NETWORK ROUTING AND PROTOCOL
Distributed Energy Saving Mechanism Based on CoMP in LTE-A System Fei Zheng 1, Wenjing Li 1*, Luoming Meng 1, Peng Yu 1, Lei Peng 2 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China 2 Electronics and Information Engineering Faculty, Jinggangshan University, Jian, Jiangxi Province, 343009,China 1
Abstract: To reduce the energy consumption of the LTE-A system, a distributed energy-saving mechanism based on CoMP (CoMPDESM) is proposed to solve the inadequate coverage problem under the dormant cells. First, the network is divided into clusters based on the equivalent cell principle. Then, we transfer global optimization into a group of subproblems. Second, a joint processing-based cooperative cell selection model is constructed to determine cooperative cells and dormant cells. Third, the compensative cells with a determined threshold are selected to control users’ access. Finally, a simulation is implemented in Matlab. Results show that the energy-saving rate can reach 36.4% and that the mechanism meets the network coverage requirement. Thus, joint processing can be effectively applied in an energy saving mechanism and used to improve the network performance of edge users without increasing transmission power. Keywords: LTE-A; distributed energy-saving mechanism; CoMP; equivalent cell principle; energy-saving rate
I. INTRODUCTION Green communication is currently an important issue. Wireless network consumes a considerable amount of in ICT industry because it uses a large number of access nodes. Thus, an
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important research topic is how energy can be saved in a wireless access network [1–2]. As a new technical standard in LTE-A, coordinated multiple point (CoMP) [3] can be applied in an energy-saving mechanism to reduce energy consumption. The CoMP technology expands cells’ coverage better than traditional compensation technologies, such as transmission power improvement and antenna tilt adjusting. Several technologies, including joint processing (JP) and cooperative scheduling, support CoMP. Using JP to expand cells’ coverage and implementing energy conservation with fewer cells are significant issues. To solve the above issue, this paper integrates link budget and received signal-to-interference-plus-noise ratio (SINR) level into an abstract distributed energy-saving mechanism based on CoMP (CoMP-DESM). First, the network is divided into clusters based on the equivalent cell principle with a distributed approach. Transferring global optimization into a group of subproblems has good flexibility, strong robustness, and low complexity [4]. Second, a JP- based cooperative cell selection model is proposed to determine cooperative cells and dormant cells, with a greedy algorithm being used for the solution. Third, the compensative cells with a determined threshold are selected to control users’ access. Finally, the simulation is implemented in Matlab. The rest of this paper is organized as folChina Communications • July 2016
lows: In Section 2, recent research results are investigated. In Section 3, the CoMP-based energy-saving mechanism is introduced, including clustering cells, selecting cooperative cells in each cluster, and redistribution of users in dormant cells. In Section 4, the performance of the proposed mechanism is evaluated. Conclusions and discussions are given in Section 5.
II. RELATED WORK Currently, research on energy saving mechanisms can be divided into two aspects: planning and operation. In the network planning phase, an energy-aware network planning strategy based on CoMP is proposed in reference [5]. The network planning problem of mixed integer programming is solved by using Lagrange relaxation method. However, the approach is centralized rather than distributed. The stochastic geometry theory is adopted in reference [6] to analyze the optimization problem of cell density with QoS constraints and theoretical upper and lower bounds. This approach remains in the theoretical planning level. In the operating phase of energy saving, 3GPP presents a variety of energy saving cases in multiple scenarios, e.g., [7]. One scenario involves switching the energy saving state when it could be covered by other cells. The conceptions in graph theory are adopted in reference [8] to describe associations between users and base stations, which are abstracted to construct a binary integer programming problem. Two issues related to energy saving mechanisms are theoretically analyzed in reference [9] for cell execution and corresponding user redistribution. It aims to minimize the cost between energy efficiency and performance with user-BS association strategy and greedy algorithm. The above studies indicate no concrete methods and network coverage compensation. A clustering method based on equivalent cells is proposed in reference [10]. However, this method is inefficient because enhancing transmission power would introduce interferences into the network, thereby requiring frequent China Communications • July 2016
adjustment of network parameters. Reference [11] focuses on finding a tree topology with a minimum number of child nodes. However, selecting the cooperation cell group is difficult. Multi-cell cooperation technology is applied in traffic-aware, energy-aware, and energy-efficient transmission [12]. Reference [13] attempts to reduce the energy consumption of a cellular network through multi-cell cooperation. In these two papers, we proposed the new energy saving idea that the coverage of cooperative cells can be expanded by adopting CoMP to compensate for dormant cells without increasing transmission power. Therefore, network energy management based on CoMP technology is further studied and improved in this paper.
