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.