Blind identification of FIR MIMO channels by decorrelating subchannels

We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required by most existing methods for the same problem.

An approach to nonirreducible MIMO FIR channel equalization

It is known that a nonirreducible multiple-input– multiple-output finite-impulse-response channel driven by colored signals that are mutually uncorrelated and of sufficiently diverse power spectra can be identified blindly by exploiting only the second-order statistics of the measured data. In this brief, we propose an approach to dealing with the equalization of a nonirreducible channel, provided that the estimate of the channel matrix is available. Both zero-forcing and minimum-mean-square-error equalizers are developed to perform the channel equalization. The effectiveness of the approach and equalizers is demonstrated by simulation examples.

An iterative equalization method for nonirreducible MIMO FIR channels

This paper deals with the equalization of a nonirreducible multiple-input multiple-output (MIMO) finite-impulse-response (FIR) channel provided that the estimate of the channel matrix is available. An iterative method is developed to perform the channel equalization. The effectiveness of the proposed equalization method is demonstrated by simulation examples.

Channel estimation of dual-hop MIMO relay system via parallel factor analysis

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

Superimposed channel training for two-way MIMO relay systems

Two-way relaying systems are known to be capable of providing higher spectral efficiency compared with one-way relaying systems. However, the channel estimation problem for two-way relaying systems becomes more complicated. In this paper, we propose a superimposed channel training scheme for two-way MIMO relay communication systems, where the individual channel information for users-relay and relay-users links are estimated. The optimal structure of the source and relay training sequences are derived when the mean-squared error (MSE) of channel estimation is minimized. We also optimize the power allocation between the source and relay training sequences to improve the performance of the algorithm. Numerical examples are shown to demonstrate the performance of the proposed channel training algorithm.

Robust channel estimation algorithm for dual-hop MIMO relay channels

In conventional two-phase channel estimation algorithms for dual-hop multiple-input multiple-output (MIMO) relay systems, the relay-destination channel estimated in the first phase is used for the source-relay channel estimation in the second phase. For these algorithms, the mismatch between the estimated and the true relay-destination channel affects the accuracy of the source-relay channel estimation. In this paper, we investigate the impact of such channel state information (CSI) mismatch on the performance of the two-phase channel estimation algorithm. By explicitly taking into account the CSI mismatch, we develop a robust algorithm to estimate the source-relay channel. Numerical examples demonstrate the improved performance of the proposed algorithm. © 2012 IEEE.

Blind estimation of MIMO relay channels

In this paper, we integrate two blind source separation (BSS) methods to estimate the individual channel state information (CSI) for the source-relay and relay-destination links of three-node two-hop multiple-input multiple-output (MIMO) relay systems. In particular, we propose a first-order Z-domain precoding technique for the blind estimation of the relay-destination channel matrix, while an algorithm based on the constant modulus and mutual information properties is developed to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, our algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm. © 2014 IEEE.

Channel estimation for frequency-selective two-way MIMO relay systems

In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We propose a superimposed channel training algorithm to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the meansquared error (MSE) of channel estimation. We also derive the optimal power allocation between the source and relay training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.

A novel approach to maximize the sum-rate for MIMO broadcast channels

This paper considers the sum-rate of wireless broadcast systems with multiple antennas at the base station. In a conventional MIMO-BC system with a large number of users, selecting an optimal subset of users to maximizing the overall system capacity is a key design issue. This paper presents a novel approach to investigate the sum-rate using Eigen Value Decomposition (EVD). Particularly, we derive the lower bound on sum-rate of a conventional MIMO-BC using a completely different approach compared to the existing approaches. The paper formulates the rate maximization problem for any number of users and any number of transmitting antennas using EVD approach of the channel matrix. This also shows the impact of channel angle information on the sum-rate of conventional MIMO-BC. Numerical results confirm the benefits of our technique in various MIMO communication scenarios.

Channel estimation for time-varying MIMO relay systems

In this paper, we investigate the channel estimation problem for multiple-input multiple-output (MIMO) relay communication systems with time-varying channels. The time-varying characteristic of the channels is described by the complex-exponential basis expansion model (CE-BEM). We propose a superimposed channel training algorithm to estimate the individual first-hop and second-hop time-varying channel matrices for MIMO relay systems. In particular, the estimation of the second-hop time-varying channel matrix is performed by exploiting the superimposed training sequence at the relay node, while the first-hop time-varying channel matrix is estimated through the source node training sequence and the estimated second-hop channel. To improve the performance of channel estimation, we derive the optimal structure of the source and relay training sequences that minimize the mean-squared error (MSE) of channel estimation. We also optimize the relay amplification factor that governs the power allocation between the source and relay training sequences. Numerical simulations demonstrate that the proposed superimposed channel training algorithm for MIMO relay systems with time-varying channels outperforms the conventional two-stage channel estimation scheme.