A Power Allocation Scheme for Achieving High Energy Efﬁciency in Two-tier Femtocell Networks Bin Han, Wenbo Wang and Mugen Peng Wireless Signal Processing and Network Laboratory Key Laboratory of Universal Wireless Communication, Ministry of Education Beijing University of Posts and Telecommunications, Beijing, 100876, China Email: [email protected] Abstract—Two-tier networks consisting of femtocells and macrocells is a hot research topic since it can provide high spectral efﬁciency and low power consumption. In this paper, we investigate the energy efﬁciency problem for green communications in two-tier femtocell networks when macrocells and femtocells share the same frequency spectrum. This paper describes the radio base station (RBS) power consumption model and introduces a novel energy efﬁciency metrics - telecommunications energy efﬁciency ratio (TEER). Based on the power consumption model and energy efﬁciency metrics, we propose a power allocation scheme for achieving high energy efﬁciency. Validated by simulation results, the proposed scheme can improve the performance of both TEER and average rate compared with equal power allocation scheme.

I. I NTRODUCTION Currently, concern regarding climate warming caused by accumulation of greenhouse gas (primarily carbon dioxide CO2 ) has been a signiﬁcant increase. According to [1], it is shown that information and communications technologies (ICT) infrastructure accounts for about 3%-4% of the worldwide energy (approximately, 1.5%-2% by wireless). Likewise, the exponential growth of wireless communications requires more and more wireless infrastructure which signiﬁcantly increases energy consumption. This growth will lead to that energy consumption of ICTs increases by approximately 16% to 20% in every ﬁve years [1]. As a result, ICTs has become a major contributor to overall power consumption and green house gas emissions. Therefore, green communications, which aim to improve the energy efﬁciency and decrease the carbon emission for future communication networks, has received more and more attention for researchers and wireless industry. Telecommunication operators as well as researchers have become aware of this energy issue, and have begun to study “green communications”. Recently, some energy efﬁcient strategies for limited battery life terminal has been studied in [2] [3] [4]. However, through the analysis in [5], it reveals that the radio access network (RAN) consumes the most energy among wireless equipments. Therefore, it is necessary to investigate the downlink energy efﬁcient strategy since base stations (BSs) are the primary energy consumers of cellular networks. On the other hand, femtocell networks are deployed to improve cell coverage and system capacity. Femtocell BS is a low power, short range data access point located around high ___________________________________

user density hotspots serving stationary or low-mobility users [6]. Its radio coverage range (10-50 meters) is much smaller than the macrocell radius (300-2000 meters), which enables superior signal reception and lowers the transmit power of mobile station (MS) in return prolonging the battery life and improving the energy efﬁciency. Therefore, two-tier cellular networks consisting of macrocells and femtocells which can provide high spectral efﬁciency and low power consumption will be widely used in next generation wireless networks. Many previous works for two-tier femtocell networks mainly focused on system capacity [7] [8] but ignored the energy consumption of both macrocell BS and femtocell BS. An energy efﬁcient spectrum allocation strategy was investigated in [9] for macro-femto networks to achieve the maximum energy efﬁciency. The authors proposed a dedicated spectrum allocation scheme which can avoid the cross-tier interference. However, frequency spectrum will be scarcer in future wireless network and this scheme can not make full use of frequency resource, especially in high load system. Therefore, in this paper, we propose an energy efﬁcient power allocation scheme for green communications in two-tier femtocell networks when macro BS and femto BS share the same frequency spectrum with cross-tier interference. This paper is organized as follows. In section II, the system model including pathloss model and interference analysis is described for two-tier macro-femto networks. Section III gives the energy efﬁciency metrics and discribes the proposed energy efﬁcient power allocation scheme. In section IV, the numerical results are given. The last section concludes this paper. II. S YSTEM M ODEL We consider a two-tier network with macrocells and femtocells as Fig.1 based on orthogonal frequency division multiple access (OFDMA), which is one of key technologies in the next generation wireless networks such as worldwide interoperability for microwave access (WiMAX) [10] and long term evolution (LTE) [11]. In OFDMA systems, the total bandwidth is divided into multiple orthogonal sub-channels. Therefore, users in femtocells or macrocells have exclusive sub-channel to avoid intracell interference. We deﬁne the total bandwidth of system and the number of sub-channel as W Hz and N , respectively. The ﬁrst tier network consists of Nc standard

978-1-61284-307-0/11/$26.00 ©2011 IEEE

P LI = −10log10

λ 4πR

+ Lc + kw Lw + n( n+1 −0.46) Lw , n+2

(1)

Closee Macro MS

Reference Macrocell

Femtocell MS

Desired signal

Intra-tier Interference

Cross-tier Interference

Fig. 1: The macro-femto two-tier network.

