Dynamic ICIC triggering condition ½ ¯ ¾ . ¯ αk,i kHk,i k2 Ei Fk = j ¯ < δ, ∀j = 6 i αk,j kHk,i k2 Ej

(5)

. where the HeNB ICIC active set is denoted by Fa = S k∈Ka Fk and Ka is the active user set for the specific s or (s, c), respectively. Alt 2. ICIC via over-the-air broadcasting

where δ denotes the HeNB ICIC triggering threshold. Then, the ( existence of a victim UE k is characterized by bk = 1, k ∈ Kv . where the victim UE set is denoted by Kv = 0, k ∈ / Kv {k|Fk 6= ∅}.

Alt 1. ICIC via UE relaying

Home eNB Home UE Macro eNB

Macro UE

Victim UE

Dead-zone

B. Static ICIC function: Frequency-domain silencing When the probability of being placed in the vicinity of HeNB and the deployment ratio of HeNB are low, the resulting outage probability will be negligible. In this case, we can rely on the static ICIC function without any huge sacrifice from the HeNB since the ICIC event rarely occurs. As far as the performance of HeNB reaches a certain level, the HeNB can allocate silencing resources in time/frequency domains, i.e., escape carrier [13] or blank subframe [14]. An evacuation procedure is predefined where MeNB and HeNB make a reservation of the silencing resources in a static manner via S1 signaling. In addition, the behavior of the inter-cell interference should be controllable, considering the loose backhaul latency of Digital Subscriber Line (DSL) connection. Thus, the allocated power and the number of active subcarriers are determined by the femto utilization ratio η ∈ (0, 1] and the synchronous frequency-domain silencing is given as follows: ( 1 IL , c ≤ bηNc c Si (s, c) = ηLi i , ∀s and ∀i. (6) 0, otherwise C. Dynamic ICIC function: Time-domain silencing When the deployment ratio of HeNB is high and, thus, the outage probability is noticeable, the static ICIC function may not be a promising solution to cope with the dynamic behavior of inter-cell interference in heterogeneous networks. In this case, we can consider direct extensions of legacy ICIC function in the LTE system, relying on X2 signaling: Relative Narrowband Tx Power (RNTP), Overload Indicator (OI) and High Interference Indicator (HII) [4]. To exchange X2 signaling, we need to have some alternative backhaul instead of the X2 interface. In [15], two alternatives are identlified: UE-assisted relaying and over-the-air broadcasting, as shown in Fig. 1. Basically, those approaches can provide a tight level of coordination between MeNB and HeNB and, thus, dynamic ICIC function can fully exploit potential gain of HeNB without causing victim UE. For this reason, the victim UE can dynamically request temporal silencing to the aggressive HeNB and dynamic time or time-and-frequency domain silencing is given as follows: Time : Si (s, c) = 0,

∀c, ∃s and ∃i ∈ Fa (7) ( 0, ∃c, ∃s and ∃i ∈ Fa Time-and-frequency : Si (s, c) = 1 Li ILi , otherwise (8)

Aggressors

DSL gateway

ICIC via X2 interface

Fig. 1: Alternative wireless backhaul for dynamic ICIC function: UE relaying and over-the-air broadcasting. In the following section, numerical results are presented to show the severity of the downlink dead-zone problem and the benefits of a static/dynamic ICIC scheme based on time/frequency-domain silencing. Simulation assumptions, compliant with 3GPP LTE-A, are listed in Table II. TABLE II: LTE-A system-level simulation assumptions. Parameter Antenna configuration Subband size Scheduling Resource allocation Transmission mode Modulation and coding Link abstraction Hybrid ARQ

Feedback

Link adaptation Overhead Traffic model

Explanation/Assumption 4 × 2 uniform linear array with 0.5 λ spacing 50 RB (wideband), 6 RB (subband) Proportional fair in time/frequency domains RB-level indication Single-user MIMO MCS based on LTE transport formats Exponential effective SINR mapping Chase combining, non-adaptive/asynchronous Maximum 3 retransmissions RI (wideband): 2 bit PMI (wideband/subband): 4 bit LTE codebook CQI (wideband/subband): 4 bit CQI 5 ms (period), 6 ms (delay) No measurement/feedback errors Target block error rate: 10 % (ACK: +0.5/9 dB, NACK: -0.5 dB) CCH: 3 OFDM symbols, LTE R8 CRS: 16 RE Full queue (backlogged)

