The Linearly Constrained LSCMA for Blind Multi-user Detection

Abstract

The least square constant modulus algorithm (LSCMA) is a popular constant modulus algorithm (CMA) because of its global convergence and stability. But the performance will degrade when it is affected by the problem of interference capture in the MC-CDMA system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA multiuser detection algorithm is proposed by using the spreading code of the desired user to impose linear constraint on the LSCMA. The proposed algorithm ensures the algorithm convergence to the desired user. Thus the performance of the system is improved. The simulation results demonstrate that the proposed algorithm offers faster convergence rate and provides better output signal-to-interference-plus-noiseratio (SINR) and bit error rate (BER) performance compared with the traditional LSCMA.

Blind Detection, Parameters Estimation and Despreading of DS-CDMA Signals in Multirate Multiuser Cognitive Radio Systems

“…Nevertheless, they could be computationally complex since they may require significantly long observation time. Several blind approaches (i.e., when the process of recovering data from multiple simultaneously transmitting users without access to any training sequences) have been addressed in the literature (Buzzi et al, 2010;Khodadad, Ganji & Mohammad, 2010;Khodadad, Ganji & Safaei, 2010;Meng et al, 2010;Yu et al, 2011;Zhang et al, 2011). However, most of them require some prior knowledge about users parameters such as signature waveform, processing gain, pseudo-noise code (for a particular group of active users) or chip rate.…”

Advances in Cognitive Radio Systems

“…Nevertheless, they could be computationally complex since they may require significantly long observation time. Several blind approaches (i.e., when the process of recovering data from multiple simultaneously transmitting users without access to any training sequences) have been addressed in the literature (Buzzi et al, 2010;Khodadad, Ganji & Mohammad, 2010;Khodadad, Ganji & Safaei, 2010;Meng et al, 2010;Yu et al, 2011;. However, most of them require some prior knowledge about users parameters such as signature waveform, processing gain, pseudo-noise code (for a particular group of active users) or chip rate.…”