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The problem of blind recovery of QAM and PSK signals for multiple-input multiple-output (MIMO) communication systems is investigated. We propose a simplified version of the well-known constant modulus algorithm (CMA), named simplified CMA (SCMA). The SCMA cost function consists in projection of the MIMO equalizer outputs on one dimension (either real or imaginary part). A study of stationary points of SCMA reveals the absence of any undesirable local stationary points, which ensures a perfect recovery of all signals and a global convergence of the algorithm. Taking advantage of the phase ambiguity in the solution of the new cost function for QAM constellations, we propose a modified cross-correlation term. It is shown that the proposed algorithm presents a lower computational complexity compared to the constant modulus algorithm (CMA) without loss in performances. Some numerical simulations are provided to illustrate the effectiveness of the proposed algorithm.

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is work is about reducing energy consumption in the receiver chain by limiting the use of the equalizer. It is to make the radio receiver aware of its environment and able to take decision to turn on or off the equalizer according to its necessity or not. When the equalizer is off, the computational complexity is reduced and the rate of reduction depends on the percentage of time during which this component is disabled. In order to achieve this scenario of adapting the use of the equalizer, we need to develop a decisionmaking technique that provides the receiver with the capacities of awareness and adaptability to the state of its environment. For this, we improve a technique based on a statistical modeling of the environment by de�ning two metrics as channel quality indicators to evaluate the effect of the intersymbol interferences and the channel fading. e statistical modeling technique allows to take into account the impact of the uncertainties of the estimated metrics on the decision making.

In this paper, we propose a new iterative algorithm for the minimization of the constant modulus (CM) criterion. It is composed of two stages: Prewhitening followed by complex Givens rotations, whence the name Givens CMA (GCMA). Initially, prewhitening reduces the channel matrix into a unitary matrix. Subsequently, this unitary matrix is computed via complex Givens rotations by minimizing the CM criterion. The proposed algorithm provides similar or even better performance than the Analytical CMA (ACMA) algorithm at reduced computational cost. Some simulation results are provided to illustrate the effectiveness of the proposed algorithm.

This paper addresses the issue of blind multiple-input multiple-output (MIMO) demodulation of communication signals, with time-varying channels and in an interception context. A new adaptive-blind source separation algorithm, which is based on the implementation of the Multimodulus cost function by analytical methods, is proposed. First a batch processing analysis is performed; then an adaptive implementation of the (Analytical Multi-Modulus Algorithm) AMMA and its simplified version named (Analytical Simplified Constant Modulus Algorithm) ASCMA is detailed. These algorithms, named adaptive-AMMA and adaptive-ASCMA, respectively, are compared with the adaptive (Analytical Constant Modulus Algorithm) ACMA and the MMA (Multi-Modulus Algorithm). The adaptive-AMMA and adaptive-ASCMA achieve a lower residual intersymbol interference and bit error rate than those of the adaptive-ACMA and MMA.

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