hexagonal macro BSs in which all the macro MSs are uniformly distributed. The overlaid second tier network consists of Nf femto BSs which are distributed as a Homogeneous Spatial Possion Point Process (SPPP) on the plane of intensity λf in each macrocell. Moreover, in the femto BS, all the femto MSs are located as uniform distribution. We assume that the radius of macro BS and femto BS are Rc and Rf , respectively (Rc Rf ). The distance between macro BS and femto BS is Rd . Then, the average number of femtocell BSs per macrocell can be given as Nf = λf S(Rc ), where S(Rc ) is the area of macrocell. When the operators deploy femtocell networks, there are two different access policies. One of them is open access, where all the users including macro MSs and femto MSs belonging to the operators network can access the femtocell. The other one is the closed access where the macro MSs cannot access the femto BS even if the macro MS receive a stronger signal from the femto BS than that from macro BS. Therefore, open access is appropriate for hot spots and public places. Meanwhile, closed access is more suitable for home and small-ofﬁce scenarios. It has been proved that open access can provide a better performance since closed access causes cross-tier interference problems for macro MSs if they are close enough to a femto BS in downlink transmission. However, considering security problem and other aspects, most of operators choose to use closed access femtocell networks. A. Pathloss Model In the two-tier femtocell network, the pathloss models can be divided into outdoor to outdoor propagation model for macrocells, outdoor to indoor propagation model between macrocells and femtocells and indoor to indoor propagation models for femtocells. In this section, we mainly describe propagation model for indoor to indoor and outdoor to indoor propagation model, while vehicular pathloss model [12] are used for outdoor to outdoor scenario. The indoor pathloss model P LI is given by [13]

where R is the distance between transceivers and λ is the wavelength in meters. Lc is the constant loss and here it is set to be 37dB. Lw is penetrated loss which is deﬁned as the ratio of the signal strength before penetrating the wall to that after penetrating the wall. kw is the number of penetrated walls. n refers to the number of penetrated ﬂoors. To state conveniently, considering the sub-channel n, we deﬁne the channel gain from macro BS i to macro MS j and femto MS k as |hnMi ,Mj |2 and |hnMi ,Fk |2 , respectively. Similarly, the channel gain from reference femto BS i to macro MS j and femto MS k are given as |hnFi ,Mj |2 and |hnFi ,Fk |2 , respectively. We denote the maximum transmit powers of macro BS and femto BS in the total bandwidth as PM and PF , respectively. Then, pnMi and pnFi are the transmit power of macro BS and femto BS for the MS i on the sub-channel n, respectively. The signal to interference plus noise ratio (SINR) for macro MS and femto MS can be represented as follows. B. Interference Analysis and Received SINR a) Interference Analysis for Macro MS Since there is no intracell interference, we assume that the received interference for the reference macro MS using subchannel n is from neighboring macro BSs and the “close enough” cross-tier femto BSs. Here, “close enough” interfering femto BS refers to the femto BS whose distance is less than a certain value between macro MS. Moreover, cross-tier interference from other femto BSs is ignored for long distance. Therefore, the SINR of macro MS i using sub-channel n can be written as

n = SIN RM i

2 pnMi hnMi ,Mi , 2 2 Nc n n n n n n ρFj pFj hFj ,Mi + ρMk pMk hMk ,Mi + N0 k=2

(2)

where ρnFj = 1 and ρnMk = 1 indicate that there is co-channel interference from femtocell j and macrocell k to the user i on the sub-channel n, respectively. Otherwise, it is 0. N0 n denotes the received noise power. Let CM denote the spectral efﬁciency of macro MS on the sub-channel n. Therefore, we n n = log2 (1 + SIN RM ). can give the expression as CM i b) Interference Analysis for Femto MS The received interference for the reference femto MS is mainly from macro BSs and other femto BSs located in the same macrocell when we neglect the interference from femto BS in the neighboring macrocells as the signal strength is weak. Then, the SINR of femto MS i can be given as formula (3) (on the top of next page), where the variables ρnMj , ρnFk and N0 indicate the similar meaning as (2). Since the number of penetrated walls kw for the received signal from femtocell to femtocell is more than 2 and Lw is usually set to be 18.3dB for

SIN RFni

2 pnFi hnFi ,Fi = 2 N 2 Nc f n n n ρnMj pnMj hnMj ,Fi + ρFk pFk hFk ,Fi + N0 j=1

k=2

reinforced concrete [13], expression (3) can be approximately rewritten as

SIN RFni

2 pnFi hnFi ,Fi ≈ N , 2 c n n n ρMj pMj hMj ,Fi + N0

(3)

(4)

where α is the energy coefﬁcient of macro BS. b) Femto BS Power Consumption Similarly, femto BS power consumption also can be divided into linear part PLF and constant part PCF . The model can be given by

j=1

Similarly, the spectral efﬁciency of femto sub-channel n can be calculated by log2 (1 +

MS CFn on SIN RFni ).

the

A. Energy Efﬁciency Metrics Energy efﬁciency metrics is of great importance to green communications as it can provide quantiﬁed information to evaluate energy efﬁciency. Different metrics have been proposed in previous works to design the networks [14]. In this paper, we focus on the power allocation scheme to maximize energy efﬁciency in two-tier femtocell network under QoS constraints. The metrics adopted in this paper is telecommunications energy efﬁciency ratio (TEER) [15] T (5) . E Where T is effective system throughput in bits per second and E is the energy consumptionin watts. Therefore, the unit bit of TEER is bit/J Wbps att = Joule . T EER =

