Abstract
A novel interference alignment (IA) scheme based on the errors-in-variables (EIV) mathematic model has been proposed to overcome the channel state information (CSI) estimation error for the MIMO interference channels. By solving an equivalently unconstrained optimization problem, the proposed IA scheme employing a weighted total least squares (WTLS) algorithm can obtain the solution to a constrained optimization problem for transmit precoding (TPC) matrices and minimizes the distortion caused by imperfect CSI according to the EIV model. It is shown that the design of TPC matrices can be realized through an efficient iterative algorithm. The convergence of the proposed scheme is presented as well. Simulation results show that the proposed IA scheme is robust over MIMO interference channels with imperfect CSI, which yields significantly better sum rate performance than the existing IA schemes such as distributed iterative IA, maximum signal-to-interference-plus-noise ratio (Max SINR), and minimum mean square error (MMSE) schemes.
Highlights
The importance of the interference management has been emphasized in [1, 2]
Simulations are conducted to evaluate the performance of our weighted total least squares (WTLS)-based Interference alignment (IA) scheme presented in Section 3 for multiuser multiple input multiple output (MIMO) interference system with imperfect channel state information (CSI)
The CSI errors are assumed to be i.i.d. zero-mean complex Gaussian, and Gaussian input is considered. size of data matrix D is fixed in Figures 2 to 5; that is, the value of n in (38) is fixed, while the WTLS estimation convergence tolerance δ0 = 10−5
Summary
The importance of the interference management has been emphasized in [1, 2]. It is of paramount importance to conceive efficient interference management schemes for multiuser wireless networks. Interference alignment (IA) has been proposed as a powerful and promising technique to mitigate the interference in multiuser interference channels [1,2,3]. In [1], Jafar and Shamai first characterized the degrees of freedom (DoF) for two-user multiple input multiple output (MIMO) interference channels. The earlier studies [1,2,3] of his group were focused on the DoF for various distributed systems. IA technique has been used to structure interfering signals to occupy a reduced-dimensional interference subspace at the receivers and maximize the multiplexing gain (or the sum rate of system)
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