TY - GEN
T1 - Towards approaching near-optimal MIMO detection performance ONAC-programmable baseband processor
AU - Ahmad, Ubaid
AU - Li, Min
AU - Amin, Amir
AU - Li, Meng
AU - Van Der Perre, Liesbet
AU - Lauwereins, Rudy
AU - Pollin, Sofie
PY - 2014
Y1 - 2014
N2 - Lattice Reduction aided softoutput MIMO detectors have been demonstrated to offer a promising gain. However, computing Log-Likelihood ratios (LLR) for near-optimal MIMO detection, still poses a significant challenge for practical implementations. In this work, we present counter-ML bit-flipping algorithm for LLR generation. The proposed LLR generation algorithm has been designed to take advantage of the previously reported list generation algorithm, Multi-Tree Selective Spanning (MTSS), by maximizing the reuse of computations. Afterwards, a C-programmable MIMO detector architecture providing both data level parallelism (DLP) and instruction level parallelism (ILP), is designed for implementation. The proposed solution supports multiple MIMO detection modes, with both hard and softoutput. Performance of the proposed solution can be tuned ranging from SIC to near-ML to near-MAP, by adjusting a single parameter. In case of 4 × 4 QAM-64, it achieves peak-throughputs of 2.43Gbps and 629Mbps in case of hard and softoutput MIMO detection, with only 66.37mW and 76.14mW respective power consumption.
AB - Lattice Reduction aided softoutput MIMO detectors have been demonstrated to offer a promising gain. However, computing Log-Likelihood ratios (LLR) for near-optimal MIMO detection, still poses a significant challenge for practical implementations. In this work, we present counter-ML bit-flipping algorithm for LLR generation. The proposed LLR generation algorithm has been designed to take advantage of the previously reported list generation algorithm, Multi-Tree Selective Spanning (MTSS), by maximizing the reuse of computations. Afterwards, a C-programmable MIMO detector architecture providing both data level parallelism (DLP) and instruction level parallelism (ILP), is designed for implementation. The proposed solution supports multiple MIMO detection modes, with both hard and softoutput. Performance of the proposed solution can be tuned ranging from SIC to near-ML to near-MAP, by adjusting a single parameter. In case of 4 × 4 QAM-64, it achieves peak-throughputs of 2.43Gbps and 629Mbps in case of hard and softoutput MIMO detection, with only 66.37mW and 76.14mW respective power consumption.
UR - http://www.scopus.com/inward/record.url?scp=84905251994&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854331
DO - 10.1109/ICASSP.2014.6854331
M3 - Conference contribution
AN - SCOPUS:84905251994
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3893
EP - 3897
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -