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We propose a lattice-reduction-aided sphere decoding (SD) algorithm for MIMO detection achieving exact maximum likelihood (ML) detection performance with very low computational complexity in the mid and high signal-to-noise ratio (SNR) regions. A simple criterion is presented to determine if the ML detection result is obtained by a primary search. If not, a further search will be carried out to find...
In this paper, we consider user selection criteria for various multiple input multiple output (MIMO) detectors to exploit the multiuser diversity. It is shown that the user selection criterion plays a crucial role in exploiting both multiuser and receive (or spatial) diversity. We also show that the maximum likelihood (ML) and even some low complexity suboptimal detectors (based on the lattice reduction...
In this paper, we propose a near-maximum likelihood (ML) detection method referred to as reduced dimension ML search (RD-MLS). The RD-MLS detector is based on a partitioned search method that divides the symbol space into two groups and searches over the vector space of one group instead of that comprising all of the symbols. First, a minimum mean square error (MMSE) dimension reduction operator suppressing...
In multiple antenna systems employing spatial multiplexing to raise data rates, it is preferable to use maximum likelihood (ML) detection to fully benefit from multiplexing and diversity gain. In this paper, we present a new tree search based detector that uses layer ordering in conjunction with a partial lattice basis reduction. Compared with conventional tree detectors, the error performance of...
Maximum-likelihood detection in MIMO communications amounts to solving a least-squares problem with a constellation (alphabet) constraint. One popular method that can be used to solve this problem is sphere decoding. We show in this letter that by employing a simple stopping criterion, it is possible to significantly reduce the complexity of sphere decoding over a wide range of SNRs, without a noticeable...
Many powerful data detection algorithms employed in multiple-input multiple-output (MIMO) communication systems, such as sphere decoding (SD) and lattice-reduction (LR)-aided detection, were initially designed for infinite lattices. Detection in MIMO systems is, however, based on finite lattices. In this paper, we systematically study the consequences of finite lattice-size for the performance and...
There are many maximum likelihood (ML) solutions to the detection problem for multiple-input multiple-output systems (MIMO). Recently, sphere detection (SD) method has been used as a powerful mean to find ML point. This method searches closest lattice point using number theory tools. Several algorithms for SD method were presented and among them, the algorithm II is an efficient one. In different...
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