IV. N UMERICAL R ESULTS A. Geometry Analysis Fig. 2 shows the impact of the deployment ratio on the wideband SINR distribution for both MUE and HUE. It shows that the geometry of MUE is drastically degraded in the dead-zone and the resulting wideband SINR becomes much less than the outage threshold, c.f., -12 dB for data channel considering the lowest MCS and maximum 4 retransmissions and -8 dB for control channel satisfying target block error rate ≤ 1% [16]. As the deployment ratio (DR) increases, the geometry of MUE does not change significantly because a limited number of dominant HeNBs gives a large portion of interference and the remaining interferences are negligible. However, the geometry of HUE is significantly degraded due to the increase of interference level from HeNBs and the asymmetric shift is observed since the impact of interior/exterior wall losses are

depending on the position of interferer and the number of walls to be penetrated, as described by the dual-strip model [12].

0.6

FL=1, DR=10% FL=1, DR=25% FL=1, DR=50% FL=1, DR=75% FL=1, DR=100% FL=1, DR=0% (baseline)

0.4 0.2 0 −80

−60

−40

−20 0 MUE wideband SINR [dB]

20

40

(a) Cumulative density function

1 0.8 0.6 0.4 0.2

FL=1, DR=2.5% FL=1, DR=5% FL=1, DR=10% FL=1, DR=20% FL=1, DR=30% FL=1, DR=40% FL=1, DR=60% FL=1, DR=80% FL=1, DR=100%

1 0.9 0.8

0 −40

−20

0

20 40 HUE wideband SINR [dB]

60

80

(b)

Fig. 2: Wideband SINR distribution for various femto deployment ratios. (a) Macro UE. (b) Home UE.

Cumulative density function

Cumulative density function

1 0.8

III. In addition, the HUE throughput is degraded due to the increase of inter-cell interference among HeNBs. However, as we expected, the cell area throughput increases significantly since the cell-splitting gain of HeNB increases. Table III shows the benefits of a dynamic ICIC scheme based on the time-domain silencing. We assume that HeNB ICIC function is triggered when the interference level shows 10 dB difference 1 compared to the signal strength. In the sparse and dense deployment scenario, we can observe that time-domain silencing provides 7 % gain (2.02 → 2.16) and 20 % gain (1.77 → 2.14) in terms of cell average throughput and the outage behavior is completely avoided when a simple ICIC function is enabled.

0.6 0.4

−10

0

0.4 0.3 FL=6, DR=0% (baseline) FL=6, DR=5% FL=6, DR=10% FL=6, DR=20% FL=6, DR=30%

0

0

20 30 40 HUE wideband SINR [dB]

50

60

70

80

(a)

0.2

0.3 0.4 MUE throughput [bps/Hz]

0.5

0.6

0.7

1 FL=6, DR=5% FL=6, DR=10% FL=6, DR=20% FL=6, DR=30%

0.8

10

0.1

(a)

0.9

FL=1, DR=5% FL=2, DR=5% FL=4, DR=5% FL=6, DR=5% FL=8, DR=5% FL=10, DR=5%

0.2 0 −20

0.5

0.1

1 0.8

0.6

0.2

Cumulative density function

Cumulative density function

Fig. 3 shows the impact of multiple floors (FL) on the wideband SINR distribution of HUE and it shows that the interference from different floors is also prominent especially in the sparse deployment scenario and the results with 6∼10 floors are almost identical, regardless of the deployment ratio. Therefore, we will consider only a 6 floor model with varying the deployment ratio in the following results.

