Abstract:
Sparsity based techniques have been found to be promising for detection in large/massive multiple-input multiple-output (MIMO) systems. They initialize with an initial so...Show MoreMetadata
Abstract:
Sparsity based techniques have been found to be promising for detection in large/massive multiple-input multiple-output (MIMO) systems. They initialize with an initial solution vector, localize its erroneous symbols and then correct them. However, the process works only if the errors in the initial solution vector are sparse enough and are localized accurately. In this paper, we improve the localizing capability by proposing a modification in the residual update computed by the generalized orthogonal matching pursuit algorithm. Subsequently, we show that the sparsity behaviour can be improved by concatenating error vectors to create a larger vector, with no increase the complexity. Combining both these strategies, we propose to concatenate MMSE solution vectors (say K) as a single vector and then apply the improved error localization algorithm. It is shown that the proposed strategy results in a significant improvement in error performance without compromising the complexity.
Published in: 2016 IEEE Globecom Workshops (GC Wkshps)
Date of Conference: 04-08 December 2016
Date Added to IEEE Xplore: 09 February 2017
ISBN Information: