Linearly Constrained Constant Modulus Algorithm Research Articles

Fuzzy step size least mean square algorithm tuned linearly constrained constant modulus algorithm for multi‐carrier code division multiple access blind multiuser detector

ABSTRACTAdaptive fuzzy logic step size least mean square algorithm (FLCLMS) tuning unit is designed to adjust the step size of the linear constrained constant modulus algorithm for blind multiuser detection (MUD) in multi‐carrier code division multiple access (MC‐CDMA) systems is presented. In wireless communications system, the conventional constant modulus algorithm for blind MUD has some disadvantages such as slow convergence speed and phase rotation. To overcome these shortages, a modified constant modulus algorithm was developed for blind multiuser detector. On the basis of the deterministic behavior of the FLCLMS algorithm, it has also proposed simplification over the cost function calculation, obtaining more efficient algorithm and creating new perspective for the MUD implementation. The proposed algorithm ensures the algorithm convergence to the desired user and suppresses the multiple access interference in this cost function. Thus, the performance of the system is improved. The computational experiments show that the proposed FLCLMS not only can find optimal or closed‐to‐optimal solutions but also can obtain the low computation complexity both better and more robust results than existing algorithm reported recently in the literature. Copyright © 2013 John Wiley & Sons, Ltd.

Sample-by-sample and block-adaptive robust constant modulus-based algorithms

In this study, a robust sample-by-sample linearly constrained constant modulus algorithm (LCCMA) and a robust adaptive block-Shanno constant modulus algorithm (BSCMA) are developed. The well-established quadratic inequality constraint approach is exploited to add robustness to the developed algorithms. The LCCMA algorithm is implemented using a fast steepest descent adaptive algorithm, whereas the BSCMA algorithm is realised using a modified Newton's algorithm without the inverse of Hessian matrix estimation. The developed algorithms are exercised to cancel the multiple access interference in a loaded direct sequence code division multiple access (DS/CDMA) system. Simulations are presented in a rich multipath environment with a severe near-far effect to evaluate the robustness of the proposed DS/CDMA detectors. Finally, a comprehensive comparative analysis between the sample-by-sample and block-adaptive constant modulus-based detectors is presented. It has been demonstrated that the developed robust BSCMA detector offers rapid convergence speed and very low computational complexity, whereas the developed robust LCCMA detector engenders about 5 dB improvement in the output signal-to-interference-plus-noise ratio over the BSCMA detector.

Blind Multiuser Detection in Frequency Domain for MC-CDMA Systems Using Particle Swarm Optimization

In digital communication systems, the constant modulus algorithm (CMA) for blind multiuser detector has some disadvantage such as slow convergence speed and weight phase rotation. In this paper, we put forward a novel receiver, which combines CMA blind adaptive multiuser detection and particle swarm optimization (PSO) technique in frequency domain for multi-carrier direct sequence code division multiple access (MC DS-CDMA) systems. By this approach we present an adaptive multistage structure based on PSO technique. In this structure, the weights are obtained by PSO technique between the received signal and its estimate through a CMA. The resulting weights contain reliability information for the hard decisions made in the previous stage. The simulation results show that BER performance of the PSO-based CMA blind multiuser detector is superior to that of steepest decreasing constant modulus algorithm (SDCMA), linearly constrained constant modulus algorithm (LCCMA), CMA based multiuser detection and is worthy due to its significant detection performance to achieve desirable improvement in convergence speed and tractability.

Performance analysis of RLS linearly constrained constant modulus algorithm for multiuser detection

The linearly constrained constant modulus algorithm (LCCMA) is a blind multiuser detector (MUD) solution to multiple access interference (MAI) suppression that is widely investigated in direct-sequence code division (DS-CDMA) systems. However, the conventional CMA based on the stochastic gradient descent (SGD) has slow convergence speed. Our research introduces an approximation of recursive least square (RLS) into LCCMA for better convergence speed in DS-CDMA system and quantifies the performance of blind adaptive filter based on RLS-LCCMA in both a static and a time-varying channel. In this investigation, we derive the expressions for the excess mean-square error (EMSE) of the MUD with a framework called feedback approach, and further obtain a relationship between the step size of SGD-LCCMA and the forgetting factor of RLS-LCCMA. Eventually, simulation results show the advantage of RLS-LCCMA and verify the performance analysis of the algorithm.

