Low-complexity inverse square root approximation for baseband matrix operations

Perttu Salmela, Adrian Burian, Tuomas Järvinen, Aki Happonen, Jarmo Takala

    Research output: Contribution to journalArticleScientificpeer-review

    3 Citations (Scopus)
    61 Downloads (Pure)

    Abstract

    Baseband functions like channel estimation and symbol detection of sophisticated telecommunications systems require matrix operations, which apply highly nonlinear operations like division or square root. In this paper, a scalable low-complexity approximation method of the inverse square root is developed and applied in Cholesky and QR decompositions. Computation is derived by exploiting the binary representation of the fixedpoint numbers and by substituting the highly nonlinear inverse square root operation with a more implementation appropriate function. Low complexity is obtained since the proposed method does not use large multipliers or look-up tables (LUT). Due to the scalability, the approximation accuracy can be adjusted according to the targeted application. The method is applied also as an accelerating unit of an application-specific instruction-set processor (ASIP) and as a software routine of a conventional DSP. As a result, the method can accelerate any fixed-point system where cost-efficiency and low power consumption are of high importance, and coarse approximation of inverse square root operation is required.
    Translated title of the contributionLow-complexity inverse square root approximation for baseband matrix operations
    Original languageEnglish
    Pages (from-to)1-8
    Number of pages8
    JournalISRN Signal Processing
    Volume2011
    Issue number615934
    DOIs
    Publication statusPublished - 2011
    Publication typeA1 Journal article-refereed

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