B. RBS Power Consumption For the energy efﬁciency problem, we can divide wireless equipments into radio BSs (RBS) and wireless terminals. A RBS here refers to a macro cellular BS or wireless access point such as femto BS, while a wireless terminal refers to a MS with wireless interfaces. In this paper, we aim to maximize RBS energy efﬁciency for reducing the CO2 emission and lowering OPEX. Then, the power consumption model for macro BS and femto BS can be given as follows. a) Macro BS Power Consumption The power consumption of a macro BS takes into account power consumed by power supply, ampliﬁer and feeder losses, cooling and other auxiliary equipments, etc. From [9], the macro BS power consumption consists of linear part PLM (linear with transmit power) and constant part PCM (power consumption of cooling and other auxiliary equipments). Then, we can give the expression of power consumption for macro BS as PM = PLM + PCM = α

n=1

pnM

+ PCM ,

N

pnF + PCF ,

(7)

n=1

III. E NERGY E FFICIENT P OWER A LLOCATION S CHEME

N

PM = PLF + PCF = β

(6)

where β is the energy coefﬁcient of femto BS. PLF is linear with the transmit power of femto BS. C. Power Allocation for Green communications It has been shown that when the femto BSs are densely located in the macrocells, the femtocell network costs the same level energy as macro BS. Therefore, in this paper, we focus on the power allocation scheme of femto BS while the macro BS can use other schemes in previous works to achieve high energy efﬁciency. This is outside the scope of this paper. Therefore, macro BS uses the equal power allocation, in which same power is allocated to all the sub-channels. If there is a low trafﬁc load in the femtocell and all the channel gains including macro MSs and femto MSs can be known by feedback during the uplink transmission, it can avoid cross-tier interference by scheduling macro MSs and femto MSs on the orthogonal sub-channels. While, in a high trafﬁc load, the femto MSs are inevitable to use the same sub-channel that is also allocated to macro MS. In this case, the transmit power of femto BS on shared sub-channel should adaptively adjust according to channel conditions. We assume all the channel gains of macro MSs and femto MSs are available. Then, we propose a power allocation scheme to maximize local TEER when considering the reference femto MS and close interfered macro MS in downlink. Here, local TEER means sum TEER of reference femto BS and interfered macro MSs. The proposed scheme which is called MLTEER scheme in this paper is expressed as

max arg {p1F ,...,pnF }

W N

UM

s.t.

N n=1

n n δM (CM (pn F ))+UF

Psystem (pn F)

N n=1

n (CF (pn F ))

N

pnF ≤ PF n=1 CFn (pnF ) ≥ CnF T n (pnF ) ≥ CnM T CM

(8) where UM and UF = 1 − UM is used to adjust the capacity weight between the macro MSs and femto MSs, respectively.

TABLE I: simulation parameters

5

10

Value omni-directional 33dBm 13dBm 18.3 dB α = 21.54 , β = 7.84 -174 dBmn/Hz CM T = 5, CF T = 2 (bps/Hz)

=

n=1

n n δM pM + PCM

N

+β

N n=1

UF=0.3(MLTEER) UF=0.3(EQ)

3

10

10 0.1

9 8.5 UF=0.3(MLTEER)

8

U =0.3(EQ) F

7.5

0.3

0.5

0.7

6.5 0.1

0.9

0.3

0.5

0.7

Rd (m)

Rd (m)

(a) Local TEER

(b) Average Rate

0.9

Fig. 2: Local TEER And average Rate versus Rd (UF = 0.3). 5

10

pnF + PCF

10.5 10

(9)

where above variables have been deﬁned in the section III. Based on aforementioned assumptions and analysis, we can ﬁnd that expression (8) is a non-convex problem. Although it’s difﬁcult to ﬁnd the global optimal points, we can ﬁnd a local optimum value by interior-point method for nonconvex nolinear programming [16]. For the length is limited, the computational process is not given in this paper.

4

10

UF=0.5(MLTEER) U =0.5(EQ)

3

F

10

9.5 9 8.5 8 UF=0.5(MLTEER)

7.5

UF=0.5(EQ)

7 6.5

2

10 0.1

IV. N UMERICAL R ESULTS In this section, the performance of proposed energy efﬁcient power allocation scheme in two-tier macro-femto network, which consists of Nc = 19 macrocells and Nf = 120 femtocells, is evaluated based on Monte Carlo simulation. The macrocell and femtocell radius are 577m and 30m, respectively. The adopted pathloss model has been described in section II with the center frequency of 2GHz. In the simulation, we assume that the cell load of macrocell and femtocell are 90% and 95%, respectively, where the cell load is deﬁned as the expectation of the ratio of utilized subchannel to total sub-channel in each cell. In order to guarantee that there are close enough macro MSs for the reference femto BS, we distribute at least one macro MS in the region of circular ring (Rf < d < Rf + 20) in every snapshot. Additional simulation parameters are given in TABLE I. Fig.2(a) shows the Local TEER as a function of Rd for proposed MLTEER power allocation scheme and equal (EQ) power allocation scheme when UF = 0.3. The curve shows that MLTEER scheme can achieve a huge gain on energy efﬁciency. Moreover, it is noteworthy that improvement on Local TEER is achieved with increment of Rd . Fig.2(b) illustrates the user transmission rate as a function of Rd for two schemes. With the MLTEER scheme, the average rate improved about 1bps/Hz on average. Fig.3 and Fig.4 show the similar results as Fig.2 when UF is 0.5 and 0.9, respectively. Through above analysis, we can conclude that proposed MLTEER scheme has

9.5

7 2

Average Rate (bps/Hz)

Psystem (pnF )

N

4

10

Average Rate (bps/Hz)

Local TEER (bit/J)

n δM is deﬁned as the utilization indicator for close enough n = 1 denotes that the sub-channel is allocated macro MS. δM n n = 0. CFn T and CM for the macro MS, otherwise δM T are the minimal requirement of user average rate to femto MSs and macro MSs, respectively. Psystem (pnF ) is the power consumption of local RBS.