A Multistage Hybrid Constant Modulus Array With Constrained Adaptation for Correlated Sources

The multistage constant modulus (CM) array was previously proposed for capturing multiple received signals in a cochannel signal environment. It consists of a cascade of individual CM array stages combined with adaptive signal cancelers that remove the various signals captured across the stages. However, when the received signals are mutually correlated, the signals captured by the CM array stages are not completely canceled, and previous parallel extensions of the system do not guarantee that different signals will be captured across the stages. In this paper, we present a hybrid implementation of the multistage CM array for separating correlated signals where the canceler weights in the cascade structure provide estimates of the direction vectors of the captured signals. These estimates are then used in a parallel implementation of the linearly constrained CM (LCCM) array leading to the hybrid structure. Since the direction vectors are obtained directly from the canceler weights, the hybrid implementation does not require prior knowledge of the array response matrix and is independent of the type of antennas used in the receiver. The effect of a bias in the direction vector estimates for closely-spaced signals is analyzed, and the steady-state performance of the hybrid structure is compared to that of a conventional constrained implementation for correlated sources. Computer simulations for example cochannel scenarios are provided to illustrate various properties of the system. Mean-square-error (MSE) learning curves indicate that the proposed hybrid LCCM algorithm converges faster and has lower MSE than previous implementations

Performance analysis of the linearly constrained constant modulus algorithm-based multiuser detector

This paper quantifies the adaptive performance of a blind adaptive multiuser detector (MUD) based on the linearly constrained constant modulus algorithm (LCCMA) in both a stationary and nonstationary channel. A framework is developed to apply the feedback analysis method to analyzing adaptive MUD schemes. A closed-form expression for the excess mean square error (EMSE) of LCCMA blind adaptive MUD in a CDMA communications system is derived for both of the steady-state and tracking cases. The effects of additive noise and multiple access interference are considered. A transient analysis is performed that predicts the learning curve of the adaptive filter. Computer simulation is used to verify the accuracy of the analysis.

On the Convexity of the LCCM Cost Function for DS-CDMA Blind Multiuser Detection

The linearly constrained constant modulus algorithm (LCCMA) applied to blind DS-CDMA multiuser detection was widely considered in the last few years. In some papers, global convergence of the linearly constrained constant modulus (LCCM) algorithm was incorrectly claimed, after which corrections were published by other authors (see the Introduction section for references). In this letter, analysis of the LCCM criterion is extended to prove convexity of the LCCM cost function between any point and the global minimum, subject to certain conditions, by using the definition of a continuous convex function.

A Non-Canonical Linearly Constrained Constant Modulus Algorithm for a Blind Multiuser Detector

We investigate an alternative blind adaptive multiuser detection scheme based on a non-canonical linearly constrained constant modulus (LCCM) criterion and prove that, under the constrained condition, the non-canonical linearly constrained constant modulus algorithm (LCCMA) can completely remove multiple-access interference. We further demonstrate that the non-canonical LCCM criterion function is strictly convex in the noise-free state, and that under the constrained condition, it is also strictly convex even where small noise is present. We present a simple method for selecting the constant as well as a stochastic gradient algorithm for implementing our scheme. Numerical simulation results verify the scheme's efficiency.

Blind adaptive detection using differential CMA for CDMA systems

AbstractIn this paper, we consider the application of adaptive linear filters adjusted by stochastic gradient algorithms to the suppression of multiple‐access interference in DS/CDMA communications. It is pointed out that the linearly constrained constant modulus algorithm (LCCMA) cannot converge to the optimum point if the desired user magnitude is less than a critical value. Next, we propose to use the linearly constrained differential constant modulus algorithm (LCDCMA), and show that the LCDCMA always converges to the optimum point regardless of the desired user magnitude. Several simulation results show the following three points. First, the LCDCMA provides better performance than existing algorithms because the variance of the weight vector adjusted by the LCDCMA is less than that by existing algorithms. Second, the LCDCMA is relatively insensitive to the estimation error of the desired signal vector. Third, the LCDCMA combined with a blind channel estimation algorithm is useful. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 85(1): 1–13, 2002

A robust algorithm for untrained adaptive signal processing

A new adaptive signal processing technique is described that does not require a training or reference signal for adaptation. The approach taken is to minimize variations in the complex envelope of an adaptive processor output while subjecting the processor coefficients to a set of linear constraints. This Linearly-Constrained Constant-Modulus (LCCM) method is motivated by the drawbacks associated with two existing untrained algorithms in certain signal environments. The problems specifically addressed by LCCM are the signal cancellation problem of Linearly-Constrained Power Minimization (LCPM) and the signal ambiguity problem of the Constant-Modulus Algorithm (CMA). A complete adaptive implementation of the LCCM algorithm is presented including its stability and convergence properties. In addition, adaptive performance of LCCM is demonstrated in a comparison with CMA and LCPM.