 Research
 Open Access
Cochannel interference suppression for multicell MIMO heterogeneous network
 Yu Li^{1} and
 Zufan Zhang^{1, 2}Email author
https://doi.org/10.1186/s1363401603208
© Li and Zhang. 2016
 Received: 13 May 2015
 Accepted: 9 February 2016
 Published: 24 February 2016
Abstract
The heterogeneous network, contains a macro cell and a grid of low power nodes with the same frequencies, can improve the system capacity and spectrum efficiency. Configuring lowpower nodes that share the same spectrum with macro cell to form heterogeneous networks makes it more likely to improve the system capacity and spectrum efficiency, but inevitably, strong cochannel interference is the main barrier to further improvement for heterogeneous networks. This paper proposes an algorithm which combines the triangular decomposition and signal to leakage and noise ratio (SLNR) (TDSLNR) to suppress strong cochannel interference in multicell multiple input and multiple output (MIMO) heterogeneous networks. Firstly, the proposed algorithm can reduce the number of intercell interferences in half. As a result of triangular decomposition, an equivalent interference channel model is extracted to eliminate the rest of interferences using SLNR and interference suppression matrix. Theoretical analysis shows that the proposed algorithm provides a potential solution to suppress the cochannel interference with low complexity and reduce the computation complexity without adding extra interference suppression matrices and computation complexity at receivers. Furthermore, the simulation results show that TDSLNR algorithm can improve system capacity and energy efficiency comparing with the traditional SLNR algorithm.
Keywords
 Multicell MIMO heterogeneous network
 Cochannel interference
 Signal leakage noise ratio
 Triangular decomposition
1 Introduction
In the new generation of broadband mobile communication systems, multiple input and multiple output (MIMO) technique [1, 2] and heterogeneous networks [3–5] are key methods to enable the fast and reliable wireless communication access. On one hand, MIMO provides increased channel capacity and data rate using multiple antennas on both base stations and terminals. On the other hand, heterogeneous networks use hybrid networks with lower power nodes (pico, femto cells) and main nodes (macro cells) to achieve highfrequency efficiency and system capacity. The topology of a typical heterogeneous network consists of one macro cell and several pico/femto cells sharing the same frequency within the one area. Such networks bring cochannel interferences (CCI) which need to be eliminated in wireless communications [6–8]. For instance, users of the pico/femto cells are affected by the strong power signal from the macro cell, whereas high power terminals at the edge of macro and pico/femto cells produce strong interferences to the surrounding pico/femto cells. It is worthwhile to investigate methods to reduce or eliminate the CCI especially after MIMO be introduced to the heterogeneous networks.
At present, interference coordination is the key technology of interference suppression for heterogeneous network. The intercell interference in traditional networks can be significantly reduced by adjusting the spectrum allocation and transmission power among cells [9–11]. The intercell interference coordination requires a central control node to achieve parameter sending and so on. However, in heterogeneous network, a large of low power nodes are randomly allocated according to the demands of users; thus, the low power nodes cannot use the X2 interface [12], which will cause strong uplink and downlink interference to adjacent cells, such as the downlink dead zone and uplink blocking [8, 13]. Therefore, the novel interference coordination schemes designed for heterogeneous network are proposed [14–16]. For example, in [10], a selfadaptive and flexible algorithm about spectrum utilization is proposed; this algorithm uses flexible spectrum utilization algorithm in cells formed by low power nodes while adopts fixed multiplexing schemes to manage interference in the macro cell. Meanwhile, there are many studies about interference suppression using various power control schemes for heterogeneous network [17, 18]. However, the interference coordination scheme based on the spectrum allocation cannot fully exploit the spectrum, and the interference coordination scheme based on the power control will result in higher computation complexity and a large number of signaling interactions. Furthermore, the application of interference coordination in actual deployment is usually restrained by many factors such as implementation complexity, central management, and distribution control; thus, we will focus on using the intercell interference elimination to suppress the interference in the heterogeneous network.
Currently, the interference elimination mainly includes two aspects, namely precoding design at transmitting end and the signal detection at receiving end, respectively. As for signal detection, it is usually limited by the achievement of channel state information, and it has highcomplexity iterative detection process [19, 20]. As for the precoding design, CCI suppression can employ the block diagonalization precoding algorithm [21–24]. However, initially, this algorithm requires the number of transmitting antennas should be greater than the total number of all users’ receiving antennas, which is difficult to satisfy. At present, some regularized versions of BDtype algorithms can deal with the limitation of the number of transmitting antennas [25, 26], but they are not referred to the application in multicell environment. Furthermore, BD algorithm will not take the problems of noise amplification into consideration; thus, it is unrealistic to put it into practice. Therefore, the signal to leakage and noise ratio (SLNR) algorithm proposed by Sadek is chosen to be the criterion of precoding design [27]. This algorithm maximizes the ratio of signal over the sum of leakage and noise, decomposes the multiuser MIMO system into multiple collateral and independent singleuser MIMO systems, so that it is not restricted by the number of antennas, and gets the precoding matrices of each user independently, so the better interference suppression performance and extensive application scenarios can be obtained [28–31]. Moreover, in order to reduce the intercell cochannel interference, the interference alignment technology is adopted in multicell MIMO network [32, 33]. Generally, the interference alignment technology designs the interference suppression matrices at receivers and aligns the interference signal to the corresponding zero zones of interference suppression matrices to suppress the intercell cochannel interference. Though the interference alignment technology is helpful to suppress the intercell and intracell interference as well as enhance the system capacity, it will undoubtedly increase the number of interference suppression matrices at receivers and its corresponding computation complexity.

This paper firstly discusses the interference situation of the network and then proposes an interference suppression algorithm combining triangular decomposition and SLNR under the considered network. This algorithm can reduce the number of intercell interferences in half through using triangular decomposition to the joint channel matrix, before interference suppression process at receiver.

Based on the equivalent interference model, combining with the SLNR algorithm, this paper computes the precoding matrices of each user in each cell and the corresponding closedform interference suppression matrices in detail according to the different interference situations of each cell.

Considering the computation complexity, we compare the proposed algorithm with the traditional SLNR as well as the algorithm adopting interference alignment technology.

We verify that comparing the proposed algorithm with traditional SLNR, a great improvement of system capacity and energy efficiency can be achieved. Furthermore, the impact of different number of data streams and antennas on the system performance is further analyzed.
The remainder of this paper is organized as follows. Section 2 mainly discusses the interference situations in multicell MIMO heterogeneous network. Section 3 makes a detailed introduction about the proposed algorithm which combines the triangular decomposition and SLNR. Furthermore, the computation complexity of the proposed algorithm with the traditional SLNR as well as the algorithm adopting interference alignment was compared. The simulation results will be discussed in Section 5. Finally, conclusions are presented in Section 6.
2 The interference situation discussion for multicell MIMO heterogeneous network
As shown in Fig. 1, there are two kinds of interferences in the model, including interference from macro BS to PUS and interference from pico BS to MUS. Due to the different path loss from pico BS to MUS, the received interference strength of different MUS is not the same. It is obvious that MUS1 may receive the strongest interference, MUS2 followed but also relatively strong, and MUS3 may receive the weakest interference strength. Furthermore, as the path loss from macro BS to MUS2 and MUS3 is much more than that to MUS1 and if we want to make the received signal to interference plus noise ratio (SINR) of each user the same, it is needed to increase the transmitting power of macro BS, which will result in strong interference from macro BS to PUS.
In conclusion, the interference situations in multicell MIMO heterogeneous network are absolutely different with that in traditional macro cell. According to the above discussion, we know that it is not suitable for the interference suppression of multicell MIMO heterogeneous network through judging the interference strength and designing the corresponding interference coordination scheme just according to the users’ locations. Thus, this paper proposes an interference elimination algorithm for edge users, which can fully eliminate the interference according to the various interference situations on terminals.
3 Cochannel interference suppression
where, x _{ k } = [(x _{1,k })^{ T }, (x _{2,k })^{ T }]^{ T } indicates the 2d × 1 data streams sent by the BS in kth cell; x _{1,k }, x _{2,k } are the d × 1 data streams sent by the BS in kth cell to users 1 and 2, respectively, satisfying the power constraint \( E\left[{\mathbf{x}}_k^H{\mathbf{x}}_k\right]=P(k);\kern0.5em {\mathbf{H}}_b^{i,k} \) which represents the N _{ r } × N _{ t } channel matrix between the BS in the bth cell and the ith user in the kth cell; u _{ i,k } is the intracell interference suppression matrix of the ith user in the kth cell, and (u _{ i,k })^{ H } u _{ i,k } = I _{ d }; w _{ k } = [w _{1,k }, w _{2,k }] is the N _{ t } × 2d dimension precoding matrix; w _{1,k } and w _{2,k } are the N _{ t } × d dimension precoding matrices of users 1 and 2 in the kth cell, respectively; n _{ i,k } is the N _{ r } × 1 dimension additive white Gaussian noise with zeromean, unit variance, and \( E\left[{\mathbf{n}}_{i,k}{\left({\mathbf{n}}_{i,k}\right)}^H\right]={\mathbf{I}}_{N_r}. \)
3.1 Triangular decomposition of the equivalent channel
From the point of joint channel matrix of all cells, this paper exploits triangular decomposition to the joint channel matrix. It can be seen from the discussion about the downlink interference in Section 2 that different base stations have different transmission power, which results in different degree of interference on users, and the base stations with higher transmission power have stronger cochannel interference to edge users of other cells. Thus, due to the different extent of interference on different users, it is needed to rank the receiving signal powers of users in different cells then obtain the suitable joint channel matrix by adjusting the row vectors of joint channel matrix.
