Low-Complexity DCD-Based Sparse Recovery Algorithms
نویسندگان
چکیده
منابع مشابه
Low Complexity and High speed in Leading DCD ERLS Algorithm
Adaptive algorithms lead to adjust the system coefficients based on the measured data. This paper presents a dichotomous coordinate descent method to reduce the computational complexity and to improve the tracking ability based on the variable forgetting factor when there are a lot of changes in the system. Vedic mathematics is used to implement the multiplier and the divider in the VFF equatio...
متن کاملLow-complexity widely linear RLS filter using DCD iterations
Resumo— Recentemente, filtros adaptativos amplamente lineares estão sendo usados para acessar completamente estatı́sticas de segunda ordem de sinais impróprios, com o objetivo de melhorar a estimação. Essa caracterı́stica torna esses filtros vantajosos em relação aos seus equivalentes estritamente lineares, apesar de apresentarem maior complexidade computacional. Nesse sentido, com o intuito de r...
متن کاملPULSE: Peeling-based Ultra-Low complexity algorithms for Sparse signal Estimation
PULSE: Peeling-based Ultra-Low complexity algorithms for Sparse signal Estimation by Sameer Anandrao Pawar Doctor of Philosophy in Engneering-Electrical Engineering and Computer Science University of California, Berkeley Professor Kannan Ramchandran, Chair An emerging challenge in recent years for engineers, researchers, and data scientists across the globe is to acquire, store, and analyze eve...
متن کاملUnderwater acoustic channel estimation based on sparse recovery algorithms
The authors consider underwater acoustic (UWA) channel estimation based on sparse recovery using the recently developed homotopy algorithm. The UWA communication system under consideration employs orthogonal frequencydivision multiplexing (OFDM) and receiver preprocessing to compensate for the Doppler effect before channel estimation. The authors first extend the original homotopy algorithm whi...
متن کاملComparison of threshold-based algorithms for sparse signal recovery
Intensively growing approach in signal processing and acquisition, the Compressive Sensing approach, allows sparse signals to be recovered from small number of randomly acquired signal coefficients. This paper analyses some of the commonly used threshold-based algorithms for sparse signal reconstruction. Signals satisfy the conditions required by the Compressive Sensing theory. The Orthogonal M...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2017
ISSN: 2169-3536
DOI: 10.1109/access.2017.2715882