Reduced Complexity Sphere Decoding

TL;DR

This paper introduces a novel reduced complexity sphere decoding technique for MIMO systems that maintains performance while significantly lowering computational costs through metric deconstruction and structural exploitation.

Contribution

A new sphere decoding method that decreases computational complexity without performance loss, utilizing metric deconstruction and a smart implementation with initial radius from ZF-DFE.

Findings

Substantial reduction in decoding complexity for MIMO systems.

Efficient soft bit metric calculation in coded MIMO systems.

No performance degradation compared to traditional sphere decoding.

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 is introduced. This application calculates the soft bit metrics of a bit-interleaved convolutional-coded MIMO system in an efficient manner. 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. Other than that, a technique of a particular data structure is also incorporated to efficiently reduce the number of executions carried out by SD. Simulation results show that these approaches achieve substantial gains in terms of the computational complexity for both uncoded and coded MIMO systems.