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In this article, the transceiver design optimization problem is investigated for multi-hop multicasting amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, where multiple source nodes broadcast their message to multiple destination nodes via multiple serial relay nodes. Multiple antennas are installed at the sources, relays, and the destination nodes. In the transceiver design, we consider the mismatch between the true and the estimated channel state information (CSI), where the CSI mismatch follows the Gaussian-Kronecker model. A robust transceiver design algorithm is developed to jointly optimize the source, relay, and destination matrices to minimize the maximal weighted mean-squared error (WMSE) of the received message at all destination nodes. In particular, the WMSE is made statistically robust against the CSI mismatch by averaging through the distributions of the true CSI. Moreover, the WMSE decomposition is exploited to reduce the computational complexity of the transceiver optimization. Numerical simulations show a better performance of the proposed robust transceiver design against the channel mismatch.

Two-way relay-based power-line communication (PLC) has been used in many smart grid applications. It is necessary to model the channel characteristics of such system in order to design a reliable communication link between premises and data centre. The channel transfer function (CTF) is preferred to be found using the bottom-up approach, as it represents the true topology of the PLC network. In this paper, we model a bottom-up multipath channel model for single relay two-way PLC channel based on transmission and reflection coefficients. We also compare the proposed model with the ABCD method and the voltage ratio approach (VRA) method. We show that the multipath model can well represent the two-way relay-based PLC channel.

Power line communications (PLC) is one of the preferred technology for smart grid communication. Recent works have shown that the performance of the system improved with the aid of relay nodes. However, many PLC applications need two-way communication links where the channels are highly correlated to each other. For this reason, two-relay two-way PLC channel system become more desirable. In this paper, we aim to model the two-relay PLC forward and backward channel transfer functions (CTFs) using ABCD matrix. We also investigate the channel characteristics by comparing the CTFs between single-relay and two-relay two-way PLC systems. It is shown that for non-direct links, the CTFs of the two systems are comparable. However, single-relay systems have better direct link CTFs compared to the ones of two-relay systems.

A point-to-point (P2P) indoor power line communications (PLC) channel has similar broadcasting nature as the wireless propagation channel because of its tree-like topology. However, once a relay node is introduced into the PLC system, it shows some notable differences between the relay-aided (RA) PLC channels and the wireless channels. In the literature, a channel model for three-node one-way RA PLC system has been discussed. However, three-node two-way PLC system is needed in several applications such as smart grids. Therefore, in this paper, we aim to model the RA PLC system with forward and reverse channels. We use the ABCD method to derive the channel transfer function (CTF) expressions. The CTFs of an RA PLC channel cannot be simply stimulated by cascading two independent P2P channels which can be proved by the simulation results.

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.

In this paper, we propose a superimposed channel training algorithm 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). The proposed algorithm can estimate the individual first-hop and second-hop time-varying channel matrices of MIMO relay systems. 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 which determines 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.

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.

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.

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.