Dynamic Grouping and Frequency Reuse Scheme for Dense Small Cell Network

Transcription

1 GRD Journals Global Research and Development Journal for Engineering International Conference on Innovations in Engineering and Technology (ICIET) July 2016 e-issn: Dynamic Grouping and Frequency Reuse Scheme for Dense Small Cell Network 1 B.Sridevi 2 R.Shakanapriya 3 R.Jeyalakshmi 4 P.Saranya 1 Assistant Professor 2,3,4 Student 1,2,3,4 Department of Electronics and Communication Engineering 1,2,3,4 Velammal college of engineering and technology, Madurai Abstract Small cell networks have emerged as a key technology in residential, office building or hotspot deployments that can significantly fulfil high data demands in order to offload indoor traffic from outdoor macro cells. However, as one of the major challenges, inter-small cell interference gets worse in some building scenarios because of the presence of numerous interfering sources and then needs to be considered in early network planning phase. Though, penetration loss and complex indoor environment make that it is difficult for existing cellular network to sufficiently follow the demand in indoor mobile traffic. The deployment of small cells is envisioned to be a key solution for providing high wireless data-rates, offloading the macro cell traffic and enhancing the coverage of existing networks. Based on the edge nodes all interfering nodes in the small cells are grouped into number of groups, and then the frequency reuse factor is applied according to the dominance interference strength. Hence the power gets transmitted for different groups according to the interference strength. Simulation results shows, the proposed scheme yields great performance gain in terms of the efficiency relative to the universal frequency reuse. Keyword- In-building small cell networks; frequency reuse I. INTRODUCTION Recently, it has been reported that about 80% of wireless communication traffic comes from indoors[1]. The deployment of femtocells is envisioned to be a key solution for providing high wireless data-rates, offloading the macro cell traffic and enhancing the coverage of existing networking [2].Small cells can be densely deployed in a small area such as an office building, hotspot area, or residential area. Nevertheless, an unplanned or random user-installed deployment faces several problems and challenges in terms of interference management and backhaul constraints [3]. In co-channel deployments, all small cells reuse the spectrum resources, and interference from other small cells might significantly deteriorate the overall system performance by multiple dominant interferences, which are from small cells not only on the same floor due to adjacent deployment, but also on the upper and lower floors due to cross-floor signal penetration. Hence, interference modelling used in traditional cellular scenario is not proper in a building environment. Currently, several interference mitigation schemes in macro base station scenario have been widely studied mainly by means of interference randomization, interference control, interference suppress, and interference coordination. In the first class the interference is averaged across the whole spectrum via spreading sequences (e.g. scrambling and interleaving),and therefore is not actually cancelled out. The second class uses the power control and static beam forming to reduce the interference level. But by contrast, in the third class, the interference is successfully suppressed by using advanced signal processing techniques [4]. Although these techniques are becoming popular, however, the complexity at the receiver side and backhaul constraint are still the challenging issues particularly in the presences of multiple dominant interferences. Inter-Cell Interference Coordination (ICIC) techniques [5], on the other hand, present pragmatically a more feasible solution. Unlike the macro base station, small base stations can be installed by users in a random manner, making it difficult to handle the interference problem. The traditional methods can be applied to the mitigation of inter-small cell interference when femtocells are deployed in a systematic way with low density[6]. However, when multiple small cells are densely deployed in a building environment, the interference source will be greatly increased, and the interference scenario will drastically vary due to a large number of dominant random interfering nodes. In order to mitigate the inter-femtocell interference in the dense environment, several methods have been proposed, e.g. Fractional Frequency Reuse(FFR) [7-8], which uses the flexible Frequency Reuse Factors (FRF) in the celledge in the cell- edge area and cell-centre area. But in Ref. [7], dense inter-small cell interference is not specially considered. While in Ref. [8], a small cell can be granted admission into a group only when it interferes with all the small cells already admitted by that group. However, it should be noted that it has high computational complexity and the graph based method is very flexible. Therefore, different admission control criterions can be developed to tune the size of each group, and spectrum resource can be fully used for each group to improve the spectrum efficiency. In this paper, a new soft frequency reuse Scheme is proposed for interference management in dense small cell networks, which was partly presented in Ref. [9]. Based on the Reference Signal Received Power (RSRP) from the serving users, the multiple interfering small cells can be determined to form several groups, and then the minimum sub channels with different soft frequency reuse factors for these groups 397

