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Sparse constraint Least Mean Square (LMS) is a recently proposed efficient adaptive algorithm for sparse system identification. However, its computational complexity is quite high especially when the filter length is long and convergence is slow for colored input signal. This paper extends the idea of sparse constraint into multidelay frequency adaptive filter (MDF) algorithm and proposes the sparse...
In DMT-based communication systems where full-duplex transmission is required, digital echo cancellers are employed to cancel echo by means of adaptive filters. In order to reduce the computational complexity of these cancellers, the structure of the Toeplitz matrix containing the transmitted signal is usually exploited to transform the time domain signals and perform the emulation and adaptive update...
Echo path estimation in echo canceling for teleconference system is a problem in double-talk condition. The correlation function based algorithms were defined by the authors to solve this problem. In this paper, in order to improve the convergence speed of correlation function based algorithm, we propose a new modified proportionate step-size adaptation method, and then implement it into frequency...
The decision-directed space-alternating generalized expectation-maximization (SAGE) algorithm is introduced in [1] to estimate the channel and track the channel varying for OFDM systems with transmitter diversity. However, this method is based upon a discrete Fourier transform (DFT), which will cause power leakage and result in an error floor in a multipath channel with non-sample-spaced time delays...
This paper addresses adaptive channel estimation for time-varying mobile wireless channels with nonstationary statistics. We presents a reduced complexity adaptive channel estimator based on a set membership filtering approach known as the Optimal Bounding Ellipsoid (OBE) algorithm. To exploit time and frequency domain correlation properties of the channel in an efficient low-complexity way, we allow...
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