According to the ranking result, corresponding cells are named as cell 1, cell 2,…, cell K.
where, \( \mathbf{X}=\left[\begin{array}{c}\hfill {\mathbf{w}}_1{\mathbf{x}}_1\hfill \\ {}\hfill {\mathbf{w}}_2{\mathbf{x}}_2\hfill \\ {}\hfill \vdots \hfill \\ {}\hfill {\mathbf{w}}_{\mathrm{K}}{\mathbf{x}}_{\mathrm{K}}\hfill \end{array}\right],{\mathbf{y}}_k=\left[\begin{array}{c}\hfill {\mathbf{y}}_{1,k}\hfill \\ {}\hfill {\mathbf{y}}_{2,k}\hfill \end{array}\right],{\mathbf{n}}_k=\left[\begin{array}{c}\hfill {\mathbf{n}}_{1,k}\hfill \\ {}\hfill {\mathbf{n}}_{2,k}\hfill \end{array}\right],k=1,2,\dots,\ K. \)
where, \( \mathbf{H}=\left[\begin{array}{cccc}\hfill {\mathbf{H}}_{11}\hfill & \hfill {\mathbf{H}}_{21}\hfill & \hfill \cdots \hfill & \hfill {\mathbf{H}}_{\mathrm{K}1}\hfill \\ {}\hfill {\mathbf{H}}_{12}\hfill & \hfill {\mathbf{H}}_{22}\hfill & \hfill \cdots \hfill & \hfill {\mathbf{H}}_{\mathrm{K}2}\hfill \\ {}\hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \cdots \hfill & \hfill \vdots \hfill \\ {}\hfill {\mathbf{H}}_{1\mathrm{K}}\hfill & \hfill {\mathbf{H}}_{2\mathrm{K}}\hfill & \hfill \cdots \hfill & \hfill {\mathbf{H}}_{\mathrm{K}\mathrm{K}}\hfill \end{array}\right] \) is the joint channel matrix of the whole system and its dimensions is 2KN _{ r } × KN _{ t }.
where, \( {\mathbf{R}}_{bj}{\mathbf{V}}_b{\mathbf{x}}_b=\left[\begin{array}{c}\hfill {\mathbf{R}}_b^{1,j}\hfill \\ {}\hfill {\mathbf{R}}_b^{2,j}\hfill \end{array}\right]{\mathbf{V}}_b{\mathbf{x}}_b=\left[\begin{array}{c}\hfill {\mathbf{R}}_b^{1,j}{\mathbf{V}}_b{\mathbf{x}}_b\hfill \\ {}\hfill {\mathbf{R}}_b^{2,j}{\mathbf{V}}_b{\mathbf{x}}_b\hfill \end{array}\right], \) b, j = 1, 2, …, K. When b = j, R _{ bj } is the intracell equivalent channel matrix; When b ≠ j, R _{ bj } is the intercell equivalent channel matrix. \( {\mathbf{R}}_b^{1,j} \) and \( {\mathbf{R}}_b^{2,j} \), whose dimension is N _{ r } × N _{ t }, represents the equivalent channel matrices between base stations of bth cell and user 1, user 2 in the jth cell, respectively. V _{1,k } and V _{2,k } represent the equivalent precoding matrices of user 1 and user 2 in the kth cell, and the dimension is N _{ t } × d, V _{ k } = [V _{1,k }, V _{2,k }], is N _{ t } × 2d dimension matrix, k = 1,2,…, K.
As can be known from (11), after using the triangular decomposition for joint channel matrix, the users of cell 1 in equivalent model do not receive the interference from other cells (intercell interference) but only receive the intracell interference. As can be known from (12), the users of cell 2 only receive the interference from cell 1 and intracell interference. As can be known from (13), the users in the kth cell receive the interference from cells 1, 2,…, k1 and the intracell interference. Therefore, the number of intercell interferences in half is reduced through using triangular decomposition for joint channel matrix, without other complex interference suppressing operation, which reduces the computation complexity at receiver.
