Abstract
The block diagonalization (BD) precoding technique is a well-known linear transmit strategy for multiuser multi-input multi-output (MU-MIMO) systems. 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 lattice reduction-aided decoding (LRAD) features the reduced decoding complexity in MIMO communications. The Lenstra-Lenstra-Lovasz (LLL) algorithm has been extensively used to obtain better bases of the channel matrix while the complex lattice reduction (CLR) is aimed at improving orthogonality of basis vectors and shortening them. The orthogonalization and size reduction work are left for the CLR algorithm so that a modification of the channel matrix is carried out, resulting in better precoding and detection performances. We also derive bounds for lattice decoding. Simulation results show that the bit error rate (BER) performance of our proposed algorithm is better than that of conventional ones and it reduces the complexity compared with the LLL algorithm-based schemes.
Highlights
Multiple-input multiple-output (MIMO) systems have been proposed for the next-generation wireless communication systems to increase the transmission capacity, and a high-performance and low-complexity MIMO detector becomes an important issue
If the second precoding filters for the equivalent single user MIMO (SU-MIMO) channels after the first singular value decomposition (SVD) were designed based on the lattice-reduced channel matrix, a better bit error rate (BER) performance can be achieved
We propose complex lattice reduction aided with block diagonalization for multiuser multi-input multi-output (MU-MIMO) systems
Summary
Multiple-input multiple-output (MIMO) systems have been proposed for the next-generation wireless communication systems to increase the transmission capacity, and a high-performance and low-complexity MIMO detector becomes an important issue. If the second precoding filters for the equivalent SU-MIMO channels after the first SVD were designed based on the lattice-reduced channel matrix, a better bit error rate (BER) performance can be achieved. A CLR-aided regularized BD (RBD) precoding algorithm is proposed, which has a lower complexity and achieves a better BER performance than the RBD or QR/SVD RBD [12, 13]. To reduce the complexity of precoding scheme, we employ the CLR to replace the SVD of conventional BD-based precoding algorithm by introducing a combined channel inversion to eliminate the MUI. The simulation results show that the BER performance of our proposed algorithm is better than that of conventional algorithms and the complexity is reduced compared with the LLL algorithm-based schemes.
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