A novel antenna allocation technique for green single cell MIMO and MIMO-CoMP downlink transmission

Accepted Manuscript Regular paper A Novel Antenna Allocation Technique for Green Single Cell MIMO and MIMO-CoMP Downlink Transmission Tohid Tamadoni, Mohsen Eslami, Shahrokh Jam, Danial Davarpanah PII: DOI: Reference:

S1434-8411(16)30720-8 http://dx.doi.org/10.1016/j.aeue.2017.06.013 AEUE 51931

To appear in:

International Journal of Electronics and Communications

Received Date: Accepted Date:

21 October 2016 11 June 2017

Please cite this article as: T. Tamadoni, M. Eslami, S. Jam, D. Davarpanah, A Novel Antenna Allocation Technique for Green Single Cell MIMO and MIMO-CoMP Downlink Transmission, International Journal of Electronics and Communications (2017), doi: http://dx.doi.org/10.1016/j.aeue.2017.06.013

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A Novel Antenna Allocation Technique for Green Single Cell MIMO and MIMO-CoMP Downlink Transmission Tohid Tamadonia,1 , Mohsen Eslamia,, Shahrokh Jama , Danial Davarpanaha a Department

of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz - Iran Email: {t.tamadoni, m.eslami, jam, d.davarpanah}@sutech.ac.ir

Abstract To meet the increasing traffic and energy consumption demands of wireless networks, energy efficiency and energy efficient transmission techniques have become an urgent need for cellular networks. In this work, the problem of base station (BS) power consumption reduction for increased network energy efficiency of downlink TDMA-based transmission is considered. To meet network’s high traffic demand due to high data rates required by large numbers of users, multiple-input multiple-output (MIMO) and coordinated multi-point (CoMP) transmission have been considered. By adopting realistic power consumption models for single cell MIMO and multi-cell MIMO-CoMP networks, enhanced antenna allocation techniques are proposed and their energy efficiency is compared to the conventional power allocation schemes. It is shown that for a target signal to interference plus noise ratio (SINR), the proposed techniques consume less total power compared to the traditional schemes, which leads to higher energy efficiency. In addition, for same power level, the symbol error rate (SER) is reduced and system’s sum rate increases, which leads to higher spectral efficiency. Keywords: Green Cellular Network, MIMO, CoMP, Antenna Allocation 2010 MSC: 00-01, 99-00

Preprint submitted to Journal of LATEX Templates

June 1, 2017

1. Introduction Today global warming has emerged as a serious threat to the civilization of the earth, with carbon dioxide (CO2 ) emission being considered as its prime cause. 5

The rapid expansion of information and communication technology

(ICT) has significantly increased both the energy consumption and greenhouse gases emissions, leading to further energy crisis and global warming issues. Being an important part of ICT, cellular networks play a key role in reducing carbon dioxide (CO2 ) emissions [1]. Increasing concern about the energy consumption of the mobile networks

10

is driving operators to manage their equipments so as to optimize energy utilization without sacrificing user experience. Due to worldwide growth in the number of mobile subscribers, and with the majority of the tele-traffic evolving from low data rate speech and text messaging towards high data rate multimedia services, an increase in contribution of cellular communication networks to

15

the overall energy consumption of the world is observed. With unprecedented expansion of mobile data traffic and exponential increase in demand for Internet access using smart phones, a very large number of base stations (BSs) need to be deployed to handle huge volume of data delivered with high data rates. The large number of BSs contributes significantly to the total energy consumption

20

of the network. More than 60% of power consumption in cellular networks is consumed at the BS [2, 3], making BS power consumption reduction one of the top challenges in cellular networks. Until recently, the main concern of designers and service providers in cellular communication has been increasing capacity, data rate, coverage area and re-

25

ducing the power consumption in mobile phones. In traditional cellular network designs, increasing capacity and data rate with a constraint on the maximum transmit power has been the main goal. On the other hand, in recently developed green cellular networks, network power reduction without sacrificing user Quality of Service (QoS) is being considered.

