Received: 4 March 2016
Revised: 27 November 2016
Accepted: 23 December 2016
DOI 10.1002/dac.3279
RESEARCH ARTICLE
Power optimization using optimal small cell arrangements in different deployment scenarios Akhil Gupta
| Rakesh Kumar Jha
Department of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Jammu and Kashmir, India Correspondence Akhil Gupta, Research Scholar in Department of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, J&K, India. Email:
[email protected] Rakesh Kumar Jha, Assistant Professor in Department of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, J&K, India. Email:
[email protected]
Summary For smooth transition from 4G to 5G, there is a need for optimizing the power in the wireless communication. In 5G, it is expected that the number of users will increase drastically that correspondingly increase the utilization of power in the transmitter and receiver sides. So researchers and academicians are now finding ways to optimize the power. Some optimizing methods like convex optimization are very helpful for optimized algorithm design. In this paper, a proposed mathematical approach for deployment of small cell access point is used for optimizing the power consumption in the massive multiple input and multiple output and small cell scenario. The new proposed mathematical approach will also help in deciding the optimal number of small cell access points and optimal location of these small cell access points for the particular deployment scenario like urban macro heterogeneous deployment scenario in the 3GPP LTE standard and different macro deployment scenario in the ITU‐R M.2135 standard like urban macro, suburban macro, and rural macro, for optimizing the power. K E Y WO R D S
5G, massive MIMO, power optimization, small Cell
1 | IN T RO D U C T IO N In the next generation of wireless communication, wireless data usage is increasing at a faster rate. But the frequency spectrum that is available for use is scarce and expensive. So, for us to meet the growing demand of wireless services, modern wireless networks are required to operate in an efficient manner. Hence for improving the efficiency of the wireless services, the application of mathematical optimization methods including classic optimization methods and multiobjective optimization tools plays a key role. Classic optimization methods comprise of integer, semidefinite, linear, and convex optimization, while game theory, majorization theory, and statistical approximations are different multiobjective optimization tools.1 Among the different classic optimization methods, convex optimization has proved to be the most appropriate technique for algorithm design in the present wireless generation. Thus, it has become the most preferable engineering tool for the researcher's globally.2 So the power can be optimized using Int J Commun Syst. 2017;e3279. https://doi.org/10.1002/dac.3279
the convex optimization method. Moreover by optimally arranging the small cell access (SCA) points in the given deployment scenarios, power can be further optimized. It is extensively recognized that the growing capacity need of the next generation wireless networks can only be fulfilled by momentous densification of the network with the deployment of small cells.3,4 Small cells provide an effective way of enhancing the local capacity like hotspots in urban areas. Network densification can also be done by implementing massive multiple input and multiple output (MIMO) network using increased number of antennas at each cell location.5 With the increased number of antennas, intracell and intercell interference6 can be proficiently reduced by using the massive MIMO technology having large number of antennas that can concentrate the radiated energy precisely towards the projected receivers. Furthermore, massive MIMO multiplexed the data streams for different terminals on the same time‐frequency resource by using the extra spatial degrees of freedom. In the recent times, the idea of deploying SCA points in the macro cell scenario having massive MIMO has attracted
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a lot of researchers. Massive MIMO and SCA in the macro cell scenario form the heterogeneous wireless network. Similar to their macro cell counter parts, SCA points in the heterogeneous network that occupy the same amount of bandwidth. Deployment of SCA points in the macro cell scenario is beneficial in efficiently offloading the traffic, increased capacity due to frequency reuse, and increased cell coverage even for the cell edge users. But the success of this deployment concept depends on the efficient deployment approach.7 In the past, the deployment of macro base station solely emphasizes on the enhancement of coverage.8 This is the reason behind the efforts of the world wide researchers for identifying coverage holes, ie, identifying the areas deprived of the coverage. Small cell access deployment paved the way not only towards the coverage enhancement but also towards the capacity improvement and power optimization. Some important factors that help in determining the desirable locations of SCAs are given below7: • Interference instigated to the user equipments (UEs) that are being served by SCAs points from the macro base stations (BSs)—This type of interference significantly affects the performance of UEs of SCAs because the transmit power of macro BSs is 46 dBm, which is considerably higher than the SCAs having a transmit power of 30 dBm.9 Hence, the deployment of SCAs should be such that the interference between macro BS and UEs of SCAs is less severe. • Interference instigated to the UEs that are being served by macro BSs from the SCAs—This type of interference has comparatively less effect from the interference from the macro BS to macro UE because of the low‐transmit power of SCAs. • User equipment association—SCAs should be deployed in such locations or arrangements such that more number of UEs that are formerly being attended by the macro BSs are now being attended by the SCAs. This will help in the optimal utilization of the resources that will correspondingly increase the throughput. • Existing geometry locations of macro UEs—From the existing geometry locations of macro UEs in the macro cellular system, it is identified that there exist network coverage holes. Hence, SCAs should be deployed in such arrangements such that there will be no network coverage holes. • User equipment density—It is a vital factor in defining the optimal locations for the deployment of SCAs. Deployment locations should be according to the UE density such that maximum number of UEs can be offloaded from macro BSs to SCAs. This deployment arrangement helps in balancing the load between macro BSs and SCAs, which correspondingly improve the spectral efficiency and system capacity.
