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- W2885433354 abstract "Least-Mean-Square (LMS) is the most popular adaptive filtering algorithm, due to its numerical stability, satisfactory steady-state error and relatively low computational complexity. LMS algorithm is commonly employed in a multitude of tasks such as channel equalization, adaptive noise cancellation, system identification and frequency tracking. Owing to its computational complexity, LMS algorithm is commonly implemented in hardware, in which, due to speed and energy constraints, a fixed-point arithmetic is usually adopted. As shown in the literature, the effects due to fixed-point arithmetic alter the behavior of the algorithm, causing a significant increase in the steady-state error, due to the so-called stall phenomenon. In this paper we propose a novel fixed-point scheme which significantly limits the stalling. This gives increased flexibility to the designer in the choice of the step-size, that controls the tradeoff between convergence rate and steady-state-error. Implementation results in a 40nm CMOS technology are presented in the paper, to assess the impact of the proposed scheme in terms of area, speed and power." @default.
- W2885433354 created "2018-08-22" @default.
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- W2885433354 date "2018-07-01" @default.
- W2885433354 modified "2023-09-27" @default.
- W2885433354 title "Stall-Aware Fixed-Point Implementation of LMS Filters" @default.
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- W2885433354 doi "https://doi.org/10.1109/prime.2018.8430376" @default.
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