• ETRI Journal
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• v.35 no.5
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• pp.919-922
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• 2013

A MIMO multi-way relay channel with full data exchange in which K users exchange messages with each other via the help of a single relay is considered. For the case in which each link is quasi-static Rayleigh fading and the relay is full-duplex, the fundamental diversity-multiplexing tradeoff (DMT) is investigated, and we show that a compress-and-forward relay protocol can achieve the optimal DMT.

• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.67-74
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• 2014

In multi-hop communication systems, two-way relaying is one of the solutions to mitigate the spectral efficiency loss caused by a half-duplex transmission. In this paper, a simple two-way relaying scheme is proposed for two-hop two-user multiple input multiple output (MIMO) systems. In the proposed system, a base station and a relay station (RS), both equipped with two antennas, form a point-to-point MIMO channel, while the RS and two single-antenna mobile users form a point-to-multipoint multiuser (MU)-MIMO channel. Numerical examples show that the proposed system achieves a significant sum rate gain as compared to a one-way relaying system as the distance between a relay and the two users decreases. We also show that although we can expand the proposed scheme to more than two users, its performance gain as compared to that of one-way relaying decreases with an increase in the number of users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4967-4986
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• 2015

Multi-hop relaying communications have great potentials in improving transmission performance by deploying relay nodes. The benefit is critically dependent on the accuracy of the channel state information (CSI) of all the transmitting links. However, the CSI has to be estimated. In this paper, we investigate the channel estimation problem in one-way multi-hop MIMO amplify-and-forward (AF) relay system, where both the two-hop and three-hop communication link exist. Traditional point-to-point MIMO channel estimation methods will result in error propagation in estimating relay links, and separately tackling the channel estimation issue of each link will lose the gain as part of channel matrices involved in multiple communication links. In order to exploit all the available gains, we develop a novel channel estimation model by structuring different communication links using the PARAFAC and PARATUCK2 tensor analysis. Furthermore, a two-stage fitting algorithm is derived to estimate all the channel matrices involved in the communication process. In particular, essential uniqueness is further discussed. Simulation results demonstrate the advantage and effectiveness of the proposed channel estimator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.956-970
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• 2015

In this paper, we study the design of network coding for the generalized transmit scheme in multiple input multiple output Y channel, where K users wish to exchange specified and shared information with each other within two slots. Signal space alignment at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together. The cross-pair interference can be canceled during both multiple accessing channel phase and broadcasting channel phase. The proposed signal processing scheme achieves the degrees of freedom of ${\eta}(K)=K^2$ with fewer user antennas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3498-3511
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• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.