Channel estimation in communication systems is one of the most important issues that can reduce the error rate of sending and receiving information as much as possible. In this regard, estimation of OFDM-based wireless channels using known sub-carriers as pilot is of particular importance in frequency domain. In this paper, channel estimation under short cyclic prefix (CP) in OFDM system is con...

Abstarct In this paper, combination of channel, receiver frequency-dependent IQ imbalance and carrier frequency offset estimation under short cyclic prefix (CP) length are considered in OFDM system. An adaptive algorithm based on the set-membership filtering (SMF) algorithm is used to compensate for these impairments. In short CP length, per-tone equalization (PTEQ) structure is used to avoid i...

Updating Optimal Coefficients and Selected Observations Affine Projection is an effective way to reduce the computational and power consumption of this algorithm in the application of adaptive filters. On the other hand, the calculation of this algorithm can be reduced by using subbands and applying the concept of filtering the Set-Membership in each subband. Considering these concepts, the fir...

Suppression of noise and artifacts is a necessary step in biomedical data processing. Adaptive filtering is known as useful method to overcome this problem. Among various contaminants, there are some situations such as electrical activities of muscles contribute to impulsive noise. This paper deals with modeling real-life muscle noise with α-stable probability distribution and adaptive filterin...

Two-dimensional (TD) adaptive filtering is a technique that can be applied to many image, and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to TD structures and the novel TD adaptive filters are established. Based on this extension, the TD variable step-size normalized least mean squares (TD-VSS-NLMS), the TD-VSS affine projection algorithms (...

Tensor-based signal processing methods are usually employed when dealing with multidimensional data and/or systems a large parameter space. In this paper, we present family of tensor-based adaptive filtering algorithms, which suitable for high-dimension system identification problems. The basic idea is to exploit decomposition-based approach, such that the global impulse response can be estimat...