30

In addition, service providers need to take into account the power consump-

2

tion cost factor when adopting new wireless technologies and will have more incentive in migrating to greener solutions, should such solutions provide economic incentives too. If the final cost of a green communication strategy is substantially higher than a traditional solution, the operators might have less 35

incentive to adopt green technologies. Accordingly, this article presents total power reduction techniques in a cellular network without requiring substantial change in the structure of currently deployed systems. 1.1. Related Work Investigating the influence of changing the cell radius for different traffic

40

loads [4, 5, 6], joint power consumption and cell size optimization [7, 8], BS On/Off switching strategies [9, 10, 11], distributed antenna deployment in a cell [12], energy efficient resource allocation [13, 14, 15], cell planning and advanced radio technique such as multiple-input multiple-output (MIMO), coordinated multi-point (CoMP) transmission [16], heterogeneous networks, and

45

relays [17, 18, 19], are a number of techniques that have been proposed for green cellular communications. In addition, antenna selection (AS) has been proposed for increasing spectral efficiency [20], increasing receiver SNR [21], and energy efficiency [22]. A survey concerning the state of the art energy efficient AS techniques including energy efficient AS in point-to-point MIMO, cooperative

50

MIMO and multiuser MIMO systems has been recently published [23]. The use of multi-antenna technology, MIMO, improves both the achievable data rate and the link reliability in single cell wireless systems without the need for extra power or bandwidth. The gain of the MIMO technique is however, severely degraded in a multi-cell environment due to inter-cell inter-

55

ference, especially for cell edge users. For solving the inter-cell interference problem, cooperation between the BSs has been suggested, that is known as CoMP transmission. In this paper, both single-cell and multi-cell models are considered. In order to maximize capacity while guaranteeing a target signalto-noise ration (SNR) for each user, a distinct resource allocation method called

60

antenna allocation is proposed and compared to the conventional power alloca3

tion techniques. It is shown that the proposed scheme results in considerably lower total network power consumption. The rest of the paper is organized as follows: System and power consumption models are described in Section 2 followed by channel model in this section. In 65

Section 3, ergodic capacity of MIMO single-cell and MIMO CoMP systems is reviewed. Section 4, presents the proposed scheme. In section 5, performance of the proposed scheme is compared to the conventional schemes in terms of power consumption and capacity. Finally, Section 6 concludes the paper.

2. Power Consumption and Channel Models 70

2.1. Power Consumption Models As already mentioned, a main contributor to power consumption of cellular networks is BS equipments. In [8], a model is suggested, which describes the power consumption at a BS according to PBS = am Ptx + bm ,

(1)

where PBS and Ptx denote the average power consumed by BS equipment and 75

the transmitted power from the BS, respectively. The coefficient am accounts for the power consumption that scales with the average radiated power due to amplifier and feeder losses as well as BS site cooling. The term bm models an offset power. In this model, power consumption is just a function of transmit power. Hence, this model is not suitable for multi-antenna BSs. In 2010, a

80

realistic model was suggested for single cell multi-antenna systems that is an extension of (1) to multi-antenna systems [24]: PBS = aPtx + bPSP .

(2)

In this model BS power consumption is a function of transmit power and signal processing (PSP ) at the BS. The coefficients a and b incorporate effects that scale with their corresponding power type. In this model [24], signal processing 85

is defined as PSP = psp (0.87 + 0.1Nc + 0.03Nc2 ), 4

(3)

where psp and Nc are coefficients that depend on cell size and number of BS antennas, respectively. 10% of the overall analog and digital processing power are due to uplink channel estimation and roughly 3% are due to uplink and downlink MIMO processing. In [12],[24] this model has been extended to cooperative 90

multi-cell multi-antenna systems according to PBS = aPtx + bPSP + cPbh ,

(4)

where, Pbh is the power due to backhauling and c is the coefficient of the power consumed in backhauling. In this model, contribution of power due to signal processing is obtained as PSP = 0.87psp + NCoM P (0.1Nc + 0.03Nc2 )psp ,