0.7

0.7 0.6 0.5 0.4 0.3

Cumulative density function

0.2

1 0.8 0.6 0.4

FL=1, DR=20% FL=2, DR=20% FL=4, DR=20% FL=6, DR=20% FL=8, DR=20% FL=10, DR=20%

0.1 0

0.2 0 −40

0

0.5

1

1.5 2 HUE Throughput [bps/Hz]

2.5

3

3.5

(b) −20

0

20 40 HUE wideband SINR [dB]

60

80

(b)

Fig. 3: Home UE wideband SINR distribution for various femto floors. (a) Sparse deployment. (b) Dense deployment. B. Throughput Analysis Fig. 4 shows the severity of the outage behavior of MUE without HeNB ICIC function. As the deployment ratio increases, we observe that the cell-edge user performance of macro cell is significantly degraded. The outage probability becomes approximately 13.5 %, which coincides with the ratio of HeNB cluster area to cell sector area. A cell average throughput loss of 18 % (2.16 → 1.77) is observed in Table

Fig. 4: Throughput distribution; no ICIC function. (a) Macro UE. (b) Home UE. Fig. 5 shows the benefits of a static ICIC scheme based on the frequency-domain silencing. We summarize the trade off relationship between performance improvement of MUE and sacrifice of HUE by varying the utilization ratio and the deployment ratio. The main observation is that as the HeNB utilization ratio decreases, HUE throughput decreases significantly since HeNB can not use certain resources. Meanwhile, the frequency-domain silencing improves MUE 5% throughput 1 In [17], the 10 dB difference is recommended in that it requires 50 % and 25 % additional uplink overhead for inter/intra-cell multi-cell feedback, considering the performance improvement for the cell-edge users.

TABLE III: Macro/Femto system performance: dynamic ICIC function. DR

ICIC

0%

Off On Off On Off On Off On

5% 10% 20% 30% 1

Avg. 2.16 2.08 2.16 2.02 2.16 1.83 2.15 1.77 2.14

Macro cell 5% user 0.07 0.06 0.07 0.03 0.07 0 0.07 0 0.07

Femto cell Avg. Util. 3.19 100 % 3.18 99.7 % 2.89 100 % 2.88 99.7 % 2.47 100 % 2.46 99.7 % 2.09 100 % 2.08 99.7 %

Out.1 0% 1.6 % 0% 3.3 % 0% 8.6 % 0% 13.5% 0%

Area 2.16 40.4 40.4 71.5 71.4 120.6 120.5 152.2 152.0

Outage probability = Pr(Rk ≤ λth ) where Rk denotes the k-th user throughput and λth = 0.01 [bps/Hz].

but degrades HUE throughput. As the HeNB deployment ratio increases, the cell-edge performance gain becomes prominent. Hence, it shows that the performance improvement of victim MUE throughput comes with the sacrifice of HUE throughput. As the HeNB deployment ratio increases, HUE throughput decreases drastically since the frequency-domain silencing protects only victim MUE. However, the inter-HeNB interference problem is still significant and further investigations are required. For the better use of femto cell, we need to expand the objective of HeNB ICIC from victim MUE to victim HUE as well. 1 FL=6, DR=10%, η=0.5 FL=6, DR=10%, η=0.7 FL=6, DR=10%, η=0.9 FL=6, DR=10%, η=1.0 FL=6, DR=30%, η=0.5 FL=6, DR=30%, η=0.7 FL=6, DR=30%, η=0.9 FL=6, DR=30%, η=1.0

0.9

Cumulative density function

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

0.5

1

1.5 2 HUE throughput [bps/Hz]

2.5

3

3.5

(a) 3 η=1

HUE average throughput [bps/Hz]

2.5

η=1

η=0.9

2 η=0.7 η=0.9

1.5

η=0.5

η=0.7 1 η=0.5 0.5

0

0

0.02

η=0

η=0

FL=6, DR=30% FL=6, DR=10% 0.04 0.06 0.08 MUE 5% throughput [bps/Hz]

0.1

0.12

(b)

Fig. 5: Trade-off relationship between macro and femto; static ICIC function. (a) Home UE. (b) Macro/Home UE.