1.1 | Background of research Small cell deployment has been extensively studied in the literature, particularly for the urban environment.10 Present day researchers and academicians are dedicated to decide the optimal small cell deployment arrangements while considering the different performance metrics. For maximizing the spectral efficiency of the network while avoiding the interference posed by SCAs is proposed in 1 study.11 For finding the optimal locations for placing the small cells in throughput as the performance metric is implemented in previous studies.7,12 The effect on throughput and spatial outage performance of using different deployment topologies like random and grid topologies is considered in the work of Chen et al,13 While an outage‐minimum deployment mechanisms under realistic metropolitan scenarios is formulated in previous studies.14–17 Most of the researches are also working on the feasibility of mobile small cell deployment arrangements.18 According to the background study, it is clear that the work that has already been done on small cell deployment does not focus on optimizing the power of the network. Contribution: In this paper, a new mathematical approach has been proposed that can optimized the power consumption, although considering the quality of service (QoS) management at the user end and power optimization at the SCA, and BS. Power optimization can be achieved by transforming the classical macro cell scenario by overlaying it with SCA and massive MIMO at the BS. The new mathematical approach will help in finding the optimal locations for the deployment of SCAs with optimized power. This new mathematical approach will also help in deciding the optimal number of SCAs and optimal location of these SCAs for the particular deployment scenario like urban macro (UM) heterogeneous deployment scenario in the 3GPP LTE Standard (case 1) and different macro deployment scenarios in the ITU‐R M.2135 standard (case 2) like UM, subUM (SUM), and rural macro (RM), for optimizing the power.
2 | SYSTEM MODEL For realizing the massive MIMO and small cell model, first, we have to consider a structure model consisting of a macro base station having NBS antennas with a capacity of serving K single antenna users and S ≥ 0 number of randomly deployed SCAs to form a heterogeneous network. For optimizing the power, SCAs should be fitted with 1 ≤ NSCA ≤ 2 antennas each and following the strict power constraints resulted in limited coverage area.19 But for high QoS targets in a large coverage region, BS should support considerable power constraints. This can be achieved by using massive MIMO at the BS with NBS, should be NBS ≫ K, and is ranging from 8 to 100.19 In the structure model, there is no need of channel estimation because we are using time division duplexing (TDD), thus helps in optimizing the power. There is a block‐fading channel for user k and single flat–fading channel represented
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1×NBS 1×NSCA in the baseband as hH and hH , for base k;0 ∈C k;j ∈C station and SCA, respectively. Hence, the received signal y at user k is S H yk ¼ hH k;0 x0 þ ∑j¼1 hk;j xj þ nk ;
processing and filters, mixers, and converters that are encompassing in the circuits of each antenna. The equation is also normalized with the total number of subcarriers C ≥ 1. Each BS and SCA is susceptible to Lj power constraints
(1) ∑Kk¼1 wH k;j Qj;l wk;j ≤ qj;l ; l ¼ 1; ……; Lj:
where x0 and xj represents the transmitted signals at the BS and jth SCA point, respectively. The term nk e∁N 0; σ2k is the circularly symmetric complex Gaussian receiver noise with zero‐mean and variance σ2k , measured in milliwatt.18 For obtaining the transmitted signals, xj ¼ ∑Kk¼1 wk;j xk;j ; j ¼ 0; …; S:
(2)