(5)

where NCoM P is the number of cooperating BSs. 95

2.2. Channel Model The receive power of a signal transmitted over the wireless channel is assumed to be [25] P˜rx,i = K(ri )−λ P˜tx,i ,

(6)

where P˜rx,i , and P˜tx,i are received power at the ith user and transmit power from the serving BS of ith user, respectively. ri is the BS distance to the ith 100

user. In this model, only channel path-loss is considered. K and λ are the propagation environment dependent coefficient and path-loss exponent, respectively. A network of 7 cells with 120 degree sectors for each cell have been considered. For the system with CoMP, clusters of 3 cells have been assumed. Only interferences from the first tire is taken into account and BSs are assumed to transmit

105

at maximum power resulting in the following interference power [25], PI,i =

6 X

(K(dn,i )−λ Ptx,n ),

(7)

n=1

where PI,i is the interference from the neighboring BSs received by the ith user. Ptx,n and dn,i , are power transmitted from the nth BS and its distance to the 5

Figure 1: Cell geometry with D =

√

3R and A =

3

√ 2

3

R2 . [26]

ith user. For simplicity of analysis, it is assumed dn,i = D for i = 1, . . . , 6 where √ D = R 3 (R is the cell radius), i.e., averaged distance between ith user and 110

neighboring BSs is equal to the distance between two adjacent BSs (PI,i = PI ). Using 6 and 7, signal to interference-plus-noise ratio (SINR) is obtained as Γi =

P˜rx,i P I + N0

(8)

where, Γi and N0 denote SINR of ith user and noise power, respectively. In the next section, relation between SINR, channel capacity and the number of transmit antennas is investigated.

115

3. MIMO And CoMP Capacity In this paper, the number of BS antennas is assumed to be MBS , and users are assumed to be equipped with a single antenna. Downlink time division multiple access (TDMA) is considered with channel state information (CSI) being available at the BS.

120

3.1. Single Cell MIMO The channel capacity (bit/sec/Hz) for MISO with unknown CSI at transmitter is equal to SIMO with known CSI at the receiver and for the channel between the BS and the ith user is given by [26], [27]

6

CM ISO,i = log2 (1 + Γi ) = log2 (1 +

Es,i hi hH i ) P I + N0

(9)

where Es,i is the energy of the s transmitted symbols. Size 1 × Mt,i vector hi 125

with independent and identically distributed (i.i.d.) complex Gaussian random components denotes the vector of channel coefficients and hH i is its complex conjugate transpose. Mt,i is the number of antennas allocated to the ith user. For the case of known CSI at the transmitter the capacity is obtained as [26], [27] CM ISO,i = log2 (1 +

130

γi Es,i hi hH i ), PI + N 0

(10)

where γi is a coefficient introduced due to CSI knowledge and it is shown that γi = Mt,i . In addition E[hi hH i ] = Mt,i ,

(11)

resulting in CM ISO,i ≈ log2 (1 +

Es,i M2 ) PI + N0 t,i

(12)

In (12) the relation between the numbers of antennas allocated to each user and the capacity is shown. 135

3.2. Multi-Cell Multi-Antenna Coordinated multi-point transmission (CoMP) is attractive as it improves cell edge coverage and average cell throughput. It is especially suitable to increase spectral efficiency of dense networks in urban areas and capacity of hotspots. In this section, 3 BSs are assumed to be transmitting with coordina-

140

tion. The SINR of each user is obtained as [28] γn,i ¯ ¯ H Gi Hi P I + N0 i h ¯T = E ¯s,1,i E ¯s,2,i , E s,i