In conclusion, the algorithm reduce the number of intercell interferences in half using triangular decomposition, then the rest of interference is eliminated by using SLNR to design precoding matrix, so that the receiver does not need to design individual interference suppression matrix to eliminate intercell interference, which reduces the number of interference suppression matrices and simplifies the computation complexity at receiver. Besides, as can be known from (9) and matrix theory that dimension matching matrices satisfy span(w _{ k } x _{ k }) = span(V _{ k } x _{ k }), that is to say, they can be spanned to the same space, which indicates that the triangular decomposition will not impact the degree of freedom, so the obtaining for equivalent model will not change the degree of freedom either. There is a need to explain that the proposed algorithm is implemented at base stations; meanwhile, it is assumed that all stations in this system can get channel matrix information through central control or circular polling mechanism, etc.
3.2 Interference suppressing algorithm combining triangular decomposition and SLNR
According to the above analysis, it can be found that the number of intercell interferences in half can be eliminated by employing triangular decomposition. In order to further decrease the number of the matrices at the receivers and the corresponding computation complexity, simplify the receivers processing; SLNR algorithm is employed to further suppress the intercell interferences. Next, for the convenience of analysis, we will take three cells as example to analyze the process of suppressing the interference for each cell in detail.
3.2.1 Interference suppression of cell 1
3.2.2 Interference suppression of cell 2
3.2.3 Interference suppression of cell 3
Obviously, the receivers in cell 3 do not need intracell interference suppression matrix to eliminate the intracell interferences. Finally, the final precoding matrix w _{ i,k } can be obtained through each equivalent precoding matrix V _{ i,k }.
4 Algorithm complexity analysis
4.1 Summarize the algorithm
The proposed algorithm that combines the triangular decomposition and SLNR can be implemented by three steps:
Step1. Employ the triangular decomposition for joint channel matrix H and then extract the equivalent interference channel model;
Step2. According to the equivalent interference channel model, exploit SLNR to compute the equivalent precoding matrices V _{1,1}, V _{2,1}, V _{1,2}, and V _{2,2} to suppress the rest of intercell interference and then obtain w _{1,1}, w _{2,1}, w _{1,2} and w _{2,2};
Step3. Compute the intracell interference suppression matrices of users u _{1,1}, u _{2,1}, u _{1,2} and u _{2,2}, respectively. Specially, the users in cell 3 use the equivalent precoding matrices V _{1,3} and V _{2,3} to eliminate the intracell interference, so the receiver does not need to add intracell interference suppression matrix any more.
4.2 Complexity analysis
Furthermore, the complexity of the triangular decomposition and SLNR algorithm is analyzed.
A and B are matrices whose dimensions are m × n, n × k, respectively; the complexity of operation A × B is ο(mnk). Thus, the complexity of operation \( {\left({\mathbf{H}}_b^{i,k}\right)}^H{\mathbf{H}}_b^{i,k} \) is ο(N _{ t } ^{2} N _{ r }), where, \( {\mathbf{H}}_b^{i,k} \) is the channel matrix whose dimension is N _{ r } × N _{ t }, and the complexity of eigenvalues operation for N _{ t } × N _{ t }dimension matrix C is ο(N _{ t } ^{3}). Therefore, the complexity of the algorithm that combines the triangular decomposition and SLNR can be concluded as follows:
Step1. Employ the triangular decomposition for H, the according complexity is \( o\left(2\times {6}^2{N}_r^2\left(3{N}_t6{N}_r/3\right)\right), \) namely, \( o\left(3{N}_r^2{N}_t2{N}_r^3\right); \)
Step2. Exploit SLNR algorithm to calculate the equivalent precoding matrices V _{1,1}, V _{2,1}, V _{1,2}, and V _{2,2} to suppress the rest of intercell interference, the according complexity is \( o\left({N}_t^3\right)+o\left({N}_t^2{N}_r\right); \)
Step3. Compute the intracell interference suppression matrices of users u _{1,1}, u _{2,1}, u _{1,2} and u _{2,2}, respectively. Specially, the users in cell 3 use the equivalent precoding matrices V _{1,3} and V _{2,3} to eliminate the intracell interference; the total complexity is \( o\left({N}_r^3\right)+o\left({N}_t^3\right)+o\left(d{N}_r^2\right)+o\left({N}_t^2{N}_r\right)+o\left(d{N}_r{N}_t\right). \)
Therefore, according to above analysis, the total complexity of the proposed algorithm is \( o\left({N}_r^3\right)+o\left({N}_t^3\right)+o\left({N}_r^2{N}_t\right)+o\left({N}_t^2{N}_r\right)+o\left(d{N}_r^2\right)+o\left(d{N}_r{N}_t\right). \)
