Baseband functions like channel estimation and symbol detection of sophisticated telecommunications systems require matrix\r\noperations, which apply highly nonlinear operations like division or square root. In this paper, a scalable low-complexity\r\napproximation method of the inverse square root is developed and applied in Cholesky and QR decompositions. Computation is\r\nderived by exploiting the binary representation of the fixedpoint numbers and by substituting the highly nonlinear inverse square\r\nroot operation with a more implementation appropriate function. Low complexity is obtained since the proposed method does not\r\nuse large multipliers or look-up tables (LUT). Due to the scalability, the approximation accuracy can be adjusted according to the\r\ntargeted application. The method is applied also as an accelerating unit of an application-specific instruction-set processor (ASIP)\r\nand as a software routine of a conventional DSP. As a result, the method can accelerate any fixed-point system where cost-efficiency\r\nand low power consumption are of high importance, and coarse approximation of inverse square root operation is required.
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