SIN Ri = ¯i = where H

h

¯ 1,i h

¯ 2,i h

¯ 3,i h

(13) ¯s,3,i E

i

¯i = , and G

¯ s,i H ¯ i . denotes matrix Hadamard product or element-wise product. E ¯s,n,i E is the symbol energy of ith transmitted from the nth BS. The dimension of the ¯ i is 1 × NCoM P and channel vector between the user BSs has dimension of G

7

145

NCoM P × 1. γn,i is the array gain for the channel between the ith user and the nth BS and γn,i = Mt,n,i . n denotes the nth cooperating BS (n = 1, 2, 3 and n = 1 is index of the BS which user i is located in its serving cell). In addition ! PNCoM P ¯ 2 Es,i Mt,n,i n=1 Ci = log2 1 + (14) P I + N0 where Mt,n,i is number of antennas in nth BS allocated to the ith user and Ci is ith user’s required capacity [28].

150

4. Proposed Antenna Allocation Algorithm In previous section, the relation between SINR and the number of antennas allocated to each user in MIMO and CoMP systems were obtained. In Subsection 4.1, an antenna allocation algorithm satisfying each user’s target is proposed. In Subsection 4.2, traditional power allocation scheme that satisfies

155

user target SINR, is revisited using prior derived formulas. 4.1. Proposed SINR based Antenna Allocation Algorithm The main idea of the proposed scheme is to assign BS antennas to users such that each user’s target SINR is achieved. Consider a cell with radius R, and m users uniformly distributed in the cell. In addition, assume Ci = CT arget ,

160

(15)

where Ci and CT arget are ith user’s required capacity and system’s average capacity, respectively. Also Es,i =

P˜rx,i Mt,i

is the total energy assigned to the ith

user. In single cell MIMO with (6) and (12): CT arget = log2 (1 +

Kri−λ P˜tx,i M2 ) Mt,i (PI + N0 ) t,i

(16)

According to Section II, P˜tx,i is the transmitted power from the BS to the ith 165

user. All antennas are assumed to be transmitting with maximum power, such that P˜tx,i = Pantenna Mt,i . 8

(17)

With (16) and (17) CT arget = log2 (1 +

K(ri )−λ 2 Pantenna Mt,i ) PI + N 0

(18)

Mt,i is the only variable in (21), and 2 Mt,i =

(2CT arget − 1)(PI + N0 )MBS , K(ri )−λ Pantenna,max

(19)

where Pantenna,max is the maximum power transmitted from each antenna. Us170

ing (19), we can achieve the number of BS antennas that are required for each user to attain its target capacity. Nc in equation (3), is the number of BS antennas when all antennas cooperate in transmission to each user. In the proposed scheme, different numbers of antennas are assigned to different users. Average

175

number of BS antennas assigned to users is obtained as Pm Mt,i ¯ N = i=1 (20) m ¯ is the average number of antennas assigned to users, and m is the where N number of users being served by the BS. Next, based on the antenna assignment, signal processing power consumption contribution of different BS parts is computed as ¯ + 0.03N ¯ 2) PSP = psp (0.87 + 0.1N

(21)

¯ ≤ MBS , and therefore, PSP (N ¯ ) ≤ PSP (MBS ). In the case Mt,i = MBS where N 180

¯ = MBS . Total power consumption of a BS is computed for i = 1, · · · , m, then N as

Pm

Mt,i ¯ ), Pantenna,max , PBS = aP¯ + bPSP (N (22) m where, P¯ is the average power transmitted from the BS. PBS is the total power P¯ =

i=1

consumed at a BS based on the antenna allocation model of single-cell MIMO. For MIMO-CoMP systems a similar scheme is proposed. From the 3 coop185

erating BSs, one must find the number of BSs and antennas of each BS required to be assigned to each user to meet user’s target SINR. Hence, the relation between the SINR and number of antennas needs to be evaluated. With (6) and (14) PNCoM P CT raget = log2

