# Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
## 1 Introduction  
Large Scale Multiple-Input Multiple-Output (LS-MIMO) systems have received great attention due to the possibility of increasing both spectral efficiency (SE) and energy efficiency (EE). They are already considered to be incorporated into the 3 rd generation partnership project (3 GPP) standard body, and are referred to as Full-Dimension MIMO (FD-MIMO) systems [1; 2; 3; 4; 5]. However, most of the performance analysis for LS-MIMO systems disregard its high implementation complexity and the overhead required for reference signals (RSs). The RS proportionately increases as the number of transmitter (TX) antennas and/or user equipment (UEs) increases. Therefore, solving this problem is required to achieve the expected performance gain introduced in the literature.  
The RS overhead reduction is a traditional problem in wireless communication systems. Blind channel estimation technologies using the specific statistical property of the signal and the channel, and the correlation based channel recovery were introduced in [6; 7]. This kind of technology can have a very high impact, but it usually requires long observation time and high complexity. Training based super imposed RS on data signals, which locates RS stream and data stream in the same resources, were also introduced in [8; 9]. This can give very efficient results for reducing RS overhead because data can be transmitted in all time and frequency resources. However, the performance loss due to data and RS imposition is inevitable.  
Generally, the reduction of the RS overhead is considered just for SE, but it becomes a problem for realizing the products in LS-MIMO systems. That is, LS-MIMO would never be realized without the reduction of the RS overhead. For example, if the number of TX antennas is increased up to several dozen, the required RS resource would be more than the available physical resource. Even 3 GPP LTE-A Release 10 uses only up to eight antennas, but the RS overhead is more than 30%.  
In this paper, the RS overhead problem is analyzed. We then propose an RS overhead reduction scheme, called beam grouping resource reuse (BGRR), and evaluate its performance for various scenarios. The main idea of BGRR scheme is that it makes dynamic virtual sectors by using a beam group in a given cell and reuse the RS resources for different virtual sectors. The high level concept of proposed methodology is given in Figure 1.  
For example, we can divide a cell into two different zones using beamforming based on a large scale antenna system, i.e., zone 'A' and zone 'B', and regard the two different zones as different sectors (Figure 1). The RS resources used for zone 'A' also can be used for zone 'B'. Then, RS overhead can be reduced by a half compared to without BGRR. Reducing the inter-virtual-sector interference (IVSI) is essential. How to divide the given cell is also a question.  
In what follows, the system model for the numerical analysis is shown in Section 2. In Section 3, we present the analysis of the RS overhead. In Section 4, we propose a BGRR scheme, and provide relevant analysis. In Section 5, we present numerical results of the proposed scheme. Section 6 is the concluding remarks.  
_Notation_: In the following, boldface lower-case and upper-case characters denote vectors and matrices, respectively. The operators \((\cdot)^{H}\) and \(E[\cdot]\) denote conjugate transpose and expectation, respectively. The \(N\times N\) identity matrix is denoted \(\mathbf{I}_{N}\), \(log(\cdot)\) is the common logarithm and \(\mathbf{X}\sim CN(0,\ \mathbf{V}_{N})\) is the complex Gaussian distributed vector with mean zero and covariance \(\mathbf{V}_{N}\).