III. ENERGY SAVING MECHANISM BASED ON JP In this paper, CoMP is introduced into an energy-saving mechanism. The following problems need to be addressed: (1) Reducing the load of shared information via distributed method. (2) Designing a compensation strategy for maximum coverage. (3) Accessing the best service cell for users. To address these challenges, we introduce a distributed energy saving mechanism based on CoMP.
3.1 Clustering cells Given a region covered by a cell set , the optimal combination of cooperative cells and corresponding dormant cells should be selected from this set to meet network performance requirements while increasing dormant cells. If the exhaustive search algorithm is used, then the time required for solving the selection function is T(Num). The calculation space of selection function is represented by L as shown in Equation (1).
(1)
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With the growth of Num, L would be unacceptable. When binary search algorithm is adopted, the binary number is , which is to say the time complexity is O(Num). The cell number is halved each time and needs to traverse all p(Num) cell combinations. Thus, the time complexity is O(p(Num)), and the total time complexity of the algorithm can be shown in Equation (2). (2) The algorithm that solves the selection function has significant time complexity. To solve this issue effectively, cell clustering is adopted in this paper as shown in Fig. 1. The global optimization problem is transformed into a group Clustering algorithm: 1 Input: eNB, r, R, num_cell 2 //Step 1: sorting cells 3 for any eNB i 4 dis(i)=|(eNB(1,i),eNB(2,i))|;// distance between eNBs and coordinate origin; 5 end for 6 sort(dis) in ascending order; 7 for all eNBs 8 sort(eNB and r) on dis; 9 end for 10 //Step 2: clustering cells 11 initialize cluster=zeros(1,num);// initially cells are independent; 12 initialize j=1;//order number of clusters; 13 for i=1:num 14 if Eq(3) 15 cluster(i)=j; j=j+1; 16 end for 17 Output: cluster;
of optimal sub-problems, thereby reducing the computational complexity of the original global optimization problem to an acceptable level. Clustering principle based on equivalent cell is adopted in this paper [10]. If cells can substitute and meet the quality of service, then a group of cells are mutually equivalent. Such an equivalent cell group is called a cluster, and the constraint distance between equivalent cells is called clustering radius (CR). The details are as follows: Given a cell set of network S, where the radius of cell i is , when CR (R) is known, a exists. Any cluster set is a cluster, and let denote an element of , where, j denotes the serial number among , and denotes the serial number among S. (3) If two cells and meet Equation (3), then they are mutually equivalent cells. Cell clustering should group cells with the strongest mutual interference into the same cluster. To make as many cells as possible dormant, as many cells as possible should be grouped into clusters. The clustering problem can be formulated as follows:
(4) The pseudocode of the clustering algorithm is as follows:
3.2 Selecting JP cells in cluster
clustering
After clustering, a theoretical model based on area SINR is proposed to evaluate the best combination of JP cells in each cluster. A greedy algorithm is then executed to search the best combination of JP cells. 3.2.1 JP technology
Fig.1 Clustering cells
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When JP is adopted in a traditional cellular communication system, as shown in Fig. 2, the superimposed signals from cooperative cells can improve the total received signal power, China Communications • July 2016
reduce interference, and enhance the SINR of the downlink, which can be calculated as Equation (5). (5) Where
denotes Gauss white noise.