α

10.5 10

Local TEER (bit/J)

Parameters Antenna Macro BS transmit power Femto BS transmit power Wall penetration loss Energy model coefﬁcient Noise power Min. average rate

0.3

0.5

0.7

0.9

6 0.1

0.3

0.5

0.7

Rd (m)

Rd (m)

(a) Local TEER

(b)Average Rate

0.9

Fig. 3: Local TEER and Average Rate versus Rd (UF = 0.5). the better performance both on Local TEER and average rate compared with EQ scheme. Fig.5 provides the performance of MLTEER scheme and EQ scheme as the function of capacity weight UF when Rd is 400m under user QoS constrains. As shown in Fig.5(a), the Local TEER of two schemes increase with increment of UF . From Fig.5(b), we can also observe that the user average rate also increases with UF . Therefore, the above results depending on UF show that the performance can be improved when the power allocation scheme adjust the capacity weight to femtocell. V. C ONCLUSION In this paper, we propose a novel power allocation scheme (MLTEER) for achieving high energy efﬁciency for green communications in two-tier femtocell network. The MLTEER scheme aims to maximize one of energy efﬁciency metrics TEER, when considering the cross-tier interference. Based on the simulation results, we can prove that the proposed scheme can obtain a huge gain on local TEER and improve user rate 1bps/Hz on average as the function of distance between macro BS and femto BS. Moreover, our results also show

6

10

10.5

5

Local TEER (bit/J)

10

4

10

U =0.9(MLTEER) F

UF=0.9(EQ) 3

10

Average Rate (bps/Hz)

10 9.5 9 8.5 8 UF=0.9(MLTEER) 7.5

UF=0.9(EQ)

7 2

10 0.1

0.3

0.5

0.7

6.5 0.1

0.9

0.3

0.5

0.7

R (m)

R (m)

(a) Local TEER

(b) Average Rate

d

0.9

d

Fig. 4: Local TEER and Average Rate versus Rd (UF = 0.9). 6

10.2

10

Local TEER (bit/J)

10

4

10

Rd = 400m(MLTEER) Rd = 400m(EQ) 3

10

Average Rate (bps/Hz)

10 5

9.8 9.6 9.4 R = 400m(MLTEER) d

9.2

Rd = 400m(EQ) 9 8.8

2

10 0.1

0.3

0.5

0.7

0.9

8.6 0.1

0.3

0.5

0.7

UF

U

(a) Local TEER

(b) Average Rate

[4] G. Miao, N.Himayat, Y. Li and D. Ormann, “Energy-efﬁcient design in wireless OFMDA,” in Proc. IEEE International Conf. on Commun. (ICC), pp. 3307-3312, May, 2008. [5] H. Karl, “An overview of energy-efﬁciency techniques for mobile communication systems,” Telecommunication Networks Group, Technical University Berlin, Tech. Rep., September 2003. [6] V. Chandrasekhar, J. G. Andrews, and A. Gatherer, “Femtocell networks: a survey,” IEEE Commun. Mag., vol. 46, pp. 56-67, Sep. 2008. [7] Lorenza Giupponi and Christian Ibars, “Distributed Interference Control in OFDMA-based Femtocells” in Proc. IEEE Personal Indoor and Mobile Radio Communications (PIMRC), pp. 1201-1206, Sept., 2010. [8] Min-Sung Kim, Hui Won Je, and Fouad A. Tobagi, “Cross-tier Interference Mitigation for Two-tier OFDMA Femtocell Networks with Limited Macrocell Information”, in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), pp. 1-5, Dec., 2010. [9] Wenchi Cheng, Hailin Zhang, Liqiang Zhao and Yongzhao Li, “Energy Efﬁcient Spectrum Allocation for Green Radio in Two-tier Cellular Networks” in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), pp. 1-5, Dec., 2010. [10] IEEE standard 802.16e, “Air Interface for Fixed Broadband Wireless Access Systems,” Feb. 2006. [11] 3GPP, TR 25.814, “Physical Layer Aspects for Evolved UTRA”, V7.0.0 (2006-06). [12] 3GPP TR 30.03 “Universal Mobile Telecommunications System Selection procedures for the choice of radio transmission technologies of the UMTS”,1998. [13] ITU-R Rec. M.1225 , “Guidelines for Evaluation of Radio Transmission Technologies for IMT-2000.” [14] Tao Chen, Haesik Kim, and Yang Yang, “Energy Efﬁciency Metrics For Green Wireless Communications” in Proc. Wireless Communications and Signal Processing (WCSP), pp. 1-6, Oct., 2010 [15] Deutsche Telekom AG, “Network and telecom equipment-energy and performance assessment test procedure and measurement methodology,” ECR initiative, Germany, Tech. Rep., 2008 [16] R. Vanderbei and D. Shanno, “An interior-point algorithm for nonconvex nolinear programming,” Computational Optimization and Applications, vol. 13, no. 1-3, pp. 231-252, Apr. 1999.

0.9

F

Fig. 5: Local TEER and Average Rate versus UF (Rd = 400m).

that the MLTEER scheme can achieve better performance with increment of capacity weight of femtocell. ACKNOWLEDGMENT This work was supported in part by the State Major Science and Technology Special Projects (Grant No. 2011ZX03003002-01), the National Natural Science Foundation of China (Grant No. 61072058), the Fok Ying Tong Education Foundation Application Research Projects (Grant No. 122005), the Program for New Century Excellent Talents in University and Chinese Universities Scientiﬁc Fund (Grant No. 2011RC0108). R EFERENCES [1] R. Tafazolli and V. Mattila, “eMobility Mobile and Wireless Communications Technology Platform: Strategic Research Agenda,” 2008. http://www.emobility.eu.org [2] Y. Xiao, “Energy saving mechanism in the IEEE 802.16e wireless MAN,” IEEE Commun. Letters, vol.9, no.7, pp. 595-597, July, 2005. [3] F. Meshkati, H. V. Poor, S. C. Schwartz, and N. B. Mandayam, “An energy-efﬁcient approach to power control and receiver design in wireless networks, IEEE Trans. Commun., vol.5, no.1, pp. 3306-3315, Nov. 2006.

