3:30, SPCOM-L2.1
CONSTANT MODULUS PERFORMANCE SEARCH USING NEWTON'S METHOD
T. OGUNFUNMI, H. JAMALI
This paper uses Newton's method to seek the global minimum of the constant modulus performance measure. Unlike the common practice of using the constant modulus adaptive algorithm, the new approach does not suffer from local minima. The paper also discusses some implementation issues of the new algorithm.

3:50, SPCOM-L2.2
BLIND EQUALIZATION OF CONSTANT MODULUS SIGNALS VIA RESTRICTED CONVEX OPTIMIZATION
B. MARICIC, Z. LUO, T. DAVIDSON
In this paper, we formulate the blind equalization of Constant Modulus (CM) signals as a convex optimization problem. This is done by performing an algebraic transformation on the direct formulation of the equalization problem and then restricting the set of design variables to a subset of the original feasible set. In particular, we express the blind equalization problem as a linear objective function subject to some linear and semidefiniteness constraints. Such Semidefinite Programs (SDPs) can be efficiently solved using interior point methods. Simulations indicate that our method performs better than the standard methods, whilst requiring significantly fewer data samples.

4:10, SPCOM-L2.3
BOUNDS FOR THE MIXING PARAMETER WITHIN THE CC-CMA ALGORITHM APPLIED IN NON IDEAL MULTIUSER ENVIRONMENT
Y. LUO, J. CHAMBERS
We derive new bounds for the mixing parameter within the cross-correlation constant modulus algirithm (CC-CMA) for blind source separation and equalization in non-ideal multiuser environments. Channel undermodelling and noise are considered when the complex sources are circularly symmetric. These tighter bounds are obtained by surface topography of the error performance surface of the CC-CMA algorithm, and replace earlier work which suggested that the mixing parameter should be greater than 4/3. The validity of the bounds is confirmed by simulation studies.

4:30, SPCOM-L2.4
FIXED POINT ANALYSIS OF THE CONSTANT MODULUS ALGORITHM
N. YOUSEF, A. SAYED
The steady-state performance of adaptive equalizers can significantly vary when they are implemented in finite precision arithmetic, which makes it vital to analyze their performance in a quantized environment. In this paper we present a fixed point analysis for the steady-state mean square error (MSE) of a blind adaptive equalizer and the optimal value of the step-size that minimizes this MSE. Such expressions are useful for selecting the adequate wordlength of a blind equalizer to achieve a specific desired steady-state performance.

4:50, SPCOM-L2.5
NORMALISED CONSTANT MODULUS ALGORITHM WITH SELECTIVE PARTIAL UPDATES
K. DOGANCAY, O. TANRIKULU
A reduced complexity realisation for the normalised constant modulus algorithm (NCMA) and its soft criterion satisfaction (SCS) version is proposed based on selective partial updating. The computational complexity of NCMA and SCS is reduced by updating a block of equaliser parameters at every iteration rather than the entire equaliser. This results in a smaller number of multiplications for updating the equaliser parameters. A simple block selection criterion is derived from the solution of a constrained minimisation problem that underpins the development of NCMA. In fractionally-spaced equalisation, the proposed selective partial updating is shown to be capable of maintaining comparable convergence speed to its full-update counterpart. This implies a significant reduction in implementation cost without necessarily penalising the convergence speed.

5:10, SPCOM-L2.6
A PENALTY FUNCTION APPROACH TO CODE-CONSTRAINED CMA FOR BLIND MULTIUSER CDMA SIGNAL DETECTION
J. MA, J. TUGNAIT
A code-constrained constant-modulus approach (CMA) was presented recently in Li and Tugnait [4] for blind detection of asynchronous short-code DS-CDMA signals in multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. The equalizer was determined by minimizing the Godard/CMA cost function of the equalizer output with respect to the equalizer coefficients subject to the fact that the equalizer lies in a subspace associated with the desired user's code sequence. An iterative projection approach was used in [4] for constrained optimization where at each iteration the equalizer was projected onto the desired subspace. In this paper we investigate an alternative, penalty function-based approach to constrained optimization. Global minima and some of the local minima of the cost function are investigated. A simulation example is presented.