Convergence analysis of blind equalization algorithms using constellation-matching

Two modified blind equalization algorithms are analyzed for performance. These algorithms add a constellation-matched error term to the cost functions of the generalized Sato and multimodulus algorithms. The dynamic convergence behavior and steady-state performance of these algorithms, and of a related version of the constant modulus algorithm, are characterized. The analysis establishes the improved performance of the proposed algorithms

SGD Frequency-Domain Space-Frequency Semiblind Multiuser Receiver with an Adaptive Optimal Mixing Parameter

A novel stochastic gradient descent frequency-domain (FD) space-frequency (SF) semiblind multiuser receiver with an adaptive optimal mixing parameter is proposed to improve performance of FD semiblind multiuser receivers with a fixed mixing parameters and reduces computational complexity of suboptimal FD semiblind multiuser receivers in SFBC downlink MIMO MC-CDMA systems where various numbers of users exist. The receiver exploits an adaptive mixing parameter to mix information ratio between the training-based mode and the blind-based mode. Analytical results prove that the optimal mixing parameter value relies on power and number of active loaded users existing in the system. Computer simulation results show that when the mixing parameter is adapted closely to the optimal mixing parameter value, the performance of the receiver outperforms existing FD SF adaptive step-size (AS) LMS semiblind based with a fixed mixing parameter and conventional FD SF AS-LMS training-based multiuser receivers in the MSE, SER and signal to interference plus noise ratio in both static and dynamic environments

Angular CMA: A modified Constant Modulus Algorithm providing steering angle updates

Conventional blind beamforming algorithms have no direct notion of the physical Direction of Arrival angle of an impinging signal. These blind adaptive algorithms operate by adjusting the complex steering vector in the case of changing signal conditions and directions. This paper presents Angular CMA, a blind beamforming method that calculates steering angle updates (instead of weight vector updates) to keep track of the desired signal. Angular CMA and its respective steering angle updates are particularly useful in the context of mixed-signal hierarchical arrays as means to find and distribute steering parameters. Simulations of Angular CMA show promising convergence behaviour, while having a lower complexity than alternative methods (e.g., MUSIC)

Blind equalization based on pdf distance criteria and performance analysis

In this report, we address M-QAM blind equalization by fitting the probability density functions (pdf) of the equalizer output with the constellation symbols. We propose two new cost functions, based on kernel pdf approximation, which force the pdf at the equalizer output to match the known constellation pdf. The kernel bandwidth of a Parzen estimator is updated during iterations to improve the convergence speed and to decrease the residual error of the algorithms. Unlike related existing techniques, the new algorithms measure the distance error between observed and assumed pdfs for the real and imaginary parts of the equalizer output separately. The advantage of proceeding this way is that the distributions show less modes, which facilitates equalizer convergence, while as for multi-modulus methods phase recovery keeps being preserved. The proposed approaches outperform CMA and classical pdf fitting methods in terms of convergence speed and residual error. We also analyse the convergence properties of the most efficient proposed equalizer via the ordinary differential equation (ODE) method