A Low Complexity DDS Based On Optimized CORDIC Algorithm

Ma, Shang; Wang, Xuesi; Li, Yongjie; Long, Kai; Zhu, Bixin; Lei, Xin

doi:10.1109/asicon47005.2019.8983676

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…The core idea of these methods is to optimize space complexity with computational complexity. CORDIC algorithm [7] is introduced to calculate the amplitude value in [8][9][10][11][12][13]. This method calculates trigonometric functions by angle rotation, which can avoid memory access, but the iteration period is longer.…”

Section: Introductionmentioning

confidence: 99%

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FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation

Liao,

Zhang,

et al. 2023

Applied Sciences

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We propose a direct digital frequency synthesizer (DDFS) by using an error-controlled piecewise linear (PWL) approximation method. For a given function and a preset max absolute error (MAE), this method iterates continuously from right to left within the input interval, dividing the entire interval into multiple segments. Within each segment, the least squares method is used to approximate the objective function, ensuring that each segment meets the error requirements. Based on this method, We first implemented a set of DDFS under different MAE to study the relationship between SFDR and MAE, and then evaluated its hardware overhead. In order to increase the frequency of the output signal, we implement a multi-core DDFS using time interleaving scheme. The experimental results show that our DDFS has significant advantages in SFDR, using fewer hardware resources to achieve high SFDR. Specifically, the SFDR of proposed DDFS can reach 114 dB using 399 LUTs, 66 flip flops and 3 DSPs. More importantly, we demonstrate through experiments that proposed DDFS breaks the SFDR theoretical upper bound of DDFS based on piecewise linear approximation methods.

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Section: Introductionmentioning

confidence: 99%

“…where n is the number of discrete points in the subinterval, then, the objective function is denoted by f (x) with x ∈ (j : k), 1 ≤ j ≤ k ≤ NU M, so the error vector can be expressed as Equation (8).…”

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confidence: 99%