2 are allocated to provide optimal performance near the cell edge. After the allocation of sub-channels to small cells in each group, the transmit power on the sub-channel part for the group is adaptively adjusted in term of the interference strength. By making The use of higher frequency reuse factor with the least number of orthogonal sub-channels according to the deployment and interference environment, the dense small cells can perform interference mitigation, and consequently improve the overall system performance. Simulation results show the SINR performance gain is more than 3 db and the average spectrum efficiency outperforms 10.12% comparing with the legacy soft FFR scheme. This paper is organized as follows. The system model is described in Section II. Section III describes the frequency reuse scheme by grouping. In Section IV, the proposed grouping and the Frequency distribution for groups based on RSSI algorithms are elaborated. Section V provides Simulation results of the proposed scheme. Finally, Section VI concludes and summarizes the paper. II. SYSTEM MODEL We consider an OFDM-based homogeneous cellular network as our baseline system. In this network, we assume that there are M macro base-stations (MBS) each equipped with three directional antennas, a user density of ῥu users per cell and T sub-channels, each of bandwidth b. We consider only the downlink traffic in this system and assume that the BSs transmit all the time in all the channels assigned to them. In Fig. 1, we provide a pictorial depiction of this system. We assume that a frequency reuse factor of 3 is used and T/3 sub-channels are exclusively assigned to each of the directional antennas. We quantify the performance of this network in terms of the geometric mean (GM) throughput of all the users. We are interested in measuring the impact of deploying low-power BSs with omnidirectional antennas on the performance of this system. Fig. 1: macro cell of the baseline system. Each sector is served by one of the directional antenna of the MBSs (represented by the large triangle). In a macro cell there are several potential locations for the deployment of the small cells. In the edge region of each small cell, the coverage is weak and/or the interference can be strong. Since, every point of intersection is shared by three hexagonal cells, it is easy to see that, for a large network and neglecting edge effects. A user association (UA) policy determines the BS to which a given user connects with. We consider the following two simple user association (UA) rules. They are Best SINR: Users associates with the BS that provides the maximum SINR. Small-cell First (SCF): Under this rule, a user associates with the small cell that gives the maximum SINR provided that this is greater than a given threshold. If no small cell provides SINR greater than β, it associates with the BS that provides the maximum SINR. III. FREQUENCY REUSE SCHEME BY GROUPING In this section, we will review several frequency reuse schemes [5] that are related to our work. A. Frequency reuse 1 (FR 1) Frequency reuse 1 is well known as universal frequency reuse scheme, where the entire frequency spectrum is reused over all cells, as shown in Figure 2. However, by employing this scheme only the cell centre users experience good channel quality whereas cell edge users suffer from low radio conditions due to severe inter-cell interference. 398

3 Fig. 2: The frequency reuse 1 scheme B. Hard frequency reuse 3 Figure 3 illustrates how the power-frequency resource restrictions are applied at each cell in frequency reuse 3 scheme. It is important to note that the frequency reuse factor is restricted by a list of integer numbers:{1, 3, 4,7,,i2 + i j + j2 i, j N }. Fig. 3: The hard frequency reuse 3 scheme C. Soft frequency reuse Soft frequency reuse scheme suggests that cell edge users are scheduled in the protected resources and cell centre users are scheduled in the shared resources with a lower transmit power, as shown in fig(4). Fig. 4: The soft frequency reuse scheme IV. PROPOSED GROUPING In the dense small cell networks, a user at the edge will be interfered by multiple dominant small cells, especially in the multi-floor office building, a user on the floor will be interfered by small cell frequency not only on the same floor but also on the upper and lower floor, and so on due to the cross-floor penetration, which is obviously different from the traditional macro cellular scenario. Based on the cell-edge regions of small cells. The cell-edge will receive the signal from various small cells, which can be classified into a number of groups to determine the frequency reuse factors and to set the priority order for the sub-channel allocation of each group. But for each cell-edge user, there have the different interfering small cells, so the same interfering small cells set is selected to do the grouping based on the number of users in the edges the small cells grouping is based on the interference graph modelling method. From the grouping results, this subsection introduces the selection of grouping-based frequency reuse factor. Figure 6 illustrates an example of the proposed scheme, where Small cells are classified into three groups. Instead of having only a group on top of the cell-edge region, it is possible to form up to multiple virtual groups each using different parts of spectrum and different frequency reuse factors. Following the concept, the group with the strongest interference strength has the lowest frequency reuse 399