The information symbols to user k, denoted as xk , 0 and xk , j, from the BS and the jth SCA, respectively, originates from the independent Gaussian codebooks as xk , j ~ CN(0, 1), for j = 0,…,S having unit power (in mW) are multiplied with the beam‐forming vectors wk;0 ∈C1×N BS and wk;j ∈C1×N SCA . These beam‐forming vectors will play an important role in solving the optimization problem. As wk , j ≠ 0, so the task of assigning the transmitter is done automatically and optimally from the optimization problem for j transmitters that are serving k number of users.19 One among the premium objectives of this paper is to achieve power optimization while maintaining the QoS. The QoS is represented as information rate in bits per second per hertz. This QoS is to be achieved in parallel by each user. These are defined as log2(1 + SINRk) ≥ γk, where γk is the fixed QoS target and
SINRk ¼ ∑Ki ¼ 1
2 2 H S w hk;0 wk;0 þ ∑j¼1 hH k;j k;j 2 2 H S H hk;0 wi;0 þ ∑j¼1 hk;j wi;j þ σ 2k
(3)
i≠k
is the combined signal to interference and noise ratio (SINR) of the kth user. Here, log2(1 + SINRk) is the representation of the information rate that is attained when the successive interference cancellation is applied on the own information symbols, whereas the couser symbols are treated as noise.20 The total power consumption per subcarrier can be represented as Pdynamic + Pstatic,21–23 where 2 2 Pdynamic ¼ ρ0 ∑Kk¼1 wk;0 þ ∑Sj¼1 ρj ∑Kk¼1 wk;j ; Pstatic ¼
S η η0 j N BS þ ∑ N SCA ; C j¼1 C
(4) (5)
where the dynamic power consumption encompass the emit 2 ted powers, ∑K wk;j ; each multiplied with a constant k¼1
ρj ≥ 1 signifying the inefficiency of the power amplifier at the specific transmitter.19 Although, the static power consumption is proportional to NBS and NSCA. ηj ≥ 0 in the equation implies the power dissipation because of baseband
(6)
The weighting matrices Q0;l ∈CN BS ×N BS , Qj;l ∈CN SCA ×N SCA ; for j = 1,…,S, are positive semidefinite. The equivalent limits are qj , l ≥ 0. The parameters Qj , l , qj , l are fixed and can define any combination of per‐antenna, per‐array, and per soft‐shaping constraints.24 Normally, the BS provides coverage, so we have q0 , l≫qj , l for 1 ≤ j ≤ S. The numerical calculation considers per‐antenna constraints of qj [mW] at the jth transmitter, given by L0 = NBS , Lj = NSCA , qj , l = qj ∀ l, and Qj , l with one at lth diagonal element and zero elsewhere. So for optimizing the power, we have to reduce the total power consumption although considering the QoS constraints and power constraints for19 minimize Pdynamic þ Pstatic ; wk;j ∀k;j
subject to log2 ð1 þ SINRk Þ ≥ γ k ∀k;
(7)
∑Kk¼1 wH k;j Qj;l wk;j ≤ qj;l ; ∀j; l:
For realizing the power‐optimized massive MIMO and small cell scenario, there should be certain algorithms that may be helpful for deciding the deployment positions of the SCA in the given scenario. There are certain approaches that may be used for the deployment of SCA7: • Random deployment—This is a basic approach of SCA deployment in which SCAs are deployed randomly. • Edge planned deployment—This approach is used for mitigating the problem of coverage holes by deploying the SCAs near the cell edge of a macro sector without considering shadow fading and approximated by considering its path loss only. • Farthest cluster center deployment—This approach is applicable by forming UE clusters per macro cell. Then an SCA is placed at the center of each cluster that is farthest from its macro BS. But for the case of number of SCAs larger than the number of clusters, the remaining SCAs are deployed randomly. The above mentioned deployment algorithms are complex and fulfilling the certain objectives while leaving the others. A deployment algorithm should be such that it will cover the entire area with maximum users while maintaining the minimum interference with less power consumption. In the next section, a new mathematical approach has been proposed that will provide the optimal locations for the deployment of SCA's for power optimization.
