Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel estimation. It is well known that the convergence speed of ISS-LMS is fixed by the initial step-size. In the channel estimation scenarios, it is very hard to make tradeoff between convergenc...

The recently proposed Chen’s LMS algorithm [1] costs only half multiplications that of the conventional direct-form LMS algorithm (DLMS). Despite of the merit, the algorithm lacked rigorous theoretical analysis. This work intends to characterize its properties and conditions for mean and mean-square convergences. Closed-form MSE are derived, which is slightly larger than that of DLMS algorithm....

In this paper, novel pipelined architectures for the implementation of the frequency domain linear equalizer are presented. The Frequency Domain (FD) LMS algorithm is utilized for the adaptation of equalizer coefficients. The pipelining of the FD LMS linear equalizer is achieved by introducing an amount of time delay into the original adaptive scheme, and following proper delay retiming. Simula...

In this paper, two popular adaptive equalization methods, fractionally spaced decision feedback equalization (FSDFE) and maximum-likelihood sequence estimation (MLSE), are investigated for the design of digital mobile receivers for the IS-54 specifications of the NADC* system. A bi-directional equalization technique is incorporated and a multiple training LMS (MT-LMS) algorithm is used as the a...

We have shown that duct modeling using the generalized RBF neural network (DM RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM RBF), at first the duct is identified using a generalized RBF network, after that N stage of time delay of the input signal to th...

This work presents a new variation of the commonly used Least Mean Squares Algorithm (LMS) for the identification of sparse signals with an a-priori known sparsity using a hard threshold operator in every iteration. It examines some useful properties of the algorithm and compares it with the traditional LMS and other sparsity aware variations of the same algorithm. It goes on to examine the app...

This paper presents an online system identification method with `1-norm regularization. Convergence analysis is performed for non-stationary environments. This work is a significant extension of the non-negative LMS in both aspects of algorithm derivation and convergence analysis. According to its performance and computational cost, the proposed algorithm performs similarly as the LMS algorithm...

Track induced self-excited vibration is commonly encountered in EMS (electromagnetic suspension) maglev systems, and a solution to this problem is important in enabling the commercial widespread implementation of maglev systems. Here, the coupled model of the steel track and the magnetic levitation system is developed, and its stability is investigated using the Nyquist criterion. The harmonic ...

The LMS (Least-Mean-Square) algorithm, which can obtain the complex transfer function in real-time, allows the changes of noise characteristics and the environment of system, thereby maintaining the convergence performance. Therefore, many algorithms based on the LMS algorithm have been modified to solve for some practical considerations, and the FXLMS (Filtered-X LMS) algorithm has been widely...