Equation (5) shows that
is the useful
superimposed signal power from two cooperative cells and
(8) where denotes a combination of cells in , and all different combinations compose a new set . Here is the i-th element in , and the denotes the number of . Let denote the absolute complement of .
is the sum of inter-
ference signal power from non-cooperating cells. Evidently, because of JP, interference signals are reduced to three cells from the original four cells. Then, useful signals can be enhanced, and SINR can be improved as well. Thus, JP can expand the coverage of cooperative cells without adjusting the transmission power or the antenna tilt of eNB. 3.2.2 Selecting JP Cells in Cluster The network optimization principle is to compensate for insufficient coverage caused by dormant cells. Thus, the optimization problem of cooperative cell combinations needs to be solved. First, we define an area signal strength function. Let (x,y)∈Z denote a location, and Z is the continuous area. The total received signal power in Z can be written as follows: (6) where P(x,y) denotes the received signal power at location (x,y). According to Equation (6), area SINR is defined as (7) and denote the useful where signal power and the interference signal power at location (x,y), respectively. is a QoS metric, which directly reflects the network performance in a specific area. Next, denotes a cluster composed , of a set of cells, namely, and
cell set is defined as
denotes outside
cluster
composed of another set of cells,
namely,
. According to
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, a new
(9) , for any According to the above dormant cell set, can be rewritten as follows:
l =1, ... L (10) and denote the rewhere and at ceived signal power from cells location (x,y), respectively. When is known as a closed space, is correlated only to l. Finally, the cooperative cell selection model is proposed (11) is the determined threshold. w here The L* denotes a set of the cooperative cell combinations, which minimizes cooperative cells. At the same time area, the SINR is larger than the predesigned . (12)
CoMP
BS#5
BS#5
I
I I
I BS#1
UE BS#1
I
C BS#1
BS#2
C
UE BS#1
BS#4
I BS#2 BS#4
I
CoMP cells BS#3
BS#3
Fig.2 JP technology
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As shown in Equation (12), the is searched to maximize , which means that the number of cooperative cells should be minimized and QoS requirements should be met.
solo cells and cooperative cells at the same time. This user accesses the proper serving cell according to the following rules: Step 1: The user preferentially accesses the solo cell.
3.3 Selecting service cells for users During the implementation of the energy saving mechanism, both cooperative cells and non-cooperative cells exist in the network. Therefore, how users access active cells is another key issue in this paper. 3.3.1 Traffic model Suppose the traffic capacity of a cell denoted by . At location (x,y), the traffic transfer requests arrival follows an inhomogeneous Poisson point process with arrival rate per unit area . The file sizes are independently distributed with mean [9]. Thus, the traffic load density is defined as follows: (13)
The traffic load of
(16) indicates when the user accesses solo cell . and denote useful signal from solo cell and interference signal from solo cell , respectively. Given the cooperation threshold , then (17) Whether or not the v-th cell takes part in cooperation, the user is served only by the v-th cell. Step 2: If Equation (17) cannot be satisfied in the first step, then cooperative cells will be considered.
is calculated with (14)
If the traffic load of an specific area Z needs to be calculated, then Z can be divided into a , where set of smaller areas is the area part covered by cell . The regional traffic load can be defined as follows: (15) 3.3.2 Users accessing cells After determining the cooperative cells, the remaining cells within the cluster are dormant cells. Only partial users need cooperation service in networks. Thus, the planning strategy of user access cells based on SINR threshold is adopted in this paper. Cooperation and dormancy of cells could change the network topological structure, thereby leading to a set of solo cells and a set of cooperative cells er, where a user
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that exist togeth. Assume that at location (x,y), can receive signals from both
(18) indicates SINR when the user accesses the cooperative set of . denotes the power strength of a useful signal from cooperative cell . The user accesses a solo cell or cooperative cells according to Equation (19)
(19)
Meeting traffic constraint, the user accesses a cell from which it receives the signal of maximum SINR.
IV. CALCULATION AND ANALYSIS 4.1 Evaluation index Two metrics are proposed to evaluate the perChina Communications • July 2016
formance of the energy saving mechanism. With the normal network taken as a reference, the evaluation indexes exhibit relativity. Performance degradation ratio (PDR) is defined as follows: At location (x,y), SINR is calculated as follows:
1600 m of CR is an inflection point. From 1300 m to 1600 m, the number of dormant cells approximately linearly increases by 3 or 4 per CR increment of 100 m. However, at both endpoints, the increment tends to be gentle, being only 1 or 2. When the CR is less than 1300 m, the cluster number is less than 10, and the cell number in each cluster is less
(20)
and denote the useful signal power from and the interference signal power from , respectively. (21) SINRpdr is the minimum SINR in a normal network. The relative evaluation metric PDR is denoted as the ratio of the area where SINR is less than SINRpdr to the total network area. (22) Dormant cell ratio (DCR) for energy saving is the ratio of the number of dormant cells to the total number of all cells. (23)
Fig.3 Coverage of cells
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4.2 Calculation and analysis
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To verify the effectiveness of the proposed mechanism, a simulation is implemented in Matlab.
