Complexity analysis for ML-based sphere decoder achieving avanishing performance-gap to brute force ML decoding
2012 (English)Conference paper (Refereed)
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 ratereliability- 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.
Place, publisher, year, edition, pages
2012. 127-130 p.
IdentifiersURN: urn:nbn:se:kth:diva-107471DOI: 10.3929/ethz-a-007071378OAI: oai:DiVA.org:kth-107471DiVA: diva2:576034
International Zürich Seminar on Communications (IZS)
FunderICT - The Next Generation
QC 201301182012-12-122012-12-122013-04-11Bibliographically approved