Lattice basis reduction: improved practical algorithms and solving subset sum problems
Mathematical Programming: Series A and B
Fundamentals of wireless communication
Fundamentals of wireless communication
On the complexity of sphere decoding in digital communications
IEEE Transactions on Signal Processing
On the sphere-decoding algorithm I. Expected complexity
IEEE Transactions on Signal Processing - Part I
Statistical Pruning for Near-Maximum Likelihood Decoding
IEEE Transactions on Signal Processing
Sphere Decoding With a Probabilistic Tree Pruning
IEEE Transactions on Signal Processing - Part I
Closest point search in lattices
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
A unified framework for tree search decoding: rediscovering the sequential decoder
IEEE Transactions on Information Theory
From theory to practice: an overview of MIMO space-time coded wireless systems
IEEE Journal on Selected Areas in Communications
Hi-index | 0.00 |
In this letter, we propose an extension of the probabilistic tree pruning sphere decoding (PTP-SD) algorithm that provides further improvement of the computational complexity with minimal extra cost and negligible performance penalty. In contrast to the PTP-SD that considers the tightening of necessary conditions in the sphere search using per-layer radius adjustment, the proposed method focuses on the sphere radius control strategy when a candidate lattice point is found. For this purpose, the dynamic radius update strategy depending on the lattice point found as well as the lattice independent radius selection scheme are jointly exploited. As a result, while maintaining the effectiveness of the PTP-SD, further reduction of the computational complexity, in particular for high SNR regime, can be achieved. From simulations in multiple-input and multiple-output (MIMO) channels, it is shown that the proposed method provides a considerable improvement in complexity with near-ML performance.