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4.2.1 Simulation setting
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As shown in Figs. 3 and 4, an urban region of 5000 m*5000 m is served by 55 eNBs. The coverage radii of cells are randomly distributed from 300 m to 700 m. Each eNB with a single omnidirectional antenna is located in the center. Other parameters are as listed in Table I. 4.2.2 Results and analyses Fig. 5 indicates that the maximum number of cells can be dormant with different CRs. The increasing relationship between dormant cells number and CR is coincident. Each 1300 and
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0
1000
2000
3000
4000
5000
Fig.4 SINR map with cells power on Table I Network parameters Parameter
Value
Parameter
Value
Transmission power
20W
Tower height
35m
Carrier frequency
2.6GHz
Antenna gain
17dB
Penetration loss
20dB
Feeder loss
3dB
Shadow fading
7dB
Interference margin
0.5dB
White noise
-111dB
Receiver sensitivity
-114dB
SINRelt
-4.84dB
SINRcmp
3dB
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55 Active
50
Dormant
Number of dormant cell
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Trend
40 35 30 25 20 15 10 5 0
1200
1300
1400
1500
CR(m)
1600
1700
1800
Fig.5 Number of dormant cells under different CRs
Fig.6 Cells in all clusters (CR=1600 m)
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4000
3000
2000
1000
0
1000
2000
3000
4000
5000
Fig.7 SINR map with cells JP off (CR=1600 m)
than 4. The number of dormant cell is relatively small. Thus, when the CR is larger than
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1600 m, only 5 isolated cells remain; 4 cells have a large size, and another is difficult to cluster because of its location. Then we focus on the 1600 m CR to illustrate changes of the above scenario. First, Fig. 6 shows that network is divided into clusters with the 1600 m CR. The cluster size (cell number in a cluster) depends on the cell radii of the inside cluster. Second, the selection model is used to determine JP cells and dormant cells. The determined threshold of SINRelt is determined to be the value of SINRpdr. As shown in Fig. 7, network performance degradation and network topology change because of cell dormancy. The JP cells serve the network and compensate for coverage in Fig. 8. The new cell coverage varies more greatly than it did before, and JP cells inside the same cluster can be considered a super cell. Fig. 9 shows that SINR is enhanced obviously, and no clear boundary exists between JP cells. When the energy saving mechanism is running, the SINR CDF with different CR is comparable to the normal network in Fig. 10. The network performance is reflected by these CDF curves, which decline with increase of CR. JP can improve coverage and enhance performance of cell edge. Therefore, the performance with 1200 m CR and 1300 m CR is better than that of the normal network in intermediate part of curves. A clear and intuitive reflection is shown in Fig. 9. Particularly, Fig. 11 shows that the edge user SINR and the distribution patterns are consistent with those in Fig. 10. The vertical dashed line indicates the determined threshold (SINRpdr=-4.84 dB) in Fig. 11. The corresponding PDR and DCR are shown in Table II. When CR is 1600 m, not only DCR reaches 36.4%, but also PDR (less than 5%) meets the requirement of network coverage. Meanwhile, Figs. 12 and 13 show the obvious cooperative compensation gain of the entire network and edge users, respectively. The red dotted lines indicate network performance with cell dormancy and without cooperation.
