This paper presents a multi-user MIMO transceiver design with a block-based decomposition and precoding scheme. To exploit spatial diversity, we propose to use block-diagonal QR decomposition (BD-QRD) to decompose the channel matrix. To eliminate multiple access interference (MAI), block-TomlinsonHarashima precoding (B-THP) is further proposed to be combined with BD-QRD so that the equivalent channel matrix after precoding at the transmitter becomes a block diagonal matrix. On the other hand, the block-based sorting is adopted to balance the energy spread among all spatial pipes for BD-QRD and thus the performance can be further enhanced. With these decomposition and precoding techniques at the transmitter, the sphere decoding (SD) techniques can be employed at the receiver with a small revision to constrain the search space. We show that the proposed BD-QRD, B-THP, and constrained SD for multiuser MIMO systems retaining the spatial diversity, outperform the conventional QRD-THP and BD-SVD in the interested SNR region.Keywords-Tomlinson-Harashima precoding, multi-user MIMO, QR decomposition, sphere decoding. I. INTRODUCTION As communication technology evolves, multiple-input multiple-output (MIMO) techniques have been widely used in wireless communication systems. They can be classified into two categories. One is open-loop MIMO, in which channel state information (CSI) is only known to the receiver. The other is close-loop MIMO. In this scheme, joint transmitter and receiver design are possible based on the CSI feedback.In open-loop MIMO, spatial diversity or spatial multiplexing is the basic feature to enhance system performance or to upgrade transmission rate. Receiver design is a critical issue in this category. Linear detectors, like zero-forcing (ZF) and minimum mean square error (MMSE) algorithms, and nonlinear detectors, like sphere decoding, are often discussed.Various matrix decomposition schemes are adopted in closeloop single-user MIMO (SU-MIMO) systems, such as singular value decomposition (SVD) or geometric mean decomposition (GMD) [1]. The user data are then multiplied by a precoding matrix before transmission so that the spatially-multiplexed MIMO channels are decomposed into independent single-input single-output (SISO) channel pipes or are triangularized, both facilitating signal detection. On the other hand, dirty paper coding or Tomlinson-Harashima precoding (THP) can be used for interference mitigation once the CSI and thus the generated inter-antenna interference are known in advance [2]. We can see that more computational complexity is moved toward the
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