I. I NTRODUCTION Currently, concern regarding climate warming caused by accumulation of greenhouse gas (primarily carbon dioxide CO2 ) has been a signiﬁcant increase. According to [1], it is shown that information and communications technologies (ICT) infrastructure accounts for about 3%-4% of the worldwide energy (approximately, 1.5%-2% by wireless). Likewise, the exponential growth of wireless communications requires more and more wireless infrastructure which signiﬁcantly increases energy consumption. This growth will lead to that energy consumption of ICTs increases by approximately 16% to 20% in every ﬁve years [1]. As a result, ICTs has become a major contributor to overall power consumption and green house gas emissions. Therefore, green communications, which aim to improve the energy efﬁciency and decrease the carbon emission for future communication networks, has received more and more attention for researchers and wireless industry. Telecommunication operators as well as researchers have become aware of this energy issue, and have begun to study “green communications”. Recently, some energy efﬁcient strategies for limited battery life terminal has been studied in [2] [3] [4]. However, through the analysis in [5], it reveals that the radio access network (RAN) consumes the most energy among wireless equipments. Therefore, it is necessary to investigate the downlink energy efﬁcient strategy since base stations (BSs) are the primary energy consumers of cellular networks. On the other hand, femtocell networks are deployed to improve cell coverage and system capacity. Femtocell BS is a low power, short range data access point located around high ___________________________________

user density hotspots serving stationary or low-mobility users [6]. Its radio coverage range (10-50 meters) is much smaller than the macrocell radius (300-2000 meters), which enables superior signal reception and lowers the transmit power of mobile station (MS) in return prolonging the battery life and improving the energy efﬁciency. Therefore, two-tier cellular networks consisting of macrocells and femtocells which can provide high spectral efﬁciency and low power consumption will be widely used in next generation wireless networks. Many previous works for two-tier femtocell networks mainly focused on system capacity [7] [8] but ignored the energy consumption of both macrocell BS and femtocell BS. An energy efﬁcient spectrum allocation strategy was investigated in [9] for macro-femto networks to achieve the maximum energy efﬁciency. The authors proposed a dedicated spectrum allocation scheme which can avoid the cross-tier interference. However, frequency spectrum will be scarcer in future wireless network and this scheme can not make full use of frequency resource, especially in high load system. Therefore, in this paper, we propose an energy efﬁcient power allocation scheme for green communications in two-tier femtocell networks when macro BS and femto BS share the same frequency spectrum with cross-tier interference. This paper is organized as follows. In section II, the system model including pathloss model and interference analysis is described for two-tier macro-femto networks. Section III gives the energy efﬁciency metrics and discribes the proposed energy efﬁcient power allocation scheme. In section IV, the numerical results are given. The last section concludes this paper. II. S YSTEM M ODEL We consider a two-tier network with macrocells and femtocells as Fig.1 based on orthogonal frequency division multiple access (OFDMA), which is one of key technologies in the next generation wireless networks such as worldwide interoperability for microwave access (WiMAX) [10] and long term evolution (LTE) [11]. In OFDMA systems, the total bandwidth is divided into multiple orthogonal sub-channels. Therefore, users in femtocells or macrocells have exclusive sub-channel to avoid intracell interference. We deﬁne the total bandwidth of system and the number of sub-channel as W Hz and N , respectively. The ﬁrst tier network consists of Nc standard

978-1-61284-307-0/11/$26.00 ©2011 IEEE

P LI = −10log10

λ 4πR

+ Lc + kw Lw + n( n+1 −0.46) Lw , n+2

(1)

Closee Macro MS

Reference Macrocell

Femtocell MS

Desired signal

Intra-tier Interference

Cross-tier Interference

Fig. 1: The macro-femto two-tier network.

hexagonal macro BSs in which all the macro MSs are uniformly distributed. The overlaid second tier network consists of Nf femto BSs which are distributed as a Homogeneous Spatial Possion Point Process (SPPP) on the plane of intensity λf in each macrocell. Moreover, in the femto BS, all the femto MSs are located as uniform distribution. We assume that the radius of macro BS and femto BS are Rc and Rf , respectively (Rc Rf ). The distance between macro BS and femto BS is Rd . Then, the average number of femtocell BSs per macrocell can be given as Nf = λf S(Rc ), where S(Rc ) is the area of macrocell. When the operators deploy femtocell networks, there are two different access policies. One of them is open access, where all the users including macro MSs and femto MSs belonging to the operators network can access the femtocell. The other one is the closed access where the macro MSs cannot access the femto BS even if the macro MS receive a stronger signal from the femto BS than that from macro BS. Therefore, open access is appropriate for hot spots and public places. Meanwhile, closed access is more suitable for home and small-ofﬁce scenarios. It has been proved that open access can provide a better performance since closed access causes cross-tier interference problems for macro MSs if they are close enough to a femto BS in downlink transmission. However, considering security problem and other aspects, most of operators choose to use closed access femtocell networks. A. Pathloss Model In the two-tier femtocell network, the pathloss models can be divided into outdoor to outdoor propagation model for macrocells, outdoor to indoor propagation model between macrocells and femtocells and indoor to indoor propagation models for femtocells. In this section, we mainly describe propagation model for indoor to indoor and outdoor to indoor propagation model, while vehicular pathloss model [12] are used for outdoor to outdoor scenario. The indoor pathloss model P LI is given by [13]

where R is the distance between transceivers and λ is the wavelength in meters. Lc is the constant loss and here it is set to be 37dB. Lw is penetrated loss which is deﬁned as the ratio of the signal strength before penetrating the wall to that after penetrating the wall. kw is the number of penetrated walls. n refers to the number of penetrated ﬂoors. To state conveniently, considering the sub-channel n, we deﬁne the channel gain from macro BS i to macro MS j and femto MS k as |hnMi ,Mj |2 and |hnMi ,Fk |2 , respectively. Similarly, the channel gain from reference femto BS i to macro MS j and femto MS k are given as |hnFi ,Mj |2 and |hnFi ,Fk |2 , respectively. We denote the maximum transmit powers of macro BS and femto BS in the total bandwidth as PM and PF , respectively. Then, pnMi and pnFi are the transmit power of macro BS and femto BS for the MS i on the sub-channel n, respectively. The signal to interference plus noise ratio (SINR) for macro MS and femto MS can be represented as follows. B. Interference Analysis and Received SINR a) Interference Analysis for Macro MS Since there is no intracell interference, we assume that the received interference for the reference macro MS using subchannel n is from neighboring macro BSs and the “close enough” cross-tier femto BSs. Here, “close enough” interfering femto BS refers to the femto BS whose distance is less than a certain value between macro MS. Moreover, cross-tier interference from other femto BSs is ignored for long distance. Therefore, the SINR of macro MS i using sub-channel n can be written as

n = SIN RM i

2 pnMi hnMi ,Mi , 2 2 Nc n n n n n n ρFj pFj hFj ,Mi + ρMk pMk hMk ,Mi + N0 k=2

(2)

where ρnFj = 1 and ρnMk = 1 indicate that there is co-channel interference from femtocell j and macrocell k to the user i on the sub-channel n, respectively. Otherwise, it is 0. N0 n denotes the received noise power. Let CM denote the spectral efﬁciency of macro MS on the sub-channel n. Therefore, we n n = log2 (1 + SIN RM ). can give the expression as CM i b) Interference Analysis for Femto MS The received interference for the reference femto MS is mainly from macro BSs and other femto BSs located in the same macrocell when we neglect the interference from femto BS in the neighboring macrocells as the signal strength is weak. Then, the SINR of femto MS i can be given as formula (3) (on the top of next page), where the variables ρnMj , ρnFk and N0 indicate the similar meaning as (2). Since the number of penetrated walls kw for the received signal from femtocell to femtocell is more than 2 and Lw is usually set to be 18.3dB for

SIN RFni

2 pnFi hnFi ,Fi = 2 N 2 Nc f n n n ρnMj pnMj hnMj ,Fi + ρFk pFk hFk ,Fi + N0 j=1

k=2

reinforced concrete [13], expression (3) can be approximately rewritten as

SIN RFni

2 pnFi hnFi ,Fi ≈ N , 2 c n n n ρMj pMj hMj ,Fi + N0

(3)

(4)

where α is the energy coefﬁcient of macro BS. b) Femto BS Power Consumption Similarly, femto BS power consumption also can be divided into linear part PLF and constant part PCF . The model can be given by

j=1

Similarly, the spectral efﬁciency of femto sub-channel n can be calculated by log2 (1 +

MS CFn on SIN RFni ).

the

A. Energy Efﬁciency Metrics Energy efﬁciency metrics is of great importance to green communications as it can provide quantiﬁed information to evaluate energy efﬁciency. Different metrics have been proposed in previous works to design the networks [14]. In this paper, we focus on the power allocation scheme to maximize energy efﬁciency in two-tier femtocell network under QoS constraints. The metrics adopted in this paper is telecommunications energy efﬁciency ratio (TEER) [15] T (5) . E Where T is effective system throughput in bits per second and E is the energy consumptionin watts. Therefore, the unit bit of TEER is bit/J Wbps att = Joule . T EER =

B. RBS Power Consumption For the energy efﬁciency problem, we can divide wireless equipments into radio BSs (RBS) and wireless terminals. A RBS here refers to a macro cellular BS or wireless access point such as femto BS, while a wireless terminal refers to a MS with wireless interfaces. In this paper, we aim to maximize RBS energy efﬁciency for reducing the CO2 emission and lowering OPEX. Then, the power consumption model for macro BS and femto BS can be given as follows. a) Macro BS Power Consumption The power consumption of a macro BS takes into account power consumed by power supply, ampliﬁer and feeder losses, cooling and other auxiliary equipments, etc. From [9], the macro BS power consumption consists of linear part PLM (linear with transmit power) and constant part PCM (power consumption of cooling and other auxiliary equipments). Then, we can give the expression of power consumption for macro BS as PM = PLM + PCM = α

n=1

pnM

+ PCM ,

N

pnF + PCF ,

(7)

n=1

III. E NERGY E FFICIENT P OWER A LLOCATION S CHEME

N

PM = PLF + PCF = β

(6)