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5000
4000
3000
2000
1000
0
Fig.8 JP cells in all clusters (CR=1600 m)
1
10 9
0.8
8 1800 1700 1600 1500 1400 1300 1200 Norm
CDF
0.6 0.5 0.4 0.3 0.2
7
Ratio(%)
0.7
3000
4000
5000
5 4
1800 1700 1600 1500 1400 1300 1200 Norm
2 1
0
5
0 -12 -11 -10
10 15 20 25 30 35 40 45
Users SINR(dB)
Fig.10 SINR CDF of the entire network
1
10 9
0.8
8
0.5 0.4
7
Ratio(%)
1600nonJP 1600JP Norm
0.6
-6
-5
-4
-3
-2
-1
2
0.1
1
10 15 20 25 30 35 40 45
Users SINR(dB)
Fig.12 JP gain
V. CONCLUSION To reduce energy consumption of the LTE-A
1600nonJP 1600JP Norm
4 3
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-7
SINR(dB)
6
0.2
5
-8
5
0.3
0
-9
Fig.11 Comparison of PDR
0.9
0.7
CDF
6
3
0.1
0 -15 -10 -5
2000
Fig.9 SINR map with cells JP on (CR=1600 m)
0.9
0 -15 -10 -5
1000
0 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1
Users SINR(dB)
Fig.13 Gain of edge user
system, CoMP-DESM is proposed to solve the inadequate coverage problem under the dormant cells. Results show that the energy sav-
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Table II Network performance and energy saving under different CRs CR(m)
1800
1700
1600
1500
1400
1300
1200
PDR
6.06%
5.51%
4.57%
2.66%
2.62%
2.08%
1.87%
DCR
41.8%
40.0%
36.4%
29.1%
21.8%
16.4%
14.5%
ing rate can reach 36.4%, while the proposed mechanism meets network coverage requirement. Thus, JP can be effectively applied in an energy saving mechanism and improve the network performance of edge users without increasing transmission power.
ACKNOWLEDGMENT This work was partially supported by the National Natural Science Foundation of China (61271187), the “863” Project (2014AA01A701), the Provincial Natural Science Foundation of China (20122BAB211039), the PAPD fund, and the CICAEET fund.
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[8] Kateˇrina Dufkov´a, Milan Bjelicaz and Byongkwon Moony, “Energy savings for cellular network with evaluation of impaction data traffic performance”, European Wireless Conference, Lucca, Italy, pp 916–923, April, 2010. [9] KyuhoSon, Hongseok Kim and Yung Yi, “Base station operation and user association mechanisms for energy-delay tradeoffs in green cellular networks”, Journal on Selected Areas in Communications, vol.29, no.8, Sept., 2011. [10] C. Peng, SB Lee, S. Lu, et al., “GreenBSN: enabling energy-proportional cellular base station networks”, Transactions on Mobile Computing, vol.13, no.11, pp 2537–2551, Nov., 2014. [11] S. Xie, Y. Wang, “Construction of tree Network with limited delivery latency in homogeneous wireless sensor networks”, Wireless Personal Communications, vol.78, no.1, pp 231–246, April, 2014. [12] Ziaul Hasan, Hamidreza Boostanimehr and Vijay K. Bhargava, “Green cellular networks: a survey some research issues and challenges”, Communications Surveys & Tutorials, vol.13, no.4, pp 524–540, Nov., 2011. [13] Tao Han and Nirwan Ansari, “On greening cellular networks via multicell cooperation”, Wireless Communications, vol.20, no.1, pp 82–89, Feb., 2013. [14] P. Guo, J. Wang, B. Li, et al., “A variable threshold-value authentication architecture for wireless mesh networks”, Journal of Internet Technology, vol.15, no.6, pp 929–936, Nov., 2014.
Biographies Fei Zheng, 1982, Ph.D. candidate of BUPT. His research interest is green access networks. Email:
[email protected]. Wenjing Li, the corresponding author, Email: wjli@ bupt.edu.cn. 1973, Professor of BUPT, M.S. advisor. Her research interest is wireless network management. Luoming Meng, 1955, Professor of BUPT. His research interest is network management. Email:
[email protected]. Peng Yu, 1986, Lecturer of BUPT. His research interest is green access networks. Email: yupeng@bupt. edu.cn. Lei Peng, 1983, Lecturer of JGSU. Her research interest is sensor networks. Email:
[email protected]. Min Lei, 1979, Lecturer of BUPT. His research interest is network information security. Email: leimin@bupt. edu.cn.
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