V. C ONCLUDING R EMARK In this paper, we clarify the ICIC triggering event to protect victim UE in the vicinity of an aggressive HeNB and describe the information sharing procedure to support dynamic/static ICIC function. To show the severity of the downlink deadzone problem and the benefit of dynamic/static ICIC scheme based on time/frequency-domain silencing, we evaluate the macro/femto system level performance in the context of a LTEA system. It is observed that as the deployment ratio of HeNBs increases, the cell-edge performance of MUE is significantly degraded. On the other hand, if the HeNB ICIC function is enabled, the MUE outage behavior is completely avoided at the expense of HeNB performance. Therefore, to overcome the dead-zone problem, we need a dynamic/static ICIC scheme based on the time/frequeny-domain silencing. Such approach can be a promising solution for femto deployment while minimizing the unnecessary sacrifice of HeNB. R EFERENCES [1] New Work Item Proposal: Enhanced ICIC for non-CA based Deployments of Heterogeneous Networks for LTE, 3GPP TSG-RAN RP100 383, Mar. 2010. [2] Further Advancements for E-UTRA Physical Layer Aspects (Release 9), 3GPP TR 36.814, v. 2.0.0, Mar. 2010. [3] Guidelines for Evaluation of Radio Interface Technologies for IMTAdvanced, ITU-R Report M.2135, Nov. 2008. [4] G. Fodor, C. Koutsimanis, A. R´acz, N. Reider, A. Simonsson, and W. M¨uller, “Intercell interference coordination in OFDMA networks and in the 3GPP Long Term Evolution system,” Academy Publisher Journal of Communications, vol. 4, no. 7, pp. 445–453, Aug. 2009. [5] A. L. Stolyar and H. Viswanatha, “Self-organizing dynamic fractional frequency reuse in OFDMA systems,” in Proc. IEEE INFOCOM, Phoenix, AZ, U.S.A., Apr. 13–18, 2008, pp. 691–699. [6] M. Bohge, J. Gross, and A. Wolisz, “Optimal power masking in soft frequency resue based OFDMA networks,” in European Wireless Conference, Aalborg, Denmark, May 17–20, 2009, pp. 162–166. [7] V. Chandrasekhar, J. Andrews, and A. Gatherer, “Femtocell networks: a survey,” IEEE Commun. Mag., vol. 46, no. 9, pp. 59–67, Sep. 2008. [8] V. Chandrasekhar and J. Andrews, “Spectrum allocation in tiered cellular networks,” IEEE Trans. Commun., vol. 57, no. 10, pp. 3059–3068, Oct. 2009. [9] S. ping Yeh, S. Talwar, S.-C. Lee, and H. Kim, “WiMAX femtocells: a perspective on network architecture capacity, and coverage,” IEEE Trans. Commun., vol. 46, no. 10, pp. 58–65, Oct. 2008. ´ [10] D. L´op´ez-Perez, Akos Lad´anyi, A. J¨uttner, and J. Zhang, “OFDMA femtocells: A self-organizing approach for frequency assignment,” in Proc. IEEE PIMRC, Tokyo, Japan, Sep. 13–16, 2009, pp. 2202–2207. [11] D. L´opez-P´erez, A. Valcarce, G. de la Roche, and J. Zhang, “OFDMA femtocells: A roadmap on interference avoidance,” IEEE Commun. Mag., vol. 47, no. 9, pp. 41–48, Sep. 2009. [12] Simulation Assumptions and Parameters for FDD HeNB RF Requirements, 3GPP TSG-RAN WG4 R4-092 042, May 2009. [13] Macro+HeNB performance with escape carrier or dynamic carrier selection, 3GPP TSG-RAN WG1 R1-101 924, Apr. 2010. [14] Interference coordination for non-CA-based heterogeneous networks, 3GPP TSG-RAN WG1 R1-102 307, Apr. 2010. [15] Static/Dynamic home eNB ICIC function, 3GPP TSG-RAN WG1 R1103 048, May 2010. [16] Effect of cell association and frequency allocation with and without FFS and BF - indoor HeNB cluster scenario, 3GPP TSG-RAN WG1 R1-100 187, Jan. 2010. [17] Performance evaluation of CoMP CS/CB, 3GPP TSG-RAN WG1 R1101 173, Feb. 2010.