1+

n=1

9

! 2 γn,i Es,n,i Mt,n,i . PI + N0

(23)

In (23), Mt,n,i n = 1, 2, 3 are the unknown variables. For each user, these 190

variables are found using the following proposed algorithm 1. For each BS, ignore other neighboring BSs and compute 2 Mt,1,i =

(2CT arget − 1)(PI + N0 ) K(r1,i )−λ Pantenna,max

(24)

where in (24) without loss of generality the BS with index n = 1 has been considered. 2. If Mt,1,i ≤ MBS the algorithm stops and NCoM P,i = 1 195

3. If Mt,1,i > MBS , the antennas of another BS in the cooperating set are required for user i. From the two BSs, the one that results in higher SINR is selected. The number of required antennas from the second BS are obtained as 2 Mt,2,i =

200

2 (P˜rx,1,i Mt,1,i ) (2CT arget − 1)(PI + N0 )MBS − −λ −λ K(r2,i ) Pantenna,max K(r2,i ) Pantenna,max

(25)

4. If Mt,2,i ≤ MBS , the algorithm terminates with NCoM P,i = 2 5. If Mt,2,i > MBS , antenna of all three cooperating BS are required. Number of antennas of the third BS are evaluated as P2 2 ( n=1 P˜rx,n,i Mt,n,i ) (2CT arget − 1)(PI + N0 )MBS 2 Mt,3,i = − −λ −λ K(r3,i ) Pantenna,max K(r3,i ) Pantenna,max

(26)

6. If Mt,3,i > MBS , it mean with all antennas of 3 cooperating BSs and full 205

transmit power, ith user does not achieve its target SINR. Similar to the single cell MIMO where average number of antennas was evaluated in equation (20), NCoM P is required to evaluate the power consumption, Pm Mt,1,i ¯ NCoM P = i=1 . (27) m Then, ¯CoM P (0.1N ¯ + 0.03N ¯ 2 )psp PSP = 0.87psp + N

10

(28)

and for transmitted power equation (22) is used to obtain ¯, N ¯CoM P ) + cPbh . PBS = a · P¯ + bPSP (N 210

(29)

In (29), Pbh decreases with decrease in number of antennas. Nevertheless, Pbh is discarded in this section to have a model similar to previous sections. 4.2. Traditional SINR based Power Allocation One traditional scheme is that BSs reduce or increase their transmit power to meet each user’s target SINR. In this concept all antennas at the BS are used

215

for data transmission. 4.2.1. Single Cell In single cell MIMO, similar to previous section, and by using equation (16) for Mt,i = MBS transmit power is obtained as (2CT arget − 1)(PI + N0 ) P˜tx,i = K(ri )−λ MBS Considering the BS’s power limitation  CT arget  (2 − 1)(PI + N0 ) P˜tx,i = min , P M . antenna,max BS K(ri )−λ MBS

220

(30)

(31)

To compute BS’s power consumption, average transmit power is required that is obtained using the following equation Pm ˜ Ptx,i P¯ = i=1 m

(32)

Finally, total power is obtained as PBS = aP¯ + bPSP (MBS ).

(33)

4.2.2. CoMP Transmission In CoMP transmission, if the main BS has adequate power to serve a user 225

and meet its target SINR, the other cooperating BSs are discarded. Otherwise, one or both of the other two coordinated BSs need to be activated, possibly with different transmit power levels. For computing BSs’ power consumption, 11

¯CoM P = N

Pm

NCoM P,i . m

i=1

(34)

Also, P˜tx,1,i = min



 (2CT arget − 1)(PI + N0 ) , P M antenna,max BS , K(ri )−λ MBS

(35)

where NCoM P,i = 1, 2 or 3. 230

4.3. SER Analysis of the Proposed Scheme In this section, symbol error rate (SER) of the proposed technique is presented and compared to the traditional power allocation scheme. In [26], the probability of symbol error is given by r ¯e Q( Pe ≈ N

