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Reduced Complexity Sphere Decoding for Square QAM via a New Lattice Representation
 

Summary: Reduced Complexity Sphere Decoding for Square
QAM via a New Lattice Representation
Luay Azzam and Ender Ayanoglu
Department of Electrical Engineering and Computer Science
University of California, Irvine
email: lazzam@uci.edu, ayanoglu@uci.edu
Abstract-- Sphere decoding (SD) is a low complexity maximum
likelihood (ML) detection algorithm, which has been adapted
for different linear channels in digital communications. The
complexity of SD has been shown to be exponential in some cases,
and polynomial in others and under certain assumptions. The
sphere radius and the number of nodes visited throughout the
tree traversal search are the decisive factors for the complexity
of the algorithm. The radius problem has been addressed and
treated widely in the literature. In this paper, we propose a
new structure for SD, which drastically reduces the overall
complexity. The complexity is measured in terms of the floating
point operations per second (FLOPS) and the number of nodes
visited throughout the algorithm's tree search. This reduction
in complexity is due to the ability of decoding the real and

  

Source: Ayanoglu, Ender - Department of Electrical and Computer Engineering, University of California, Irvine

 

Collections: Engineering