TY - GEN
T1 - Adaptive parallel and iterative QRDM algorithms for spatial multiplexing MIMO systems
AU - Mohaisen, Manar
AU - Chang, Kyung Hi
AU - Koo, Bontae
PY - 2009
Y1 - 2009
N2 - 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 unawareness of the channel and noise conditions. Another drawback is that QRDM has a sequential nature which limits the capabilities of pipelining. In this paper, we propose semi-ML adaptive parallel QRDM (APQRDM) and iterative QRDM (AIQRDM) algorithms based on set grouping. Using the set grouping, the tree-search stage of QRDM algorithm is divided into partial detection phases (PDP). Therefore, when the tree-search stage of QRDM is divided into 4 PDPs, the APQRDM latency is one fourth of that of the QRDM, and the hardware requirements of AIQRDM is approximately one fourth of that of QRDM. Moreover, simulation results show that in 4 × 4 system and at Eb/N0 of 14 dB, APQRDM decreases the average computational complexity to approximately 43% of that of the conventional QRDM. Also, at Eb/N0 of 0dB, AIQRDM reduces the computational complexity to about 54% and the average number of metric comparisons to approximately 10% of those required by the conventional QRDM and AQRDM.
AB - 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 unawareness of the channel and noise conditions. Another drawback is that QRDM has a sequential nature which limits the capabilities of pipelining. In this paper, we propose semi-ML adaptive parallel QRDM (APQRDM) and iterative QRDM (AIQRDM) algorithms based on set grouping. Using the set grouping, the tree-search stage of QRDM algorithm is divided into partial detection phases (PDP). Therefore, when the tree-search stage of QRDM is divided into 4 PDPs, the APQRDM latency is one fourth of that of the QRDM, and the hardware requirements of AIQRDM is approximately one fourth of that of QRDM. Moreover, simulation results show that in 4 × 4 system and at Eb/N0 of 14 dB, APQRDM decreases the average computational complexity to approximately 43% of that of the conventional QRDM. Also, at Eb/N0 of 0dB, AIQRDM reduces the computational complexity to about 54% and the average number of metric comparisons to approximately 10% of those required by the conventional QRDM and AQRDM.
UR - http://www.scopus.com/inward/record.url?scp=77951460002&partnerID=8YFLogxK
U2 - 10.1109/VETECF.2009.5379003
DO - 10.1109/VETECF.2009.5379003
M3 - Conference contribution
AN - SCOPUS:77951460002
SN - 9781424425150
T3 - IEEE Vehicular Technology Conference
BT - Proceedings of the 2009 IEEE 70th Vehicular Technology Conference Fall, VTC 2009 Fall
T2 - 2009 IEEE 70th Vehicular Technology Conference Fall, VTC 2009 Fall
Y2 - 20 September 2009 through 23 September 2009
ER -