lms algorithm

In this paper, we investigate the multiple-input multiple-output systems (MIMO) and the co-channel interference cancelation in these systems. In recent years, the usages of array antennas in the receiver/ transmitter or both have greatly increased in telecommunication systems. Due to the increasing transmission data rate and the decreasing error rate, the MIMO systems in wireless communication systems are very important. In the third, fourth, and fifth generations of wireless communications in order to increase the network capacity and spectrum efficiency, the same frequency channel is allocated to different users. This leads to co-channel interference for the users with the same frequency channel. Thus, it is essential to eliminate inter-channel interference in telecommunication networks. In this paper, an adaptive algorithm is evaluated to eliminate the co-channel interference in MIMO systems. We also investigate the algorithm in indoor and outdoor environments using the Wiener model, with direct and indirect channels using the Rayleigh and Rice fading models and calculate the error probability in different conditions. In order to improve the performance of the system in uplink transmission, auxiliary antennas and linear arrays of antennas at the receiver are used. The results of the proposed algorithm are simulated and compared with the previous conventional methods.

In this paper, we introduce a new algorithm to deal with the stalling effect in the LMS algorithm used in adaptive filters. We modify the update rule of the tap weight vectors by adding noise, generated by a noise generator. The properties of the proposed method are investigated by two novel theorems. As it is shown, the resulting algorithm, called Added Noise LMS (AN-LMS), improves the resistance capability of the conventional LMS algorithm against the stalling effect. The probability of update with additive white Gaussian noise is calculated in the paper. Convergence of the proposed method is investigated and it is proved that the rate of convergence of the introduced method is equal to that of LMS algorithm in the expected value sense, provided that the distribution of the added noise is uniform. Finally, it is shown that the order of complexity of the proposed algorithm is linear as the conventional LMS algorithm.

In high capacity systems, as the bit transmission rate increases, Intersymbol Interference (ISI) caused by the multi-path channel reduces the system efficiency. The technique of OFDM acts very well against this phenomenon. On the other hand, an accurate estimation of the communication channel coefficients improves the performance of communication systems effectively. In this paper, the estimation algorithms of the MIMO-OFDM channel are investigated to compare them in terms of estimation error and calculation complexity. In the following, a method is proposed to improve the estimation of the MIMO-OFDM channel. In this method, combining the LS algorithm with adaptive algorithms improves the estimation performance at different Doppler frequencies. We observe in the simulations that the use of adaptive algorithms improves the estimation of the channel at Doppler frequencies.

The least mean square (LMS) adaptive algorithm is widely used in acoustic noise cancellation (ANC) scenario. In a noise cancellation scenario, speech signals usually have high amplitude and sudden variations that are modeled by impulsive noises. When the additive noise process is nonGaussian or impulsive, LMS algorithm has a very poor performance. On the other hand, it is well-known that the acoustic channels usually have sparse impulse responses. When the impulse response of system changes from a non-sparse to a highly sparse one, conventional algorithms like the LMS based adaptive filters can not make use of the priori knowledge of system sparsity and thus, fail to improve their performance both in terms of transient and steady state. Impulsive noise and sparsity are two important features in the ANC scenario that have paid special attention, recently. Due to the poor performance of the LMS algorithm in the presence of impulsive noise and sparse systems, this paper presents a novel adaptive algorithm that can overcomes these two features. In order to eliminate impulsive disturbances from speech signal, the information theoretic criterion, that is named correntropy, is used in the proposed cost function and the zero norm is also employed to deal with the sparsity feature of the acoustic channel impulse response. Simulation results indicate the superiority of the proposed algorithm in presence of impulsive noise along with sparse acoustic channel.

In this paper, we study the impact of low-quality node on the performance of incremental least mean square (ILMS) adaptive networks. Adaptive networks involve many nodes with adaptation and learning capabilities. Low-quality mode in the performance of a node in a practical sensor network is modeled by the observation of pure noise (its observation noise) that leads to an unreliable measurement. Specifically, we consider ILMS networks with different number of low-quality nodes and compare their performance in two different cases including (i) ideal and (ii) noisy links in homogeneous and inhomogeneous environments. We show that in the case of ideal links among nodes, one node with low-quality mode degrades the estimation performance significantly and increasing the variance of observation noise does not degrade the performance anymore. Even with increasing node numbers with low-quality mode in network, estimation performance does not divergence. On the other hand, in the presence of noisy links, different behavior is observed and degradation is dependent on variance of noisy links and it may go unstable. Simulation results are provided to illustrate the discussions.

This paper presents a simple and easy implementable Least Mean Square (LMS) type approach for frequency estimation of three phase power system in an unbalanced condition. The proposed LMS type algorithm is based on a second order recursion for the complex voltage derived from Clarke''s transformation which is proved in the paper. The proposed algorithm is real adaptive filter with real parameter (not complex) which can be efficiently implemented by DSP. In unbalanced situations, simulation experiments show the advantages and drawbacks of the proposed algorithm in comparison to Complex LMS (CLMS) and Augmented Complex LMS (ACLMS) methods.