Lattice basis reduction: improved practical algorithms and solving subset sum problems
Mathematical Programming: Series A and B
An improved recursive algorithm for BLAST
Signal Processing
An efficient square-root algorithm for BLAST
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
The error probability of the fixed-complexity sphere decoder
IEEE Transactions on Signal Processing
On the complexity of sphere decoding in digital communications
IEEE Transactions on Signal Processing
A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems
IEEE Transactions on Wireless Communications
Fixing the Complexity of the Sphere Decoder for MIMO Detection
IEEE Transactions on Wireless Communications
Closest point search in lattices
IEEE Transactions on Information Theory
Adaptive control of surviving symbol replica candidates in QRM-MLD for OFDM MIMO multiplexing
IEEE Journal on Selected Areas in Communications
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QR-decomposition with M-algorithm (QRDM) achieves quasi-ML performance in multiple-input multiple-output (MIMO) multiplexing systems. Nevertheless, QRDM performs avoidable computations because of its systematic search strategy and its failure to consider the channel and noise conditions. Another drawback is that QRDM is sequential, which limits pipelining capabilities. In this paper, we propose quasi-ML adaptive parallel QRDM (APQRDM) and adaptive iterative QRDM (AIQRDM) algorithms based on set grouping. In set grouping, the tree-search stage of the QRDM algorithm is divided into partial detection phases (PDPs). These are processed in parallel and iteratively in the proposed APQRDM and AIQRDM algorithms, respectively. Therefore, when the tree-search stage of the QRDM algorithm is divided into G PDPs, the latency of the proposed APQRDM algorithm and the hardware requirements of the proposed AIQRDM algorithm are reduced by a factor of G compared to the QRDM algorithm. Moreover, simulation results show that in 4 脳 4 MIMO system, and at E b /N 0 of 12 dB, APQRDM decreases the average computational complexity to approximately 43% that of the conventional QRDM. Also, at E b /N 0 of 0 dB, AIQRDM algorithm reduces the computational complexity to about 54%, and the average number of metric comparisons to approximately 10%, compared to the conventional QRDM.