Sequential and Global Likelihood Ascent Search-Based Detection in Large MIMO Systems

• Published in: IEEE transactions on communications Vol. 66; no. 2; pp. 713 - 725
• Main Authors: Sah, Abhay Kumar, Chaturvedi, A. K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Ascent; Complexity; Complexity theory; Computer simulation; Detectors; Errors; Large MIMO; LAS algorithms; Measurement; MIMO; MIMO (control systems); neighborhood search algorithms; Search algorithms; Transmitting antennas
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2017.2761383

Neighborhood search algorithms have been proposed for low complexity detection in large/massive multipleinput multiple-output systems. They iteratively search for the vector, which minimizes the maximum likelihood (ML) cost in a fixed neighborhood. However, the ML solution may not lie in the searched space and also the search may go through a large number of intermediate vectors. Motivated by this, we first propose to cut down the size of the neighborhood so that the complexity of such algorithms can be reduced. Second, we also look for an update which is not restricted to be in a fixed neighborhood. This helps in improving the error performance. For the first purpose, we propose a metric and a few selection rules to decide whether or not to include a vector in the neighborhood. We use the indices of, say K, largest components of the metric for generating a reduced neighborhood set, which is used to reduce the complexity of the existing algorithms while maintaining their error performance. Furthermore, this reduced set facilitates the proposal of two new search algorithms. Simulation results show that the proposed algorithms have a much better error performance and also lower complexity compared with the existing algorithms.