Complexity analysis for ML-based sphere decoder achieving a vanishing performance-gap to brute force ML decoding

Arun Kumar Singh (1) , Petros Elia (2) , Joakim Jalden (3)

1 Eurecom [Sophia Antipolis]
2 Mobile communications
3 KTH - KTH Royal Institute of Technology [Stockholm]

This work identifies the computational reserves required for the maximum likelihood (ML)-based sphere decoding solutions that achieve, in the high-rate and high-SNR limit, a vanishing gap to the error-performance of the optimal brute force ML decoder. These error performance and complexity guarantees hold for most multiple-input multiple-output scenarios, all reasonable fading statistics, all channel dimensions and all full-rate lattice codes. The analysis also identifies a rate-reliability-complexity tradeoff establishing concise expressions for the optimal diversity gain achievable in the presence of any run-time constraint imposed due to the unavailability of enough computational resources required to achieve a vanishing gap.

Domaines: Théorie de l'information et codage [math.IT]; Théorie de l'information [cs.IT]

Arun Kumar Singh, Petros Elia, Joakim Jalden. Complexity analysis for ML-based sphere decoder achieving a vanishing performance-gap to brute force ML decoding. IEEE International Zurich Seminar on Communications, Feb 2012, Zurich, Switzerland. pp.127-130. ⟨hal-00707787⟩