Conditioning properties of the LLL algorithm

Luk, Franklin T.; Qiao, Sanzheng

doi:10.1117/12.834183

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The for loop between Line 4 to 6 reduces the ith basis vector and creates non-zero entries r k,i , k = i + 1, ..., j. The first part of the process Triangulate R eliminates the non-zero entries from r j,i to r i+1,i by the plane rotations [19,20]. The elimination process will create another sequence of non-zero elements r k+1,k , k = i + 1, ..., j − 1 on the subdiagonal.…”

Section: A Hybrid Methods For Lattice Basis Reductionmentioning

confidence: 99%

A Hybrid Method for Lattice-Reduction-Aided MIMO Detection

Tian¹,

Qiao²

2016

World Congress on Electrical Engineering and Computer Systems and Science

Self Cite

View full text Add to dashboard Cite

Lattice reduction has been successfully applied to data detection in multiple-input multiple-output (MIMO) systems. In this paper, we introduce a polynomial time algorithm for lattice-reduction-aided (LR-aided) MIMO detection. The hybrid method we present integrates the length-based size reduction technique into an angle-measured method. To assess the performance of the algorithm, we compare it with the LLL algorithm, a widely used algorithm in MIMO and wireless communications. Our experimental results show that despite that the two algorithms have the same complexity, the hybrid method is empirically more efficient than the LLL algorithm, and the communication channels improved by our hybrid method have smaller bit error rate (BER) than those improved by the LLL algorithm in data detection.

show abstract

Section: A Hybrid Methods For Lattice Basis Reductionmentioning

confidence: 99%

A Hybrid Method for Lattice-Reduction-Aided MIMO Detection

Tian¹,

Qiao²

2016

World Congress on Electrical Engineering and Computer Systems and Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…4, where the effect of lattice reduction on orthogonality defect is clearly apparent. Lattice basis reduction has also been shown to improve matrix conditioning [12]. It is this improvement that reduces noise enhancement in linear detection methods and reduces the error rate of LRAD-based systems.…”

Section: Design and Architectures For Digital Signal Processingmentioning

confidence: 99%

A Digital Signal Processing Architecture for Soft-Output MIMO Lattice Reduction Aided Detection

Murray¹,

Weller²

2013

Design and Architectures for Digital Signal Processing

View full text Add to dashboard Cite

An enhanced Jacobi method for lattice-reduction-aided MIMO detection

Tian

Qiao

2013

2013 IEEE China Summit and International Conference on Signal and Information Processing

Self Cite

View full text Add to dashboard Cite

Lattice reduction aided decoding has been successfully used for signal detection in multiinput and multioutput (MIMO) systems and many other wireless communication applications. In this paper, we propose a novel enhanced Jacobi (short as EJacobi) method for lattice basis reduction. To assess the performance of the new EJacobi method, we compared it with the LLL algorithm, a widely used algorithm in wireless communications. Our experimental results show that the EJacobi method is more efficient and produces better results measured by both orthogonality defect and condition number than the LLL algorithm.

show abstract

Conditioning properties of the LLL algorithm

Cited by 4 publications

References 7 publications

A Hybrid Method for Lattice-Reduction-Aided MIMO Detection

A Hybrid Method for Lattice-Reduction-Aided MIMO Detection

A Digital Signal Processing Architecture for Soft-Output MIMO Lattice Reduction Aided Detection

An enhanced Jacobi method for lattice-reduction-aided MIMO detection

Contact Info

Product

Resources

About