Robust Minimum Mean Square Error Research Articles

Robust MMSE Precoding and Power Allocation for Cell-Free Massive MIMO Systems

We consider the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with single-antenna access points (APs) and single-antenna users. An iterative robust minimum mean-square error (RMMSE) precoder based on generalized loading is developed to mitigate interference in the presence of imperfect channel state information (CSI). An achievable rate analysis is carried out and optimal and uniform power allocation schemes are developed based on the signal-to-interference-plus-noise ratio. An analysis of the computational costs of the proposed RMMSE and existing schemes is also presented. Numerical results show the improvement provided by the proposed RMMSE precoder against linear minimum mean-square error, zero-forcing and conjugate beamforming precoders in the presence of imperfect CSI.

Robust MMSE precoding for massive MIMO transmission with hardware mismatch

Due to hardware mismatch, the channel reciprocity of time-division duplex massive multiple-input multiple-output system is impaired.Under this condition, there exist several different approaches for base station (BS) to obtain downlink (DL) channel information based on the minimum mean-square-error (MMSE) estimation method.In this paper, we show that with the hardware mismatch parameters BS can obtain the same DL channel information via these different approaches.As the obtained DL channel information is usually imperfect,we propose a precoding technique based on the criterion that minimizes the mean-square-error (MSE) of signal detection at the user terminals (UTs).The proposed precoding is robust to the channel estimation error and significantly improves the system performance compared to the conventional regularized zero-forcing precoding.Furthermore, we derive an asymptotic approximation of the ergodic sum rate for the proposed precoding using the large dimensional random matrix theory,which is tight as the number of antennas both at the BS and UT approach infinity with a fixed non-zero and finite ratio.This approximation can provide a reliable sum rate prediction at a much lower computation cost than Monte Carlo simulations.Simulation results show that the approximation is accurate even for a realistic system dimension.

Channel Estimation for Massive MIMO-OFDM Systems by Tracking the Joint Angle-Delay Subspace

In this paper, we propose joint angle-delay subspace based channel estimation in single cell for broadband massive multiple-input and multiple-output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) modulation. Based on a parametric channel model, we present a new concept of the joint angle-delay subspace which can be tracked by the low-complexity low-rank adaptive filtering (LORAF) algorithm. Then, we investigate an interference-free transmission condition that the joint angle-delay subspaces of the users reusing the same pilots are non-overlapping. Since the channel statistics are usually unknown, we develop a robust minimum mean square error (MMSE) estimator under the worst precondition of pilot decontamination, considering that the joint angle-delay subspaces of the interfering users fully overlap. Furthermore, motivated by the interference-free transmission criteria, we present a novel low-complexity greedy pilot scheduling algorithm to avoid the problem of initial value sensitivity. Simulation results show that the joint angle-delay subspace can be estimated effectively, and the proposed pilot reuse scheme combined with robust MMSE channel estimation offers significant performance gains.

Frequency domain channel estimation for MIMO SC-FDMA systems with CDM pilots

In this paper, we investigate the frequency domain channel estimation for multiple-input multiple-output (MIMO) single-carrier frequency-division multiple-access (SC-FDMA) systems. In MIMO SC-FDMA, code-division multiplexed (CDM) pilots such as cyclic-shifted Zadoff-Chu sequences have been adopted for channel estimation. However, most frequency domain channel estimation schemes were developed based on frequency-division multiplexing of pilots. We first develop a channel estimation error model by using CDM pilots, and then analyze the mean-square error (MSE) of various minimum MSE (MMSE) frequency domain channel estimation techniques. We show that the cascaded one-dimensional robust MMSE (C1D-RMMSE) technique is complexity-efficient, but it suffers from performance degradation due to the channel correlation mismatch when compared to the two-dimensional MMSE (2D-MMSE) technique. To improve the performance of C1D-RMMSE, we design a robust iterative channel estimation (RITCE) with a frequency replacement (FR) algorithm. After deriving the MSE of iterative channel estimation, we optimize the FR algorithm in terms of the MSE. Then, a low-complexity adaptation method is proposed for practical MIMO SC-FDMA systems, wherein FR is performed according to the reliability of the data estimates. Simulation results show that the proposed RITCE technique effectively improves the performance of C1D-RMMSE, thus providing a better performance-complexity tradeoff than 2D-MMSE.

Robust MMSE Design With Asynchronous Interference Mitigation in Cooperative Base Station Systems

This paper investigates minimum mean square error (MMSE) with asynchronous interference mitigation in cooperative base station systems. We consider the asynchronous transmission because of the diff...

Robust Fast Time-Varying Multipath Fading Channel Estimation and Equalization for MIMO-OFDM Systems via a Fuzzy Method

Channel estimation is an important issue for wireless communication systems. A channel estimation scheme using a Takagi-Sugeno (T-S) fuzzy-based Kalman filter under the time-varying velocity of the mobile station in a multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system is proposed in this paper. The fuzzy technique is used to interpolate several linear models to approximate the nonlinear estimation system. A MIMO system with the orthogonal space-time block coding (OSTBC) scheme is considered, where the radio channel is modeled as an autoregressive (AR) random process. The parameters of the AR process and the channel gain are simultaneously estimated by the proposed method. One-step-ahead prediction can be obtained during this estimation procedure. This is useful for the decision-directed channel-tracking design, particularly in the fast-fading channel. Furthermore, the robust minimum mean-square error (MMSE) equalization design can be achieved by considering the channel prediction error to improve the performance of symbol detection. To validate the performance of our proposed method, several simulation results are given and compared with those of other methods. When considering the time-varying velocity of the mobile station communication in the MIMO-OFDM system, the enhanced equalizer based on the T-S fuzzy-based Kalman filter performs better than those based on the conventional channel estimators in terms of symbol error rate.

Robust MMSE Precoding in MIMO Channels With Pre-Fixed Receivers

In this paper, we design robust precoders, under the minimum mean square error (MMSE) criterion, for different types of channel state information (CSI) in multiple-input multiple-output (MIMO) channels. We consider low-complexity pre-fixed receivers that may adapt to the channel but are oblivious to the existence of a precoder at the transmitter. In particular, three types of CSI are taken into account: i) perfect CSI, ii) statistical CSI in the form of mean feedback, and iii) deterministic imperfect CSI assuming that the actual channel is within the neighborhood of a nominal channel, which leads to the worst-case robust design that is the focus of this paper. Interestingly, it is found that, under some mild conditions, the optimal transmit directions, i.e., the left singular vectors of the precoder, are equal to the right singular vectors of the channel, the channel mean, and the nominal channel for perfect CSI, statistical CSI, and the worst-case design, respectively. Consequently, the matrix-valued problems can be simplified to scalar power allocation problems that either admit closed-form solutions or can be efficiently solved by the proposed algorithm.

Joint CFO and channel estimation for OFDMA uplink: an application of the variable projection method

In this paper, we propose a joint carrier frequency offset (CFO) and channel estimation method for the uplink channel of orthogonal frequency division multiple access (OFDMA) systems. The proposed method includes two steps. In the first step, the variable projection (VP) method based on the least squares (LS) criterion is used for CFO estimation. In each iteration of the VP method, the CFOs and channel coefficients are updated separately, which distinguishes the VP method from an existing non-separated minimum mean square error (MMSE) method in which the CFOs and channel coefficients are treated together as a whole. In the second step, the channel is estimated by employing the robust MMSE method. The simulation results show that, compared with the non-separated MMSE method, the proposed method has faster convergence rate in terms of CFO estimation and achieves better channel estimation performance in most of the cases. The computational complexity of the proposed scheme is also lower than that of the non-separated MMSE method for large number of users at high signal-to-noise ratios.