Low-complexity near-maximum-likelihood detection and precoding for MIMO systems using lattice reduction

By Christoph Windpassinger, Robert F. H. Fischer. Proceedings 2003 IEEE Information Theory Workshop (Cat. No.03EX674), 2003

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Complex Lattice Reduction Algorithm for Low-Complexity Full-Diversity MIMO Detection (2009, background): “Since upon scaling and shifting [ 10 ], [11] the vector can be viewed as a lattice point,2 MIMO detection can be formulated as finding a reasonably close lattice point to given . By considering the reduced basis instead of , it can be shown that the performance of traditional detection schemes like ”
Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs (2015, methodology): “The LLL based LR algorithm was shown to be capable of achieving the maximum attainable/full receive diversity in MIMO decoding [87].”
Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice reduction (2004, background): “3Note that again proper shifting and scaling is necessary in order to allow for simple rounding in the quantization step [4].”
A unified framework for tree search decoding: rediscovering the sequential decoder (2005, methodology): “We also report the performance of the Yao–Wornell and Windpassinger–Fischer (YWWF) decoder which has the same complexity as the MMSE-DFE Babai decoder [48], [49].”
On maximum-likelihood detection and the search for the closest lattice point (2003, background): “This interest is due to the significant performance gain achieved by sphere decoders compared to other suboptimal detection schemes [24]–[26], and to their average polynomial complexity (experimentally demonstrated) at medium-to-high SNR.”