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3 | PROP OS ED AP PROAC H The heterogeneous 5G wireless communication network as shown in Figure 1 comprises of relay, small cell, microcell, and macrocell. These divisions in the network are very useful in increasing the coverage region. In this paper, massive MIMO and small cell concepts has been implemented in the urban, suburban, and rural conditions having variable user density. The total number of users taken in the given scenario will be 9, 10, 14, and 22 users depending on the different deployment arrangements of 3, 4, 8, and 16 SCA points, respectively. The standard for optimal number of users is not fixed. We have taken these numbers randomly for performing simulations. The main focus of this paper is on the power optimization of the 5G wireless communication network. Power can be optimized by using massive MIMO and small cell approach as shown in previous studies.19,25 But if the number of SCA and their respective location varies for a particular scenario, then it will also affect the power optimization factor. Hence for better power optimization, a new mathematical approach for deployment of SCA is proposed as given below. This approach is tested on a massive MIMO and small cell scenario. In this approach, the deployment scenario is considered as circular and is divided into 4 quadrants for attaining low complexity and better coverage. There are some disadvantages like increased interference, inefficient spectrum usage, less coverage of using random, edge‐planned, and farther cluster center deployment algorithms. But our proposed mathematical approach for deployment of SCA will help in optimizing the power of the network while maintaining the optimal coverage, minimum interference, and efficient spectrum utilization. In the proposed mathematical approach shown below, there are 4 arrangements or deployment arrangements that has been realized on the massive MIMO and small cell scenario as depicted in Figure 2A‐D. The * in the given figures is representing the users outside the small cell, while # is representing the users inside the small cells. These 4 arrangements as given in the mathematical approach help in finding the optimal deployment locations
FIGURE 1
in terms of x‐y coordinates and angle θ. Simulation results shown in Section 5 using the proposed mathematical approach help in deciding the optimal number of SCA points and optimal location of these SCA points for the particular deployment scenarios based on power consumption. Proposed mathematical approach for deployment of SCA for power optimization Step 1. Define circular area of radius (R) R = 0.5 km for urban and 3GPP urban scenario. R = 1.299 km for suburban scenario. R = 1.732 km for rural scenario. Step 2. For optimal SCA arrangements Divide the circular area into 4 sectors of sector angle of 90 Step 3. Deployment of optimal number of SCAs depending upon spacing for avoiding interference among SCAs Arrangement 1. Deploy 3 SCAs on 4 quadrants pffiffiffi 3R ; y ¼ − R ; R ; θ ¼ ð4n þ 1Þ π where n ¼ 0; 1; and 2: x ¼ 0; 4 2 4 6
Arrangement 2. Deploy 4 SCAs on 4 quadrants R R π x ¼ ; y ¼ ; θ ¼ ð2n þ 1Þ where n ¼ 0; 1; 2; and 3: 2 2 4 Arrangement 3. Deploy 8 SCAs on 4 quadrants R R R π x ¼ ; ; y ¼ ; θ ¼ ð2n þ 1Þ ; 2 4 2 4 1 3 9 11 where n ¼ 0; ; ; 1; 2; ; ; and 3: 4 4 4 4 Arrangement 4. Deploy 16 SCAs on 4 quadrants R R R R π x ¼ ; ; y ¼ ; ; θ ¼ ð2n−1Þ ; 2 4 2 4 4 3 5 7 9 11 13 15 17 where n ¼ ; 1; 1; ; ; 2; 2; ; ; 3; 3; ; ; 4; 4; and : 4 4 4 4 4 4 4 4
The heterogeneous 5G wireless communication network. MIMO indicates multiple‐input and multiple‐output
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FIGURE 2
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A, Optimal 3 small cell access (SCA) deployments. B, Optimal 4 SCA deployments. C, Optimal 8 SCA deployments. D, Optimal 16 SCA
deployments
Figure 2A‐D shows the different deployment arrangements as proposed in the mathematical approach Figure 2A shows the optimal 3 SCA deployment arrangements. This type of arrangement covers maximum area and maximum number of user with minimum interference among the SCAs. Figure 2B shows the optimal 4 SCA deployment arrangements that will significantly cover the area symmetrically but with a tolerable interference level among SCAs. Figure 2C shows the optimal 8 SCA deployment arrangements that will significantly cover the area, but the interference among the SCA increases. Figure 2D shows the optimal 16 SCA deployment arrangements that will completely cover the entire area with every single user in it. But the interference level among the SCA and among the user increases to a nonacceptable level.
