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Reduced Complexity Sphere Decoding Boyu Li and Ender Ayanoglu
 

Summary: 1
Reduced Complexity Sphere Decoding
Boyu Li and Ender Ayanoglu
Center for Pervasive Communications and Computing
Department of Electrical Engineering and Computer Science
The Henry Samueli School of Engineering
University of California - Irvine
Irvine, California 92697-2625
Email: boyul@uci.edu, ayanoglu@uci.edu
Abstract
In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can achieve performance equivalent
to full search Maximum Likelihood (ML) decoding with reduced complexity. Several researchers reported techniques
that reduce the complexity of SD further. In this paper, a new technique is introduced which decreases the
computational complexity of SD substantially, without sacrificing performance. The reduction is accomplished
by deconstructing the decoding metric to decrease the number of computations and exploiting the structure of a
lattice representation. Furthermore, an application of SD employing a proposed smart implementation with very
low computational complexity for calculating the soft bit metrics of a bit-interleaved convolutional-coded MIMO
system is presented. Based on the reduced complexity SD, the proposed smart implementation employs the initial
radius acquired by Zero-Forcing Decision Feedback Equalization (ZF-DFE) which ensures no empty spheres, and
is incorporated with a technique efficiently reducing the number of executions carried out by SD. Simulation results

  

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

 

Collections: Engineering