Multiple-input Multiple-output Two-way Relay Networks Research Articles

Robust Optimization for Energy Efficiency in MIMO Two-Way Relay Networks With SWIPT

Energy efficiency (EE) is a key issue in energy-constrained wireless networks because of the energy scarcity. In this paper, we present the robust energy-efficient optimization design for multiple-input–multiple-output two-way relay networks with simultaneous wireless information and power transfer, where the available channel state information is considered to be imperfect. We model the channel errors by using the norm bounded model, and aim to maximize the worst-case EE by jointly designing the precoders of the sources and relay, and the power splitting ratio of the relay under the worst-case transmit power constraints at the sources and relay. To solve the robust EE optimization problem, we propose an iterative optimization algorithm based on the weighted minimum mean-square-error method, where the $\mathcal {S}$ -procedure and sign-definiteness lemma are employed to eliminate the channel errors. To balance the performance and complexity, we propose a channel diagonalization algorithm based on the generalized singular value decomposition, which has lower complexity. The effectiveness of the proposed algorithms is shown by numerical results.

Cognitive MIMO Two-Way Relay Network: Joint Optimal Relay Selection and Spectrum Allocation

In this paper, for an overlay cognitive radio system in which the primary users’ information exchange is assisted by a secondary multiple-input multiple-output two-way relay network, we propose two spectrum sharing schemes that achieve the full diversity gain in both the primary and secondary networks. In the spectrum sharing problem that we formulate, there are two degrees of freedom to be optimized: relay selection and spectrum allocation. We jointly optimize these two degrees of freedom such that the achievable rate in the secondary network is maximized under the constraint that a minimum quality of service requirement in terms of the achievable rate is satisfied for the primary network. We analyze the performance of the proposed schemes in terms of the outage probability (that reflects the rate of unsuccessful spectrum sharing), the asymptotic behavior and the diversity order over Rayleigh fading channel. The analysis of the paper shows that in both of the proposed schemes, the full diversity of order $N M^2$ is achieved by the primary users, where $N$ is the number of the secondary users and $M$ is the number of antennas per terminal. Moreover, in both of the schemes, the full selection diversity of order $N M^2$ is guaranteed for the secondary network. We also provide extensive simulation results to confirm the theoretical analysis of this paper.

On MIMO Linear Physical-Layer Network Coding: Full-Rate Full-Diversity Design and Optimization

This paper considers a multiuser communication network, where a receiver is set to compute functions of the messages from $K$ users. All user nodes and the receiver are equipped with multi-antenna. We propose a space–time (ST) coded multiple-input multiple-output (MIMO) linear physical-layer network coding (LPNC) scheme that promises full-rate and full-diversity, while achieving the maximum coding gain of LPNC. In the proposed framework, the users’ messages are encoded by the same linear dispersion ST code and transmitted simultaneously. The receiver exploits the MIMO LPNC mapping in reconstructing an arbitrary number of linearly network-coded (NC) messages. We derive the NC generator matrix that leads to the greatest coding gain and minimized error probability at the receiver. On top of that, we analytically show that the proposed ST coded LPNC scheme guarantees the full-diversity and full-rate transmission. The proposed method applies to a wide range of network configurations. Two case studies on: (1) MIMO two-way relay network and (2) MIMO multiple-access relay network are presented in this paper. For both case studies, numerical results are shown to demonstrate the performance improvement of the proposed scheme over conventional schemes by more than 4 dB, while the full-rate and full-diversity behaviors are in line with our analysis.

Iterative Beamformer Design for Multi-Node MIMO Two-Way Relay Networks Using Duality

In this paper, an iterative design method for beamformers is proposed for multiple-input multiple-output (MIMO) two-way relay networks with multiple pairs of source nodes. It is known that the joint beamformer design problem based on the minimum mean-square-error (MMSE) is known to be nonconvex, and it cannot be analytically solved. To manage this difficulty, a novel iterative algorithm is proposed, which uses duality relationships. First, the MSE duality for MIMO two-way relay networks with multiple pairs of nodes is presented. Moreover, the optimization problem is divided into three subproblems, which can be separately solved; then, the duality is applied to the proposed iterative algorithm, which provides closed-form solutions for all subproblems. The numerical results demonstrate that the proposed method improves network performance and can be applied to various communication systems that use the two-way relay network.

Approximate sum rate for massive multiple‐input multiple‐output two‐way relay with Ricean fading

This study considers a multi-pair massive multiple-input multiple-output two-way relay network where the M-antenna relay simultaneously serves K pairs of single-antenna users in the same time–frequency resource. For a more general case of Ricean fading channel, the authors propose a fixed-gain maximum ratio combing/maximum ratio transmission relay scheme. The approximate expressions on the ergodic sum rate with such scheme are derived in two cases: (i) M, which is much larger than the users’ Ricean factors, is large enough; and (ii) if M is large enough but bounded, the Ricean factors of all the users go to infinity. Simulation results show that the approximate results are very tight. Based on the result for the first case, they further discuss the power-scaling laws, which reveal that despite the Ricean fading channel and the interference at the relay, the achievable sum rate can still remain unchanged if the transmitted power at each user or at the relay is or both are made inversely proportional to M.

Joint Optimization of Source and Relay for MIMO Two-Way Relay Networks Using MSE Duality

In this letter, the joint optimization of a source-relay pair is proposed for multiple-input multiple-output (MIMO) two-way relay networks. In these networks, since the joint beamformer design problem based on the minimum mean square error (MMSE) is known to be non-convex, it cannot be solved analytically. In order to solve the problem, a novel iterative algorithm is proposed that uses duality relationships. First, the MSE duality for MIMO two-way relay networks is presented; then, this duality is applied to the proposed optimization method, which provides closed-form solutions for all subproblems. Because the proposed algorithm does not involve convex optimization software packages, it efficiently reduces the computational complexity. The numerical results demonstrate that the proposed method improves the MSE performance compared with the conventional method.

Sparse Channel Estimation for MIMO-OFDM Two-Way Relay Network with Compressed Sensing

Accurate channel impulse response (CIR) is required for equalization and can help improve communication service quality in next-generation wireless communication systems. An example of an advanced system is amplify-and-forward multiple-input multiple-output two-way relay network, which is modulated by orthogonal frequency-division multiplexing. Linear channel estimation methods, for example, least squares and expectation conditional maximization, have been proposed previously for the system. However, these methods do not take advantage of channel sparsity, and they decrease estimation performance. We propose a sparse channel estimation scheme, which is different from linear methods, at end users under the relay channel to enable us to exploit sparsity. First, we formulate the sparse channel estimation problem as a compressed sensing problem by using sparse decomposition theory. Second, the CIR is reconstructed by CoSaMP and OMP algorithms. Finally, computer simulations are conducted to confirm the superiority of the proposed methods over traditional linear channel estimation methods.