Keke Zu

Low-Complexity Lattice Reduction-Aided Regularized Block Diagonalization for MU-MIMO Systems

By employing the regularized block diagonalization (RBD) preprocessing technique, the MU-MIMO broadcast channel is decomposed into multiple parallel independent SU-MIMO channels and achieves the maximum diversity order at high data rates. The computational complexity of RBD, however, is relatively high due to two singular value decomposition (SVD) operations. In this letter, a low-complexity lattice reduction-aided RBD is proposed. The first SVD is replaced by a QR decomposition, and the orthogonalization procedure provided by the second SVD is substituted by a lattice-reduction whose complexity is mainly contributed by a QR decomposition. Simulation results show that the proposed algorithm can achieve almost the same sum-rate as RBD, substantial bit error rate (BER) performance gains and a simplified receiver structure, while requiring a lower complexity.

Generalized Design of Low-Complexity Block Diagonalization Type Precoding Algorithms for Multiuser MIMO Systems

Block diagonalization (BD) based precoding techniques are well-known linear transmit strategies for multiuser MIMO (MU-MIMO) systems. By employing BD-type precoding algorithms at the transmit side, the MU-MIMO broadcast channel is decomposed into multiple independent parallel single user MIMO (SU-MIMO) channels and achieves the maximum diversity order at high data rates. The main computational complexity of BD-type precoding algorithms comes from two singular value decomposition (SVD) operations, which depend on the number of users and the dimensions of each users channel matrix. In this work, low-complexity precoding algorithms are proposed to reduce the computational complexity and improve the performance of BD-type precoding algorithms. We devise a strategy based on a common channel inversion technique, QR decompositions, and lattice reductions to decouple the MU-MIMO channel into equivalent SU-MIMO channels. Analytical and simulation results show that the proposed precoding algorithms can achieve a comparable sum-rate performance as BD-type precoding algorithms, substantial bit error rate (BER) performance gains, and a simplified receiver structure, while requiring a much lower complexity.

Multi-Branch Tomlinson-Harashima Precoding Design for MU-MIMO Systems: Theory and Algorithms

Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side which is a dual to the successive interference cancelation (SIC) detection at the receive side. Like SIC detection, the performance of THP strongly depends on the ordering of the precoded symbols. The optimal ordering algorithm, however, is impractical for multiuser MIMO (MU-MIMO) systems with multiple receive antennas due to the fact that the users are geographically distributed. In this paper, we propose a multi-branch THP (MB-THP) scheme and algorithms that employ multiple transmit processing and ordering strategies along with a selection scheme to mitigate interference in MU-MIMO systems. Two types of multi-branch THP (MB-THP) structures are proposed. The first one employs a decentralized strategy with diagonal weighted filters at the receivers of the users and the second uses a diagonal weighted filter at the transmitter. The MB-MMSE-THP algorithms are also derived based on an extended system model with the aid of an LQ decomposition, which is much simpler compared to the conventional MMSE-THP algorithms. Simulation results show that a better bit error rate (BER) performance can be achieved by the proposed MB-MMSE-THP precoder with a small computational complexity increase.

Lattice reduction-aided regularized block diagonalization for multiuser MIMO systems

By employing the regularized block diagonalization (RBD) preprocessing technique, the multi-user multi-input multi-output (MU-MIMO) broadcast channel is decomposed into multiple parallel independent single user multi-input multi-output (SU-MIMO) channels and achieves the maximum diversity order at high data rates. The computational complexity of RBD, however, is relatively high due to two singular value decomposition (SVD) operations. In this paper, a low-complexity lattice reduction aided RBD is proposed. The first SVD is replaced by a QR decomposition, and the orthogonalization procedure provided by the second SVD is substituted by a lattice reduction whose complexity is mainly contributed by a QR decomposition. Simulation results show that the proposed algorithm can achieve almost the same sum-rate as RBD while offering a lower complexity and substantial BER gains with perfect as well as imperfect channel state information at the transmit side.

Lattice-reduction aided Successive Optimization Tomlinson-Harashima Precoding strategies for physical-layer security in wireless networks

In this paper, we investigate precoding techniques for physical-layer security in multi-user MIMO systems under various conditions of channel state information (CSI). A Lattice Reduction (LR) aided non-linear precoding technique based on Successive Optimization Tomlinson-Harashima Precoding (SO-THP) and Simplified Generalized Matrix Inversion (S-GMI) technique is proposed along with a strategy for injecting artificial noise prior to transmission. Simulation results show that the proposed LR-SO-THP+S-GMI precoding technique outperforms existing non-linear and linear precoding algorithms in terms of BER and secrecy rate performances.

Multi-branch lattice reduction successive interference cancellation detection for multiuser MIMO systems

In this paper, we propose a new detection technique for multiuser multiple-input multiple-output (MU-MIMO) systems. The proposed scheme combines a lattice reduction (LR) transformation, which makes the channel matrix nearly orthogonal, and then employs a multi-branch (MB) technique with successive interference cancellation (SIC). A single LR transformation is required for the receive filters of all branches in the scheme, which proposes a different ordering for each branch and generates a list of detection candidates. The best vector of estimated symbols is chosen according to the maximum likelihood (ML) selection criterion. Simulation results show that the proposed detection structure has a near-optimal performance while the computational complexity is much lower than that of the ML detector.

Multi-branch tomlinson-harashima precoding for single-user MIMO systems

Based on a multi-branch (MB) strategy, a new Tomlinson-Harashima precoding (THP) structure is proposed for single-user multiple-input multiple-output (SU-MIMO) systems, which offers a significant performance improvement compared to the conventional THP reported in the literature. The parallelized multiple branch signals are generated at the transmit side according to the pre-designed transmit patterns. An appropriate measurement metric is developed to select the best branch for transmission. Simulation results show that the proposed MB-THP precoder has a better bit error rate (BER) performance than the conventional THP.

Coordinated Tomlinson-Harashima precoding design algorithms for overloaded multi-user MIMO systems

Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side to implement the concept of dirty paper coding (DPC). The application of THP is restricted by the dimensionality constraint that the number of transmit antennas has to be greater or equal to the total number of receive antennas. In this paper, we propose an iterative coordinate THP algorithm for overloaded scenarios in which the total number of receive antennas is larger than the number of transmit antennas. The proposed algorithm is implemented on two types of THP structures, the decentralized THP (dTHP) with diagonal weighted filters at the receivers of the users, and the centralized THP (cTHP) with diagonal weighted filter at the transmitter. Simulation results show that a significantly better bit error rate (BER) and sum-rate performances can be achieved by the proposed iterative coordinate THP algorithm as compared to previously reported techniques.

Pre-sorted multiple-branch successive interference cancelation detection for high-dimensional MIMO systems

In multiple-input multiple-output (MIMO) systems, the maximum likelihood detection (MLD) can obtain the maximum receive diversity order while its computational complexity grows exponentially with the number of constellation points and the transmitted streams. Lattice reduction (LR)-aided detection can parallel the diversity order of MLD with low complexity; however, its performance gap to MLD becomes wider with the increase of the system dimension that is expected with very large MIMO systems. In this paper, pre-sorted multiple-branch successive interference cancelation (PS-MB-SIC) detection techniques are proposed. Simulation results show that the proposed algorithm can approach the diversity order of MLD with much lower complexity and is suited to high-dimensional MIMO systems.

Low-complexity lattice reduction-aided channel inversion methods for large multi-User MIMO systems

Low-complexity precoding algorithms are proposed in this work to reduce the computational complexity and improve the performance of regularized block diagonalization (RBD) based precoding schemes for large multi-user MIMO (MU-MIMO) systems. The proposed algorithms are based on a channel inversion technique, QR decompositions, and lattice reductions to decouple the MU-MIMO channel into equivalent SU-MIMO channels. Simulation results show that the proposed precoding algorithms can achieve almost the same sum-rate performance as RBD precoding, substantial bit error rate (BER) performance gains, and a simplified receiver structure, while requiring a lower complexity.