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.
Low-Complexity Inverse Square Root
Approximation for Baseband Matrix Operations
