Low-complexity inverse square root approximation for baseband matrix operations (Q630824)

Summary: Baseband functions, like channel estimation and symbol detection of sophisticated telecommunication systems, require matrix operations that 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 fixed-point numbers and by substituting the highly nonlinear inverse square root operation with a function that is more appropriate for implementation. 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 the inverse square root operation is required.