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This paper investigates nonlinear transceiver design for full-duplex multiple-input multiple-output (FD-MIMO) relay systems. A dual-hop amplify-and-forward relaying protocol is considered. At the destination, nonlinear successive-interference-cancellation (SIC) is used for signal detection. The goal is to find the source and relay precoders such that the symbol-vector error rate (SVER) can be minimized. Due to the loop interference (LI), optimizing the relay precoder in FD systems is much more involved. In this paper, we propose novel designs to solve this problem. Starting from the QR-SIC receiver, we theoretically show that the relay precoder can be solved with a closed-form expression even when the system incurs LI. Then, we consider the system with a minimum mean-squared-error SIC receiver, where the relay precoder design entails a different problem formulation and introduces new challenges. We propose a novel iterative method, with closed-form solutions in each iteration, to solve this problem. Simulations show that our designs can significantly improve the SVER performance for FD-MIMO relay systems. [ABSTRACT FROM AUTHOR]

Full-duplex (FD) multiple-input multiple-output relaying has been considered an effective scheme to increase the spectral efficiency for wireless communications. As known, the main problem for the FD system is the cancellation of loop interference (LI). In this paper, we propose using the joint source/relay precoding to reduce the influence of LI. Therein, linear precoders are used at the source and relay, while the minimum mean-squared-error receiver is adopted at the destination. The joint precoder design is complicated when spatial multiplexing is exploited for signal transmission. To solve the problem, we propose an iterative method in which the original problem is split into two subproblems. With some matrix properties, we then show that each subproblem can be formulated as a convex optimization. Finally, a closed-form solution can be obtained with the Karush–Kuhn–Tucker conditions. Using a mean-squared-error upper bound, we also propose a low-complexity method to reduce the computational complexity. The proposed precoders have closed-form expressions, which is a great advantage in real-world implementation. Simulation results show that the proposed methods significantly outperform existing ones. [ABSTRACT FROM AUTHOR]

In this paper, we study the codebook size design strategies in the multiple-input–multiple-output (MIMO) broadcast channel (BC) systems. We adopt the nonlinear Tomlinson–Harashima precoder (THP), which is designed by the imperfect channel direction information (CDI) acquired from the random vector quantization (RVQ) feedback mechanism. Under the limitation of the total feedback rate, we flexibly allocate the size of each user's codebook so that the overall channel capacity is maximized. Since the design problem is not convex, directly finding out the optimum allocation of codebook size is not attainable. However, by sophisticatedly using the discrete majorization theory, we can derive the codebook size allocation strategies for the high- and the low-signal-to-noise-ratio (SNR) regions. Interestingly, the results show that the equal size codebook is preferable in the low-SNR region, whereas the codebook allocates the whole available codebook size to a single user in the high-SNR region. Simulations certify the theoretical result of our design. [ABSTRACT FROM PUBLISHER]

We consider joint source/relay precoding in three-node two-hop amplify-and-forward (AF) multiple-input–multiple-output (MIMO) relay systems. In our systems, linear precoders are used at the source and the relay, and the QR successive interference cancelation (SIC) receiver is used at the destination. Our design criterion is to minimize the block error rate (BLER) of the receiver. Since the BLER is a complicated function of the source and relay precoders, and the power constraints are coupled, the optimization problem is difficult to solve. To overcome the difficulty, we first apply the primal decomposition approach, transforming the original optimization to a subproblem and a master problem. In the subproblem, the optimum source precoder can be obtained with the geometric mean decomposition (GMD). In the master problem, however, the optimum relay precoder cannot be straightforwardly obtained. We theoretically prove that the optimum relay precoder exhibits a matrix diagonalization property. Using this property, we can then transform the master problem into a scalar-variable concave optimization problem. A closed-form solution can be derived by the Karuch–Kuhn–Tucker (KKT) conditions. Finally, we extend our method to the two-hop AF MIMO relay system with the minimum mean square error (MMSE) SIC receiver. Assuming a unitary source precoder, we obtain the optimum source and relay precoders in closed form. Simulations show that the proposed transceivers can significantly improve the system performance. [ABSTRACT FROM PUBLISHER]

In this paper, we study a far-end channel estimation problem in a three-node multiple-input–multiple-output (MIMO) relay system. The estimation is accomplished in two phases. In the first phase, the source node keeps silence, the relay transmits pilots, and the destination estimates the relay-to-destination channel. In the second phase, the source transmits pilots, and the relay amplifies the received pilots with a precoder and forwards the resultant pilots to the destination. The destination then estimates the source-to-relay channel based on the received signals and the estimated relay-to-destination channel in the first phase. We aim to conduct a robust design deriving the optimum source pilots and precoding matrix with the minimum mean square error (MMSE) criterion. The design can be easily formulated as an optimization problem; however, the problem is not convex and is difficult to solve. We then propose using a lower bound of the objective function in the optimization problem. We show that the optimization of the lower bound is equivalent to the original problem when channel correlation matrices have certain structures. With the correlation matrices, we can derive optimum pilot structures so that the proposed optimization can be transferred to a scalar-valued concave optimization, and a closed-form solution can be obtained via Karush–Kuhn–Tucker (KKT) conditions. Simulations show that our proposed method outperforms existing nonrobust methods. [ABSTRACT FROM PUBLISHER]