However, exploit the interference alignment algorithm to suppress the rest of intercell interference, corresponding complexity is \( o\left({N}_r^2{N}_t\right)+o\left({N}_r^3\right)+o\left({d}^3{N}_r{N}_t^2\right)+o\left({d}^3{N}_t^3\right) \) [35]. Meanwhile, the complexity of the traditional SLNR without using triangular decomposition to the multicell MIMO interference system is \( o\left({N}_r^3\right)+o\left({N}_t^3\right)+o\left(d{N}_r^2\right)+o\left(d{N}_r{N}_t\right)+o\left({N}_t^2{N}_r\right). \) It obviously seen that under the condition with certain configuration of antennas, the complexity of the proposed algorithm and the traditional SLNR are on the same order of magnitude, which means that the proposed algorithm without increasing complexity to the system compared with traditional SLNR algorithm. Furthermore, comparing with employing the interference alignment algorithm to compute the intercell interference suppression matrix at receiver [35], the proposed algorithm reduces the number of filters (matrices) at receiver, which is helpful to avoid more complex interference suppression process and have lower computation complexity as well as reduce costs of receiver.
5 Numerical results
In the downlink communication environment with cochannel interference of multicell MIMO heterogeneous network, the performance of the whole system will be affected by the antenna configurations both of transmitter and receiver; it will also be affected by the data streams sent by the transmitter. At present, the system capacity is usually used as one of the reference indexes to evaluate the system performance, and the energy consumption is a noteworthy problem as well. Thus, the system capacity and energy efficiency are chosen to be the indexes to evaluate the system performance. The system capacity is defined as C = log(1 + SINR_{ k }) (bps/Hz), and the energy efficiency is defined as the number of bits sent by in unit of energy and unit of bandwidth, namely, \( \eta =\frac{ \log \left(1+{\mathrm{SINR}}_k\right)}{P_k} \) (bit/Hz/J), SINR_{ k } represents the signal to interference plus noise ratio of kth user and P _{ k } represents the transmission power of kth user. Aiming at network environment is formed by three cells sharing the same frequency resource, in which each cell has two edge users. Assuming that all channels are flat Rayleigh fading channels with the elements that are independent identically distributed Gaussian random variables whose mean is 0, variance is 1.
It can be figured out that the traditional SLNR algorithm only reduce the intercell interference at receiver, the strong interference may weaken the interference suppression ability of SLNR, which causes the degradation of system performance. However, the proposed algorithm can reduce the number of intercell interferences in half before the interference suppression operation at receiver, so the interference environment at receiver is improved, and the application effect of the SLNR algorithm is enhanced; finally, the overall system performance is improved.
It should be noted that the analysis and studies of this paper are based on the ideal channel estimation and the actual method for obtaining channel state information is not further studied. Of course the acquisition of realtime channel state information under the actual channel condition can be studied in following. Furthermore, the performance of the proposed algorithm with realtime channel states information will be analyzed.
6 Conclusions
This paper firstly discusses the interference situations in multicell MIMO heterogeneous network. Aiming at the strong cochannel interference in multicell MIMO heterogeneous network, an algorithm that combines the triangular decomposition and SLNR has been proposed. The algorithm can reduce the number of intercell interferences in half through exploiting the triangular decomposition for equivalent channel matrix before the complex interference suppressing operation at receiver. Then based on the equivalent interference channel model extracted after triangular decomposition, the precoding matrices of each user in each cell and the corresponding closedform interference suppression matrices are derived according to different interference situations in each cell. Furthermore, we compare the computation complexity of the proposed algorithm with traditional SLNR and interference alignment algorithm. Finally, the simulation results verify that the proposed algorithm can greatly improve the system capacity and energy efficiency compared with traditional SLNR algorithm. Meanwhile, the impact of different numbers of data streams and antennas on system performance is further analyzed.
Declarations
Acknowledgements
This work was supported in part by the National High Technology Research and Development Program of China (863 Program) under Grant No. 2014AA01A705, The National Natural Science Foundation of China under Grant No. 61440062, and the Program for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT1299.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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