3.1 | Analysis The new mathematical approach has been proposed for the power optimized deployment of SCAs in the different deployment scenarios. The other deployment algorithms like random deployment, edge‐planned deployment, and farthest cluster center deployment are complex and not satisfying
the basic objectives of covering the entire area with maximum number of users, while maintaining the minimum interference with less power consumption. The proposed deployment approach will help in combating the near far problem and increasing the coverage region. This deployment arrangement is symmetrical and thus helps in covering the maximum area with minimum amount of interference among the deployed SCAs.
4 | S I M U L AT I O N PAR A M ET E R S The simulation parameters used for implementing the scenario depicted in Figure 2A‐D are included in this section. The figures shown above are representing a circular macro cell overlaid by 3, 4, 8, and 16 SCA points in 4 different arrangements. Each arrangement is having 9, 10, 14, and 22 users, respectively, in which 6 users are uniformly distributed in the whole cell in each arrangement, while 1 user is in each SCA within 40 m. Table 1 shows the parameters that are used in the simulation process. It contains the path loss models for the different propagation scenarios that have been standardized in previous studies9,26–31 and the hardware parameters22 that
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TABLE 1
Simulation parameters for the scenario
Parameters
Values
Carrier frequency
2 GHz
Number of channel realizations
5
Number of subcarriers
600
Total bandwidth
10 MHz
Subcarrier bandwidth
30 KHz
Noise figure
5 dB
Noise floor, dBm
−174 + 10log10(subcarrier bandwidth) + noise figure
Number of transmitters Number of subscribers in different SCA deployment arrangements
1 BS + number of SCAs 3 SCAs 4 SCAs 8 SCAs 16 SCAs
9 10 14 22
QoS constraint
2 bits/s/Hz per user
SINR constraints per user
(2QoS constraints − 1) * ones(number of users, 1)
Efficiency of power amplifiers
1 ρ0
Circuit power per antenna
η0 = 189 mW , ηj = 5.6 mW ∀ j
¼ 0:388; ρ1j ¼ 0:052∀j
Per‐antenna constraints
q0 , l = 66 , qj , l = 0.08 mW ∀ j , l
UM cell radius
0.5 km
SUM cell radius
1.299 km
RM cell radius
1.732 km
Minimal user distance from BS
0.035 km
Minimal user distance from SCA point
0.003 km
Standard deviation of log‐normal shadowing
7 dB
Penetration loss
20 dB
SCA radius
0.04 km
Path and penetration loss at distance d, km
148.1 + 37.6log10(d) dB
Path and penetration loss within 40 m from SCA
127 + 30log10(d) dB
Path loss for UM at distance d, km
199.5653 + 39.0864log10(d) dB
Path loss for SUM at distance d, km
180.4953 + 38.64log10(d) dB
Path loss for RM at distance d, km
177.8878 + 38.64log10(d) dB
Abbreviations: BS indicates base station; QoS, quality of service; RM, rural macro; SCA, small cell access; SINR, signal ; SUM, sub urban macro; UM, urban macro.
characterize the power consumption. The standard for optimal number of users is not fixed. We have taken these numbers randomly for performing simulations.
5 | SIMULATION RE SULTS This section consists of the results and interpretations of the performed simulations in the given massive MIMO and small cell scenario for power optimization. The simulations has been performed on different deployment scenarios like UM heterogeneous deployment scenario in the case9 1 and different macro deployment scenarios in case26 2 like urban, suburban, and rural, using the new proposed mathematical approach for deployment of SCA, while the convex optimization problems were solved by the algorithmic toolbox SeDuMi,32 using the modeling language CVX.33 The simulation result shown in Figure 3 clearly depicts that for the UM heterogeneous deployment scenario in case
1, the new method of SCA deployment using the proposed mathematical approach consumes less power per subcarrier as compared with the method in which the SCAs are deployed in the random manner. Hence for optimizing the power, the proposed mathematical approach for the deployment of SCAs proved to be useful as compared with the random SCA deployment arrangement. A. UM heterogeneous deployment scenario in the case 1 Figure 4 shows the comparison of average total power consumption for different deployment arrangements of SCAs given in Figure 2A‐D. It depicts that if we deploy SCAs similar to the arrangement shown in Figure 2B, ie, 4 SCA arrangements, then overall, there will be less power consumption but with the increase in the number of antennas at the base station, the total power consumption increases. But if we deploy SCAs similar to the arrangement shown in Figure 2A, ie, 3 SCA arrangements, then total power consumption decreases as we increase the number of antennas.
