This paper proposes a new leaky least mean square (leaky LMS, LLMS) algorithm in which a norm penalty is introduced to force the solution to be sparse in the application of system identification. The leaky LMS algorithm is derived because the performance ofthe standard LMS algorithm deteriorates when the input is highly correlated. However, both ofthem do not take the sparsity information into ...

In this brief, we develop an least mean square (LMS) algorithm that converge in a statistical sense to the global minimum of the mean square error (MSE) objective function. This is accomplished by estimating the gradient as a smoothed version of the MSE. The smoothed MSE objective begins as a convex functional in the mean. The amount of dispersion or smoothing is reduced, such that over time it...

A new adaptive step size adjustment least mean square (LMS) algorithm is presented in this paper. The proposed algorithm modified the existing LMS using the estimated output error as an important component for the modification of the step size. Experiment results demonstrate that application of the new algorithm leads to a significant gain in SNR (signal-to-noise ratio), thus visibly reduces th...

This paper explores the potential of several popular equalization techniques and proposes new approaches to overcome their disadvantages. Such as the conventional least-mean-square (LMS ) algorithm, the recursive least-squares ( RLS ) algorithm, the filtered-X LMS algorithm and their development. An H 2 optimal initialization has been proposed to overcome the slow convergence problem while keep...

In this paper we analyze adaptive algorithms for a multiple-antenna Code Division Multiple Access (CDMA) mobile receiver. We study the constrained versions of the Binormalized DataReusing LMS (BNDR-LMS) and the Normalized LMS (NLMS). Their performance is compared with that of the classical Frost algorithm. Time-varying stepsequences are used to further enhance the performance of the receiver. T...

CPM signals find widespread use in wireless communication systems due to their constant modulus property and spectral efficiency. Frequency discrimination based CPM demodulation approaches require efficient instantaneous frequency tracking. Prior attempts at frequency tracking via adaptive linear prediction have invoked the use of the fixed step-size LMS algorithm. In this paper, we present an ...

This paper applies the new least mean squares (LMS) adaptive algorithm, which is circulantly weighted LMS (CLMS), in distributed networks based on incremental strategy. Thedistributed CLMS (dCLMS) algorithm is optimized with respect to approximate a priori knowledge of input autocorrelation signals from all nodes in the network. In comparison with dLMS, the dCLMS adaptive algorithm has faster c...