Research on Load Balancing in C-RAN with Femtocells

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Introduction
To meet the growing capacity demands in wireless communication networks, Cloud Radio Access Network (C-RAN) has been proposed for the purpose of satisfying the capacity requirements and scalability problems [1][2].C-RAN is composed of a BaseBand Unit (BBU) and a remote radio unit (RRU), and it also has better flexibility because it breaks the fixed connection relationship between BBU and RRU [3].Moreover, some notable features are included in C-RAN, e.g., centralized processing, collaborative radio, real-time cloud computing infrastructure, and clean system [4].
Since the distribution and service requirement of the users are stochastic in C-RAN wireless access network [5][6], therefore, the load balancing techonology is inevitable in the case of solving the problem of unbalanced cell and improving the resource utilization.However, the traditional load balancing schemes only allow the users of overloaded macrocell to handover to the neighboring macrocells.Consequently, there will be no suitable neighboring cells to support the users' handover when the neighboring cells are also overload, and it also cannot alleviate the unbalanced problem [9][10][11].
With the increases of the data usage of wireless communication system occurs in indoor, the research of indoor service are becoming more and more important [12].As the femtocell has the advantages of small coverage, low cost and better signal quality, therefore, it has been selected as the primary canditate technology to resolve the problem of indoor coverage in the future 5G wireless communiction.The Femtocell Access Point (FAP) of femtocell and existing networks as a backhaul connectivity can meet the upcoming demand of high data rate and extend the coverage area [13][14][15].Moreover, the coexistence of macro cell and femtocell can also allow the users of overloaded macrocell to handover to the femtocell.Therefore, a joint optimization scheme of load balancing in C-RAN two tier networks is proposed The remainder of this paper is organized as follows: In Section 2, we deccribe the system model.Section 3 analyzes the detailed procedure of the proposed Joint Optimization Load Blancing Based on The Coexistence of Femtocells (JOLB) mechnism.The simulation results are discussed in Section 4. We conclude the paper in Section 5.

System Model
In the C-RAN radio access network, when the cell is overload, the network tries to move the mobile users of the overloaded cell to the appropriate femtocell to achieve the load ISSN: 1693-6930  balancing while make reasonable use of femtocell resources.The system model is as shown in Figure 1.

Figure 1. System model
As we can see form Figure 1, there are three macrocells RRU1, RRU2 and RRU3 in the C-RAN wireless access network.RRU1 is overloaded, RRU2 and RRU3 are the two neighboring macrocells with different load levels.APi-j denotes the i-th femtocell in the j-th macrocell.If the RRU2 and RRU3 are all under the situation of heavy overload, the users of RRU1 are not allowed to handover to the RRU2 and RRU3, and then the users of RRU1 will be handovered to the femtocell.For example, the users of RRU1 will be handovered to the femtocells such as AP1-1, AP1-2 and AP1-3 for the purpose of reaching the load balancing.

Joint Optimization Load Blancing Based on The Coexistence of Femtocells(JOLB)
The detaied procedure of JOLB algorithm is as follows: Step1: Compare the cell load with the predefined overload threshold.If the load value is greater than the threshold value, it will be treated as the overloaded cell.Then, to identify the neighboring cells whose load is smaller than the threshold, and generate a target cell list cell_list.
Step2: Analyze the load state of neighbor cells in the cell_list, and then arrange the cell_list in ascending order according to the load state.
Step3: Get the ue_list in descending order based on the signal strength of users .
Step4: If the cell_list is not empty, handover the first user of the ue_list to the first cell of the cell_list, and update the ue_list and cell_list based on the signal strength of users and load state of cells, separately.If the cell_list is empty, handover the first user of the ue_list to the adjacent femtocell, and update the ue_list based on the signal strength of users .
Step5: Check if the cell is still overload.If the cell is overload, repeat Step2-4, otherwise go to Step6; Step6: Terminate the load balancing and then wait for the next load balancing period.

Simulation Results and Analysis
In this section, we describe the simulation environment and results of the performance of the proposed scheme.We assume that 37 RRUs are located in the network, and the number of cells is 37. Morever, each RRU covers one cell, and the users are randomly distributed in each cell.The system model is as shown in Figure 1, which indicates the distribution of the users and base stations, and the simulation parameters are listed in Table 1.
The simulation results mainly focus on the resources utilization, the number of the unsatisfied user, blocking rate and call dropping rate of the JOLB algorithm.The compared algorithms include No Load Balancing (NLB), Load Balancing only with macrocells (LB), and Joint Otimization Load Blancing based on the coexistence of femtocell (JOLB).In Figure 2, we can see that with the increases of the user arrival rate, the average resources utilization of JOLB is larger than the NLB and LB schemes.This is because when the neighboring macrocells are in heavy loads, the LB can only transfer a small amount of users to the macrocells until it is in overload.However, the JOLB can shift the user of the overloaded cell to femtocell when the neighboring macrocells are in overload.Therefore, the JOLB scheme can improve the resource utilization of both macrocells and femtocells Figure 2. Average resource utilization rate In Figure 3, we can see that with the increases of the user arrival rate, the number of unsatisfied user of JOLB is smaller than the NLB and LB schemes.This is because when the neighboring cells are in overload, the LB and NLB schemes cannot transfer the users to the neighboring cells, whereas the JOLB scheme can transfer the users of the overloaded cell to femtocell, which ensures the user's quality of service (QoS).

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Figure 4 is the blocking rate of the network by applying different mechanisms.From the Figure 4, we can see that with the increase of the user arrival rate, the JOLB scheme has the minimum blocking rate, which indicates that the JOLB can decrease the blocking rate of users to meet the access requirement of users.In Figure 5, we can see that with the increase of the user arrival rate, the network call drop rate also increases.Under the same arrival rate, the call dropping rate of the JOLB is the least while that of NLB is the biggest.Furthermore, when the user arrival rate is 1, the call drop rate of JOLB is zero, which indicates that the handover requirements of all users are satisfied.When the arrival rate is 3, the call drop rate of JOLB drops by 15%, which means that the JOLB improves the handover success ratio.

Conclusion
In the C-RAN wireless network, a joint optimization load balancing algorithm based on the coexistence of femtocells is proposed for the purpose of providing better user service when all the neighboring macrocells are overloaded.Our proposed algorothm can meet the increasing service demands and huge data exchange by introducing the C-RAN wireless access network.Moreover, the load balancing problem is inevitable in C-RAN due to the randomness of the user's location and service request.As the traditional load balancing mechanisms of C-RAN cannot work well when all the neighboring macrocells are overloaded.Therefore, in order to resolve that kind of handover problem, we take the femtocell as the target handover cell to allow the users of overloaded macrocell to handover to the femtocell.Simulation results show that the proposed mechanism compared with the LB and NLB schemes can reduce the number of unsatisfied users, lower the blocking rate and dropped call rate while improve the average network resource utilization.

Figure 3 .
Figure 3. Number of unsatisfied users