Reduced Constellation Algorithm Research Articles

RCA 비용 함수를 개선한 Advanced CMA 등화기 알고리즘

In this paper, the concerned CMA (Constant Modulus Algorithm) adaptive equalizer convergence rate and residual inter-symbol interference using cost function in order to improved to the ACMA (Advanced CMA). The CMA method compensates amplitude but does not compensate phase. On the other hand, The RCA (Reduced Constellation Algorithm) method compensates both the amplitude and the phase but it has the convergence rate problem. MCMA method is a way to solve the phase problem of CMA method compensates both the amplitude and the phase after respectively calculating the real and imaginary components. But it is more than poor CMA method in the complexity of hardware and the compensation performance. The cost function can advantages by improving the CMA and a MCMA (Modified CMA) equalizer so that the amplitude and phase retrieval the equalization steady-state to reduce the error by using ISI and faster convergence rate and performance is good SER (Symbol Error Ratio) was confirmed by computer simulations.

A MSE optimized polynomial equalizer for 16QAM and 64QAM constellation

Recently, a Maximum Entropy (MaxEnt) algorithm and its derivation called WNEW algorithm were presented by the same author. It was shown that the MaxEnt and WNEW algorithm have improved equalization performance compared with Godard’s, reduced constellation algorithm and the sign reduced constellation algorithm. In this paper, a new equalization method is proposed for the 16QAM and 64QAM input constellation based on the WNEW algorithm which is extended with some polynomials of the equalized output and optimized with the mean square error criteria. According to simulation results, the new equalization method leads to over 15 dB advantage in the residual Intersymbol Interference compared to the results presented by Godard, 10 dB advantage compared with the WNEW algorithm and 5 dB advantage compared with the MaxEnt algorithm.

A Maximum Entropy approach for blind deconvolution

In this paper we propose a new closed approximated formed expression for the conditional expectation based on the Maximum Entropy principle and Laplace integral method. Our proposed expression does not impose any restrictions (except of even symmetric) on the probability distribution of the (unobserved) input sequence, thus it is suitable for a wider range of source probability density function compared with Bellini's, Fiori's or Haykin's expression. In addition, we propose a new, efficient and noniterative approximation for the Lagrange multipliers related to the blind deconvolution problem. Our proposed Lagrange multipliers are based on our new proposed expression for the conditional expectation and on the mean square error (MSE) criteria. Based on our new derivations, a new blind deconvolution algorithm is proposed with improved equalization performance compared with Godard's, reduced constellation algorithm (RCA), Fiori's and the sign reduced constellation algorithm (SRCA). In addition, a theoretical analysis shows that our algorithm achieves perfect equalization in the real valued and two independent quadrature carrier case. These results imply a significant improvement over Godard's algorithm in the 16 quadrature amplitude modulation (QAM) case.

The multimodulus blind equalization and its generalized algorithms

This paper presents a new blind equalization algorithm called multimodulus algorithm (MMA). This algorithm combines the benefits of the well-known reduced constellation algorithm (RCA) and constant modulus algorithm (CMA). In addition, MMA provides more flexibility than RCA and CMA, and is better suited to take advantage of the symbol statistics of certain types of signal constellations, such as nonsquare constellations, very dense constellations, and some wrong solutions.

A dynamic convergence analysis of blind equalization algorithms

Blind equalizers do not require a training sequence to start up or to restart after a communications breakdown, making them particularly useful in applications such as broadcast and point-to-multipoint networks. We study in parallel the dynamic convergence behavior of three blind equalization algorithms: the multimodulus algorithm (MMA), the constant modulus algorithm (CMA), and the reduced constellation algorithm (RCA). Using a conditional Gaussian approximation, we first derive the theoretical mean-squared-error (MSE) trajectory for MMA. This analysis leads to accurate but somewhat cumbersome expressions. Alternatively, we apply a Taylor series approximation to derive MSE trajectories for MMA, CMA, and RCA. This approach yields simpler but somewhat less accurate expressions. For the steady-state operation, however we derive even simpler formulas that accurately predict the asymptotic MSE values. We finally study the convergence rates of the three blind algorithms using their theoretical MSE trajectories, computer simulations, and a laboratory experiment.

A blind equalization with the sign algorithm for broadband access

This paper presents an optimal reduced constellation point of sign reduced constellation algorithm (SRCA) for square and nonsquare carrierless amplitude and phase (CAP)/QAM signal constellations. Convergence characteristics of the SRCA algorithm are analyzed and compared to those of the RCA algorithm.