ηd2min ) 2

(36)

¯e and dmin are the number of nearest neighbors and minimum distance where N 235

of symbols in underlying scalar constellation, respectively. η is interpreted as average SNR at the receiver. η in a MISO system is given by η = ρi Mt,i where ρi =

K(ri )−λ P˜tx,i (PI +N0 )

(37)

is the SINR at each user terminal due to the ith transmit

antenna. Furthermore, ρi =

K(ri )−λ Mt,i P˜tx,i . (PI + N0 )

(38)

Considering (36) and (37), SER for the proposed antenna allocation is ob240

tained as s ¯e Q  Pe,proposed ≈ N

 2 d2 K(ri )−λ Mt,i P min antenna,max  , 2(PI + N0 )

(39)

and for power allocation scheme s ¯e Q  Pe,traditional ≈ N

 K(ri )−λ MBS d2min P˜tx,i  . 2(PI + N0 )

Using Ctraget 12

(40)

log2

2 K(ri )−λ Mt,i Pantenna,max 1+ (PI + N0 )

! ≈ log2

K(ri )−λ MBS P˜tx,i 1+ PI + N0

! (41)

In (41), since Mt,i is an integer number the following inequality is obtained 2 P˜tx,i MBS < Pantenna,max Mt,i

(42)

Comparing SER of (39) with (40) and using (42):

Pe,P roposed < Pe,traditional , 245

(43)

which shows superiority of the proposed technique over the traditional power allocation scheme in terms of SER.

5. Simulation Results A hexagonal grid of macro cells where the radius of each cell is R = 1000 m, and in each cell m = 256 users have been randomly distributed is considered. 250

Each cell is divided into 3 sectors, and as it was already mentioned, a central cell with just the first tier of its neighbors has been considered. The BS is equipped with MBS antennas at each sector. Some of the simulation parameters, adopted from [8] ,[24], are presented in Table 1. Users of a cell are assumed to have the same target channel capacity that is an integer value between 1 b/s/Hz and 10

255

b/s/Hz, and randomly generated in each iteration. If the implemented resource allocation scheme is not able to provide a user its target SINR, the BS with maximum power and maximum number of antenna is assigned to that user. In Figure 2, BS power consumption for three values of MBS = 2, 4 and 8 is investigated. As shown, power consumption for antenna allocation technique

260

for all MBS values is less than the traditional power allocation scheme. As can be seen, in lower SNRs, power consumption of BS in antenna allocation, for different numbers of MBS is the same. This is due to the fact that the number of activated antennas in the proposed scheme is variable. On the other hand, in

13

Table 1: Simulation Parameters[8],[24]

Parameter

value

am

7.35

bm

2.9

Cell Radius

1 Km

K

−35.28 dB

MBS (antenna/sector)

2/4/8

N0

−174 dBm

NCoM P

3

Pantenna,max

2 W att

PSP

58 W att

Sector/Cell

3

λ

4

power allocation technique, number of antennas is constant. Form this figure, 265

one can conclude that BS’s power consumption is more affected by the number of active antennas than their transmit power. With increase of average target throughput, BS power consumption of antenna allocation technique approaches that of power allocation technique. This is due to the fact that at high target throughputs, both techniques require activation of all available antennas, and

270

allocate maximum power to each of selected antennas. Therefore, BS power consumption for both schemes is the same at high average throughputs. In Figure 3, average assigned data rates to all users has been plotted versus the target data rate of users. As shown, average data rate of the proposed antenna allocation scheme is higher than power allocation technique. In addition,

275

with increasing user target data rates, BSs in both schemes tend to use maximum number of available antennas, each with maximum transmit power. In this work, neither of antenna allocation for power allocation can provide some users with their target data rates. Therefore, difference between average allocated data rate and target data rate increases with increase in user target data rate.

