A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding

TL;DR

This paper introduces a novel sphere decoding algorithm that achieves DMT optimality by pruning the search tree to a single branch at high SNR, significantly reducing complexity while maintaining optimality.

Contribution

The proposed tree pruning sphere decoding algorithm reduces complexity to visiting only one branch at high SNR while preserving DMT optimality, unlike traditional methods.

Findings

Achieves DMT optimality with single-branch search at high SNR.

Reduces computational complexity compared to conventional sphere decoding.

Validated through simulations in different scenarios.

Abstract

We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm which visits merely a single branch of the search tree of the sphere decoding (SD) algorithm, while maintaining the DMT optimality at high signal to noise ratio (SNR) regime. The search tree of the sphere decoding algorithm is pruned via intersecting one dimensional spheres with the hypersphere of the SD algorithm, and the radii are chosen to guarantee the DMT optimality. In contrast to the conventional DMT optimal SD algorithm, which is known to have a polynomial complexity at high SNR regime, we show that the proposed method achieves the DMT optimality by solely visiting a single branch of the search tree at high SNR regime. The simulation results are corroborated with the claimed characteristics of the algorithm in two different scenarios.