where β is the energy coefﬁcient of femto BS. PLF is linear with the transmit power of femto BS. C. Power Allocation for Green communications It has been shown that when the femto BSs are densely located in the macrocells, the femtocell network costs the same level energy as macro BS. Therefore, in this paper, we focus on the power allocation scheme of femto BS while the macro BS can use other schemes in previous works to achieve high energy efﬁciency. This is outside the scope of this paper. Therefore, macro BS uses the equal power allocation, in which same power is allocated to all the sub-channels. If there is a low trafﬁc load in the femtocell and all the channel gains including macro MSs and femto MSs can be known by feedback during the uplink transmission, it can avoid cross-tier interference by scheduling macro MSs and femto MSs on the orthogonal sub-channels. While, in a high trafﬁc load, the femto MSs are inevitable to use the same sub-channel that is also allocated to macro MS. In this case, the transmit power of femto BS on shared sub-channel should adaptively adjust according to channel conditions. We assume all the channel gains of macro MSs and femto MSs are available. Then, we propose a power allocation scheme to maximize local TEER when considering the reference femto MS and close interfered macro MS in downlink. Here, local TEER means sum TEER of reference femto BS and interfered macro MSs. The proposed scheme which is called MLTEER scheme in this paper is expressed as

max arg {p1F ,...,pnF }

W N

UM

s.t.

N n=1

n n δM (CM (pn F ))+UF

Psystem (pn F)

N n=1

n (CF (pn F ))

N

pnF ≤ PF n=1 CFn (pnF ) ≥ CnF T n (pnF ) ≥ CnM T CM

(8) where UM and UF = 1 − UM is used to adjust the capacity weight between the macro MSs and femto MSs, respectively.

TABLE I: simulation parameters

5

10

Value omni-directional 33dBm 13dBm 18.3 dB α = 21.54 , β = 7.84 -174 dBmn/Hz CM T = 5, CF T = 2 (bps/Hz)

=

n=1

n n δM pM + PCM

N

+β

N n=1

UF=0.3(MLTEER) UF=0.3(EQ)

3

10

10 0.1

9 8.5 UF=0.3(MLTEER)

8

U =0.3(EQ) F

7.5

0.3

0.5

0.7

6.5 0.1

0.9

0.3

0.5

0.7

Rd (m)

Rd (m)

(a) Local TEER

(b) Average Rate

0.9

Fig. 2: Local TEER And average Rate versus Rd (UF = 0.3). 5

10

pnF + PCF

10.5 10

(9)

where above variables have been deﬁned in the section III. Based on aforementioned assumptions and analysis, we can ﬁnd that expression (8) is a non-convex problem. Although it’s difﬁcult to ﬁnd the global optimal points, we can ﬁnd a local optimum value by interior-point method for nonconvex nolinear programming [16]. For the length is limited, the computational process is not given in this paper.

4

10

UF=0.5(MLTEER) U =0.5(EQ)

3

F

10

9.5 9 8.5 8 UF=0.5(MLTEER)

7.5

UF=0.5(EQ)

7 6.5

2

10 0.1

IV. N UMERICAL R ESULTS In this section, the performance of proposed energy efﬁcient power allocation scheme in two-tier macro-femto network, which consists of Nc = 19 macrocells and Nf = 120 femtocells, is evaluated based on Monte Carlo simulation. The macrocell and femtocell radius are 577m and 30m, respectively. The adopted pathloss model has been described in section II with the center frequency of 2GHz. In the simulation, we assume that the cell load of macrocell and femtocell are 90% and 95%, respectively, where the cell load is deﬁned as the expectation of the ratio of utilized subchannel to total sub-channel in each cell. In order to guarantee that there are close enough macro MSs for the reference femto BS, we distribute at least one macro MS in the region of circular ring (Rf < d < Rf + 20) in every snapshot. Additional simulation parameters are given in TABLE I. Fig.2(a) shows the Local TEER as a function of Rd for proposed MLTEER power allocation scheme and equal (EQ) power allocation scheme when UF = 0.3. The curve shows that MLTEER scheme can achieve a huge gain on energy efﬁciency. Moreover, it is noteworthy that improvement on Local TEER is achieved with increment of Rd . Fig.2(b) illustrates the user transmission rate as a function of Rd for two schemes. With the MLTEER scheme, the average rate improved about 1bps/Hz on average. Fig.3 and Fig.4 show the similar results as Fig.2 when UF is 0.5 and 0.9, respectively. Through above analysis, we can conclude that proposed MLTEER scheme has

9.5

7 2

Average Rate (bps/Hz)

Psystem (pnF )

N

4

10

Average Rate (bps/Hz)

Local TEER (bit/J)

n δM is deﬁned as the utilization indicator for close enough n = 1 denotes that the sub-channel is allocated macro MS. δM n n = 0. CFn T and CM for the macro MS, otherwise δM T are the minimal requirement of user average rate to femto MSs and macro MSs, respectively. Psystem (pnF ) is the power consumption of local RBS.

α

10.5 10

Local TEER (bit/J)

Parameters Antenna Macro BS transmit power Femto BS transmit power Wall penetration loss Energy model coefﬁcient Noise power Min. average rate

0.3

0.5

0.7

0.9

6 0.1

0.3

0.5

0.7

Rd (m)

Rd (m)

(a) Local TEER

(b)Average Rate

0.9

Fig. 3: Local TEER and Average Rate versus Rd (UF = 0.5). the better performance both on Local TEER and average rate compared with EQ scheme. Fig.5 provides the performance of MLTEER scheme and EQ scheme as the function of capacity weight UF when Rd is 400m under user QoS constrains. As shown in Fig.5(a), the Local TEER of two schemes increase with increment of UF . From Fig.5(b), we can also observe that the user average rate also increases with UF . Therefore, the above results depending on UF show that the performance can be improved when the power allocation scheme adjust the capacity weight to femtocell. V. C ONCLUSION In this paper, we propose a novel power allocation scheme (MLTEER) for achieving high energy efﬁciency for green communications in two-tier femtocell network. The MLTEER scheme aims to maximize one of energy efﬁciency metrics TEER, when considering the cross-tier interference. Based on the simulation results, we can prove that the proposed scheme can obtain a huge gain on local TEER and improve user rate 1bps/Hz on average as the function of distance between macro BS and femto BS. Moreover, our results also show

6

10

10.5

5

Local TEER (bit/J)

10

4

10

U =0.9(MLTEER) F

UF=0.9(EQ) 3

10

Average Rate (bps/Hz)

10 9.5 9 8.5 8 UF=0.9(MLTEER) 7.5

UF=0.9(EQ)

7 2

10 0.1

0.3

0.5

0.7

6.5 0.1

0.9

0.3

0.5

0.7

R (m)

R (m)

(a) Local TEER

(b) Average Rate

d

0.9

d

Fig. 4: Local TEER and Average Rate versus Rd (UF = 0.9). 6

10.2

10

Local TEER (bit/J)

10

4

10

Rd = 400m(MLTEER) Rd = 400m(EQ) 3

10

Average Rate (bps/Hz)

10 5

9.8 9.6 9.4 R = 400m(MLTEER) d

9.2

Rd = 400m(EQ) 9 8.8

2

10 0.1

0.3

0.5

0.7

0.9

8.6 0.1

0.3

0.5

0.7

UF

U

(a) Local TEER

(b) Average Rate

[4] G. Miao, N.Himayat, Y. Li and D. Ormann, “Energy-efﬁcient design in wireless OFMDA,” in Proc. IEEE International Conf. on Commun. (ICC), pp. 3307-3312, May, 2008. [5] H. Karl, “An overview of energy-efﬁciency techniques for mobile communication systems,” Telecommunication Networks Group, Technical University Berlin, Tech. Rep., September 2003. [6] V. Chandrasekhar, J. G. Andrews, and A. Gatherer, “Femtocell networks: a survey,” IEEE Commun. Mag., vol. 46, pp. 56-67, Sep. 2008. [7] Lorenza Giupponi and Christian Ibars, “Distributed Interference Control in OFDMA-based Femtocells” in Proc. IEEE Personal Indoor and Mobile Radio Communications (PIMRC), pp. 1201-1206, Sept., 2010. [8] Min-Sung Kim, Hui Won Je, and Fouad A. Tobagi, “Cross-tier Interference Mitigation for Two-tier OFDMA Femtocell Networks with Limited Macrocell Information”, in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), pp. 1-5, Dec., 2010. [9] Wenchi Cheng, Hailin Zhang, Liqiang Zhao and Yongzhao Li, “Energy Efﬁcient Spectrum Allocation for Green Radio in Two-tier Cellular Networks” in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), pp. 1-5, Dec., 2010. [10] IEEE standard 802.16e, “Air Interface for Fixed Broadband Wireless Access Systems,” Feb. 2006. [11] 3GPP, TR 25.814, “Physical Layer Aspects for Evolved UTRA”, V7.0.0 (2006-06). [12] 3GPP TR 30.03 “Universal Mobile Telecommunications System Selection procedures for the choice of radio transmission technologies of the UMTS”,1998. [13] ITU-R Rec. M.1225 , “Guidelines for Evaluation of Radio Transmission Technologies for IMT-2000.” [14] Tao Chen, Haesik Kim, and Yang Yang, “Energy Efﬁciency Metrics For Green Wireless Communications” in Proc. Wireless Communications and Signal Processing (WCSP), pp. 1-6, Oct., 2010 [15] Deutsche Telekom AG, “Network and telecom equipment-energy and performance assessment test procedure and measurement methodology,” ECR initiative, Germany, Tech. Rep., 2008 [16] R. Vanderbei and D. Shanno, “An interior-point algorithm for nonconvex nolinear programming,” Computational Optimization and Applications, vol. 13, no. 1-3, pp. 231-252, Apr. 1999.

0.9

F

Fig. 5: Local TEER and Average Rate versus UF (Rd = 400m).

that the MLTEER scheme can achieve better performance with increment of capacity weight of femtocell. ACKNOWLEDGMENT This work was supported in part by the State Major Science and Technology Special Projects (Grant No. 2011ZX03003002-01), the National Natural Science Foundation of China (Grant No. 61072058), the Fok Ying Tong Education Foundation Application Research Projects (Grant No. 122005), the Program for New Century Excellent Talents in University and Chinese Universities Scientiﬁc Fund (Grant No. 2011RC0108). R EFERENCES [1] R. Tafazolli and V. Mattila, “eMobility Mobile and Wireless Communications Technology Platform: Strategic Research Agenda,” 2008. http://www.emobility.eu.org [2] Y. Xiao, “Energy saving mechanism in the IEEE 802.16e wireless MAN,” IEEE Commun. Letters, vol.9, no.7, pp. 595-597, July, 2005. [3] F. Meshkati, H. V. Poor, S. C. Schwartz, and N. B. Mandayam, “An energy-efﬁcient approach to power control and receiver design in wireless networks, IEEE Trans. Commun., vol.5, no.1, pp. 3306-3315, Nov. 2006.