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Average total power consumption for random and proposed small cell access (SCA) deployment approach for the channels similarly to urban macro heterogeneous deployments in case 1. BS indicates base station
FIGURE 3
Average total power consumption for different deployment arrangements in Figure 2A‐D for the channels similarly to urban macro heterogeneous deployments in case 1. BS indicates base station; SCA, small cell access
FIGURE 4
Hence for UM heterogeneous deployment scenario in the case 1, three SCA deployment arrangement is optimal among the rest of the arrangements. B. Deployment scenarios in the case 2 A, Average total power consumption for different deployment arrangements in Figure 2A‐D for the channels similarly to urban macro deployment scenario in case 2. B, Average total power consumption for different deployment arrangements in Figure 2A‐D for the channels similarly to sub urban macro deployment scenario in case 2. C, Average total power consumption for different deployment arrangements in Figure 2A‐D for the channels similarly to rural macro deployment scenario in case 2. BS indicates base station; SCA, small cell access
FIGURE 5
Figure 5A‐C represents the average total power consumption for the UM, SUM, and RM deployment scenarios, case 2, respectively. Figure 5A illustrates the comparison of average total power consumption for different deployment arrangements of SCAs given in Figure 2A‐D. It depicts that if we deploy SCAs similar to the arrangement shown in Figure 2A, ie, 3 SCA arrangement, then it will consume less power as compared with rest of the deployment arrangements. There is a common reason for 3 SCA deployment arrangements to be optimal in both UM heterogeneous deployment scenario in case 1 and UM deployment scenario in case 2. In urban scenarios, we cannot deploy SCAs similar to the arrangement shown in Figure 2C,D, ie, 8 SCAs and 16 SCA arrangements, respectively. It is because of the high density of users as well as the structures like buildings, vehicles,
etc in the urban scenario, which results in the increased power consumption because of the increased interference among SCAs and users. But among the deployment arrangements shown in Figure 2A,B, ie, 3 SCAs and 4 SCA arrangements, respectively, 3 SCA deployment arrangements are optimal for urban scenario. The reason behind this optimality is related with the cell size. For accumulating more number of users and increasing frequency reuse ratio, the size of the cell is constantly
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decreasing. Hence, only 3 SCA deployment arrangements will consume less power and provide optimal coverage with low cost for urban conditions having high‐user density. Figure 5B illustrates the comparison of average total power consumption for different deployment arrangements of SCAs given in Figure 2A‐D. It depicts that if we deploy SCAs similar to the arrangement shown in Figure 2C, ie, 8 SCA arrangements, then there will be less power consumption because in suburban conditions, user density is lesser as compared with urban. Hence, 3 SCAs and 4 SCA deployment arrangements are not preferable. Additionally 16 SCA deployment arrangements are also not preferable because of high cost for less user density. Figure 5C illustrates the comparison of average total power consumption for different deployment arrangements of SCAs given in Figure 2A‐D. It depicts that if we deploy SCAs similar to the arrangement shown in Figure 2B, ie, 4 SCA arrangements, then there will be less power consumption because in rural conditions, the cell size is high, so 3 SCA deployment arrangements might not be able to cover the entire region. Along with the large cell size, the user density is also very low, so deploying 16 SCAs and 8 SCA arrangements will not be cost effective.
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6 | C O NC LUS I O N In this paper, different small cell arrangements for 5G or next generation networks have been studied. A new mathematical approach has been proposed for the deployment of SCA that can minimize the power consumption, although considering the QoS management at the user end and power optimization at the SCA, and BS. Power optimization can be achieved by transforming the classical macro cell scenario by overlaying it with SCA and massive MIMO at the BS. The simulations have been performed to show that our proposed mathematical approach helps us in deciding the optimal number of SCAs and optimal location of these SCAs for the particular deployment scenario like UM heterogeneous deployment scenario in case 1 and UM, SUM, and RM deployment scenarios in case 2, while optimizing the power. It is concluded that 3 SCA deployment arrangements will consume less power and provide optimal coverage with low cost for both UM heterogeneous deployment scenario in case 1 and UM deployment scenario in case 2 having high user density, while 8 SCAs and 4 SCA deployment arrangements will consume less power for SUM and RM deployment scenarios in the case 2, respectively.
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How to cite this article: Gupta A, Jha RK. Power optimization using optimal small cell arrangements in different deployment scenarios. Int J Commun Syst. 2017;e3279. https://doi.org/10.1002/dac.3279
