Joint Transceiver Optimization For Two-Way Mimo Relay Systems With Mse Constraints

In the wireless communication system, MIMO relay, as a key technique, can effectively increase the system capacity and cell coverage. MIMO relay can be clarified to amplify-and-forward, decode-and-forward, coded cooperation, compressed-and-forward and so on according to the different protocols it uses. The AF protocol among them is nowadays considered as one of the most promising solutions for future and/or existing wireless communications since it provides a reasonable trade-off between benefits and practical implementation costs. Nowadays, the main research direction of AF relay is one-way communication system. That is to say, the signal can only transmit from one node to the other node via the relay node but cannot reverse sent at the same time. However, in practical system, the relay node need to forward two signals sent by two nodes on both sides of relay at the same time. This is the meaning of "two-way". Therefore we build the two-way MIMO relay model based on the practical scenario. On the other hand, we find that most of existing literatures use system power as constraints then optimize the objective function based on different criteria, such as MMSE, maximize MI and so on. However, few of them take QoS into consideration. Since QoS directly affect the user experience, this paper set QoS as constraints. Based on the strong link between the MSE and most commonly used MIMO communication system objective functions, the QoS criteria are set up as upper-bounds on the MSE of the signal waveform estimation at both destinations.In this paper, we propose a transceiver design for AF two-way MIMO relay systems that minimizes the total network transmission power subjecting to QoS constraints, which has not been considered before. Since the problem is non convex, an iterative algorithm is proposed to optimize the source, relay, and receive matrices by means of solving standard convex sub-problems, which attains a stationary point (possibly suboptimal) of the problem. First, we choose an initial point which satisfies the constraints. Based on this point we optimize the receiving matrix to Wiener filter. Second, in order to optimize relay matrix, we use Kronecker product and Schur complement to transform the original problem into SDP problem. It can be efficiently solved by cvx in MATLAB. Third, we convert the original problem into a form that can be solved by Lagrange multiplier method. And bisection is used to obtain two optimized source matrices. We continuously update five matrices in an alternating fashion till convergence. Note that each step of optimization may either decrease or maintain but cannot increase the objective function. Moreover, the objective function is lower bounded by zero. Therefore, the proposed algorithm converges.We also study the performance of the proposed algorithm through several numerical simulations in this paper. The simulation results demonstrate the high convergence speed of the proposed algorithm. Therefore, only a small number of iterations are required to achieve a good performance. This indicates that the proposed algorithm has a low complexity and short delay, which is important for practical two-way MIMO relay systems. In the other examples, we compare the total power versus the NMSE of the proposed iterative algorithm, the naive NAF algorithm and the SVD-based algorithm. The result shows that the proposed algorithm requires less total power than the NAF algorithm and the SVD-based algorithm. We also compare the system with different number of relay antennas and source/destination antennas. These examples demonstrate that the system with more antennas needs less total power than the system with less antennas, which reflects the typical tradeoff between QoS and complexity. Since the number of antennas in source/destination node has little effect on the algorithm complexity, we can simply increase the number of it to improve the system performance.