14

2200

BS power consumption (Watt)

2000 1800 Traditional (MBS=2) 1600 1400

Traditional (MBS=4) Traditional (MBS=8) Proposed (MBS=2)

1200 1000

Proposed (MBS=4) Proposed (MBS=8)

800 600 400 1

2

3

4

5

6

7

8

9

10

Required average throughput (capacity) for each user (b/s/Hz) Figure 2: Power consumption comparison between power allocation (Traditional) and antenna allocation (Proposed) techniques in single macro BS.

280

Improved power allocation techniques can be adopted to increase the average allocated data rate, which is beyond the scope of this paper. In Figures 4 and 5, simulation results, BS power consumption and average allocated data rate, are presented for a CoMP system. In Figure 4, it is interesting to note that in low target data rates, CoMP system with MBS = 8

285

using antenna allocation technique has lower power consumption than power allocation technique with MBS = 4. As it can be seen from Figure 5, similar to observations from Figure 3, the allocated rate to a user via the proposed scheme is much higher than that of power allocation scheme. This due to the fact that in the proposed antenna allocation scheme, once an antenna is selected,

290

it transmits with full power In figure 6, SER of the two resource allocation techniques are compared. For these results, a target user SNR has been selected. Limitation in number of antennas and transmit power has been considered. As it was shown in Section 4.3, antenna allocation technique has lower SER compared to power allocation

295

scheme.

15

10

Average total data rate (b/s/Hz)

9 8 7 6 5

Traditional (MBS=2) Traditional (MBS=4)

4

Traditional (MBS=8) Proposed (MBS=2)

3

Proposed (M =4) BS

2 1

Proposed (MBS=8) CTarget

1

2

3

4

5

6

7

8

9

10

Data rate (capacity) for each user (b/s/Hz) Figure 3: Required data rate comparison between power allocation (Traditional) and antenna allocation (Proposed) techniques in single macro BS

4 KW

Traditional (MBS=2) Traditional (MBS=4)

BS power consumption (Watt)

3.5 KW

Traditional (MBS=8) Proposed (MBS=2)

3 KW

Proposed (MBS=4) 2.5 KW

Proposed (MBS=8)

2 KW

1.5 KW

1 KW

0.5 KW

1

2

3

4

5

6

7

8

9

10

Required average throughput (capacity) for each user (b/s/Hz) Figure 4: Power consumption comparison between power allocation (Traditional) and antenna allocation (Proposed) techniques in CoMP BSs

16

10

Average total data rate (b/s/Hz)

9 8 7 6 Traditional (MBS=2)

5

Traditional (MBS=4)

4

Traditional (MBS=8) Proposed (MBS=2)

3

Proposed (M =4) BS

Proposed (MBS=8)

2

CTarget

1

1

2

3

4

5

6

7

8

9

10

Data rate (capacity) for each user (b/s/Hz) Figure 5: Required data rate comparison between power allocation (Traditional) and antenna allocation (Proposed) techniques in CoMP BSs

0

10

Symbol error rate

Traditional Proposed −1

10

−2

10

−3

10

1

2

3

4

5

6

7

8

9

10

11

12

SNR target in mobile station ( MBS = 4 ) Figure 6: Symbol error rate comparison between power allocation (traditional) and antenna allocation (proposed) techniques in single Cell

17

6. Conclusion In this paper, the problem of power consumption reduction in cellular communication systems for downlink of TDMA-based single/multi-cell multi-antenna systems was investigated. In this regard, to accommodate each user in the net300

work with its required data rate, antenna-allocation at the BS against the conventional power-allocation was recommended. It was shown that the proposed method reduces the total power consumption of the BS (sum of transmitted power and the power used at the base station for signal processing) compared to the traditional power allocation technique. As well, it was shown that in

305

addition to reducing power consumption, the proposed antenna-allocation technique, has a higher average user-rate than existing traditional scheme and results in lower SER.

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