4 factor, the group with the weakest interference strength has the highest frequency reuse factor, and the others are in the middle [6].Therefore, for Fig (5), the frequency reuse factor for group 1 is 1/3, the group 2 is 2/3, and the group 3 is 3/4. Accordingly, if there are more groups and more mobile users per group, different frequency reuse factors and frequency partition are used based on cyclic difference sets. Fig. 5: An example of proposed soft frequency reuse scheme It is expected that two nodes that have an edge between them select different sub-channel patterns and frequency reuse factor according to grouping criterion in order to improve the cell edge performance. The problem of finding out the smallest number of sub-channels in the in-building scenario can be formulated by calculating the number of mobile users. If there has an edge. The smallest number of users will be the solution of the problem. If there are not enough cell edge users to be scheduled at the reserved sub-channel, the sub-channels can be also used for cell-centre users to achieve high resource efficiency. In order to improve the performance of cell-edge, we adjust the transmit power on the sub-channels of the group based on the RSRP, which should be adaptively select the transmit power of sub-channel for cell-centre and cell edge. Based on the signal strength in each grouping the frequency can be reused [9]. The RSSI for the group can be calculated from, RSRQ = N * RSRP / RSSI V. SIMULATION RESULTS OF THE PROPOSED SCHEME The modelling platform is based on MATLAB (matrix laboratory), which is a multi-paradigm numerical computing environment and fourth generation programming language. It is a proprietary programming language developed by MathWorks, it allows matrix manipulations of plotting of functions and data implementation of algorithms, creation of user interfaces and interfacing with programs written in other languages. In this simulation model, we consider 3 cells in which the base stations provide different range of frequencies for each cells. The basic structure of cells is shown in fig (6). In these cells the mobile users are represented as nodes fig (7). Fig. 6: The basic cell structure 400

5 Fig. 7: Grouping of edge nodes In fig(8), we discussed about the grouping of nodes at the edges, which will suffer from interference and low signal strength. The nodes at the edges can access the three different frequencies of the cells. Hence frequency interference at the edges will increase. Therefore, the edge nodes are grouped for frequency reusing. Fig. 8: Cells with mobile nodes Frequency for the grouped nodes will be provided by the base station which has less number of nodes fig(9). Hence RSSI at the edge nodes will be increased and frequency will be mutually shared among all the nodes in the cells. Therefore different frequency reuse factors and transmit powers for these groups are adjusted adaptively to mitigate the mutual interference. Fig. 9: Frequency reuse by the nodes. VI. CONCLUSIONS Small cell networks, particularly dense small cell networks, are promising to provide extra capacity for high indoor data demands in enterprise environments. However, dense small cell networks will introduce excessive inter- cell interference in the high storey building scenarios. More irregular neighbour cell boundaries will be created due to cross-floor signal penetration and then efficient interference mitigation schemes are required. In this paper, a dynamic soft frequency reuse scheme was proposed for interference management in dense small cell networks. Based on RSSI, it classifies nodes into a number of groups, and then selects the frequency reuse factor and the transmit power for different groups according to the interference strength. The final simulation results show that the proposed scheme achieves not only an enhanced cell-edge spectral efficiency, but also minimal degradation of a forthcoming co-channel small cell deployment. 401

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