Adaptive Constant Modulus Algorithm Research Articles

PAPR Reduction Scheme using Precoding Evolutionary Adaptation for MIMO OFDM Data Communication

An extremely attractive procedure for fast airing over a dispersive correspondence channel is Multicarrier transmission. PAPR has a badly-behaved imperative concern to be spoken in evolving multicarrier broadcast systems. In this article PAPR reduction procedure has been designated for multicarrier transmission. In this developed technique a promising for the reduction of PAPR without loss of data transmit signal and BER of MIMO OFDM presented, but computational complexity has increased as increasing number of transmitter antennas Mt. A technique is used for finding of constant modulus (CMA) signal in such a way that beam forming weights have multiply with their orthonormal data matrix. That prime solution is an evolutionary adaptive constant modulus algorithm (EA-CMA) for optimal search of least PAPR signal. The results are simulated for QPSK, 16QAM and 64QAM modulation schemes with MATLAB 2013 version. This technique is compatible with PTS (Partial transmit sequence) scheme.

Stationary points of the finite length constant modulus optimization

Existing studies of the stationary points of the constant modulus adaptive algorithm, typically, assume a channel equalization setting and several restrictive conditions on the source sequence, the communications channel and the noise environment. Often, results are presented in terms of the combined channel and equalizer impulse response parameters, the source vector and the channel convolution matrix. As such, these results are not easily applicable in other areas. Modeling after Wiener filtering theory, this paper derives new expressions for the basic equations of the constant modulus (CM) optimization method. In particular, new formulas for the index function, its gradient vector and its Hessian matrix are obtained. These formulas involve the unknown parameters and the statistics of the sample sequence only. Consequently, they serve to convert the CM minimization problem into that of solving a system of cubic equations in the parameter vector. This new formulation of the CM optimization problem leads to a characterization of the critical points that is not exclusive to channel equalization. It also makes it possible to derive new equations for the minimum value of the cost function, the conditions for perfect signal recovery, and a tighter lower bound for the approach. Computer simulation examples are also presented to illustrate our findings.

Blind equalisation technique based on an improved constant modulus adaptive algorithm

A blind equalisation technique based on an improved constant modulus adaptive (CMA) algorithm is proposed. This technique can accomplish blind equalisation and carrier phase recovery simultaneously as well as easily switch between blind and decision-directed modes. It also takes advantage of the principle of minimum disturbance to avoid the gradient noise amplification problem and achieve improved stability and robustness with low computational complexity. It can be easily switched over to a decision-directed equalisation scheme whenever the error level is reasonably low to obtain higher convergence speed and lower residual intersymbol interference as well. Computer simulation results have indicated very attractive behaviour of the proposed technique, especially with respect to various noise characteristics and time-varying channels.

Fast constant modulus adaptive algorithm

An exact block formulation of the constant modulus algorithm (CMA) is presented, in which a reduction of arithmetic complexity is achieved. Two types of fast algorithms are explained, either in time-domain or in frequencydomain. The first one is of greater interest for small block lengths and the second one, using the FFT as an intermediate step, has greater advantage for large blocks. Due to the equivalence between the original CMA formulation and this one, the convergence properties of the CMA are maintained, which is not the case in the Treichler et al. implementation in frequency domain of this algorithm. Furthermore, this approach allows the use of very small block lengths (e.g. N = 2), the reduction of the arithmetic complexity increasing with the block size.

New processing techniques based on the constant modulus adaptive algorithm

This paper presents three extensions of the constant modulus algorithm (CMA) introduced in an earlier paper as a means of correcting degradations in constant enyelope waveforms. As originally formulated, the CMA employs an FIR filter with complex coefficients and accepts complex (quadrature) input data. In this paper, first a real input, real coefficient version of the algorithm is shown to perform arbitrarily close to the fully complex version. Secondly, the algorithm is extended for the enhancement of signals having a nonconstant but known envelope, as might arise in data signals with pulse shaping. Lastly, a multichannel version of CMA, wherein several observations are linearly combined, is presented for joint adaptation of multiple filters. This approach can be used, for example, as a means of spatial or polarization "beamsteering" to reject additive interferers and compensate for channel-induced polarization rotation.