John F. Doherty

Linearly constrained direct-sequence spread-spectrum interference rejection

A constrained least-squares technique is presented that utilizes a modified optimality criterion which enhances the detection capabilities of direct sequence spread spectrum systems. The standard least-squares rejection filter adds distortion to the decision variable at the output of the despreading operation. Constraints are introduced into the least-squares solution of the rejection filter which reduce the self-noise of the filter while simultaneously retaining its interference rejection properties. Two types of constraint conditions are considered. The first is a correlation matching property for the rejection filter output. This constraint induces the filter to pass the chip sequence undistorted. The second type of constraint is energy reduction, which induces the filter to achieve minimal energy. Both constraint types are successful at increasing the output signal-to-noise ratio (SNR). Simulation results are presented which demonstrate the output SNR improvement by using the constrained rejection filter. >

Direct-sequence spread spectrum narrowband interference rejection using property restoration

A new method of rejecting narrowband interference in direct-sequence spread spectrum (DS/SS) communications is presented. A typical approach is to reject the interference using a filter with large attenuation at the interference frequencies before despreading. The interference rejection method presented incorporates vector space projection techniques to suppress the correlated interference. Several signal characteristics are formulated which lead to constraint surfaces in the vector space of possible solutions. These constraint surfaces describe interference rejection solutions which introduce minimal distortion to the spread spectrum signal and simultaneously remove the interference. The constraint surfaces essentially correspond to spread spectrum signal estimates which, after interference rejection, conform to known characteristics of the transmitted spread spectrum signal. The formulation of the surfaces relies on prior knowledge about the spread spectrum signal correlation and spectral properties.

An adaptive algorithm for stable decision-feedback filtering

A method of decision-feedback filtering that is particularly well suited for data that have undergone spectral nulling is introduced. The lack of spectral content causes a large eigenvalue spread of the data correlation matrix, with deleterious effects on the performance of conventional adaptive algorithms. An adaptive decision-feedback equalizer update algorithm that does not have these undesirable properties is presented. The algorithm calculates an iterative regularized inverse solution for the decision-feedback equalizer coefficients and applies appropriate attenuation (regularization) at frequencies lacking signal energy. This automatic estimation of the significant signal components provides an improvement over other methods that use matrix arithmetic to perform the same task. The other features of the algorithm are linear computational complexity and fast tracking capability. Simulation results that compare the algorithm to the conventional LMS and RLS algorithms are presented. >

A convex projections method for improved narrow-band interference rejection in direct-sequence spread-spectrum systems

A method is presented for enhancing the narrow-band interference rejection capability of direct-sequence spread-spectrum systems employing an adaptive notch filter. The method, based on projection onto convex sets, restores that part of the spread-spectrum signal distorted by the filter. Simulation results are presented which show the output bit-error rate (BER) improvement gained by using the signal restoration scheme.

A minimax optimization approach to sidelobe suppression filter design

A sidelobe suppression filter is designed to operate simultaneously with a matched filter. For the purpose of the design, a filter is canonically decomposed into two orthogonal components. One is the conventional matched filter, the other is the sidelobe suppression filter. The minimax optimization technique is utilized to determine the optimal filter weights to minimize the peak sidelobe level of the combined filter output. The technique is also applied to design a close-in sidelobe-free filter. The sidelobe performance of the proposed filter is compared with those of the MMSE inverse filter and the matched filter. The loss in processing gain compared with a matched filter and the filtering complexity are also examined.

A fast method for regularized adaptive filtering

Regression models are used in many areas of signal processing, e.g., spectral analysis and speech LPC, where block processing methods have typically been used to estimate the unknown coefficients. Iterative methods for adaptive estimation fall into two categories: the least-mean-square (LMS) algorithm and the recursive-least-squares (RLS) algorithm. The LMS algorithm offers low complexity and stable operation at the expense of convergence speed. The RLS algorithm offers improved convergence performance at the expense of possible stability problems. Note that low complexity is used here to denote that the computational burden is proportional to the number of adaptive coefficients. The LMS algorithm is the standard algorithm for applications in which low complexity is tantamount. The LMS algorithm produces an approximation to the minimum mean square error estimate; that is, the expected value of the output error approaches zero. The major drawback of the LMS algorithm is that its rate of convergence is dependent upon the eigenvalue spread of the input data correlation matrix, usually excluding its use in high-speed, real-time signal processing applications [ 11. However, it is widely used where the convergence speed is not a problem [ 2,3]. The convergence rate of the LMS algorithm can be increased by introducing methods that attempt to orthogonalize the input data and reduce the eigenvalue spread of the correlation matrix [ 4,5]. The convergence rate of any adaptive algorithm can be specified by either the coefficient error or the estimation error, or both. It is well known that, for the LMS algorithm, the mean square error in the output converges faster than the mean square error in the coefficients [ 6,7]. Thus, the LMS algorithm is useful for situations in which the primary function of the adaptive system is

Performance characteristics of the IS-95 standard for CDMA spread spectrum mobile communication systems

An IS-95 based mobile communication system is simulated and its performance characteristics in multipath fading environment is presented in this paper. A new software simulation methodology to implement the system using the dynamic system simulation environment, SIMULINK(R) in MATLAB(R) is also developed. The system consists of forward and reverse CDMA channel structures in the forward link (base station to mobile station) and reverse link (mobile station to base station) respectively. A three-stage measurement based multipath channel model consisting of path loss, lognormal fading and Rayleigh fading components is implemented. Channel impulse response measurements, also described as power delay profiles, of RF signals in the range of 800-900 MHz in multipath channels are used to estimate the structure of an optimum noncoherent combining RAKE receiver. This receiver shows considerable performance improvement over a four-way combining RAKE receiver.

A constrained LMS algorithm for interference rejection

A constrained least-mean-square (LMS) technique that utilizes a modified optimality criterion which enhances the detection capabilities of direct-sequence spread-spectrum systems is presented. Two transversal tapped delay lines (TDLs) are operated simultaneously, one containing the received data and the other containing the constraint data. One set of adaptive weights operates on both TDLs with the LMS algorithm as the update technique. The filter weights are updated with respect to both minimizing the mean-square output error and minimizing the constraint error. Two types of constraint conditions are considered. The first is a correlation-matching condition and the second is a minimum-filter-energy condition. Simulation results presented demonstrate the output mean-square error improvement obtained by using the constrained rejection filter.<<ETX>>

A linearly constrained fast transversal filter for direct-sequence spread spectrum interference rejection

This paper introduces an improved direct-sequence spread spectrum interference rejection filter using the fast transversal filter (FTF) adaptive algorithm. A constraint is imposed on the FTF update, resulting in a balance of interference rejection and signal fidelity at the filter output. The constraint is controlled by a single parameter.

A constrained adaptive algorithm for multiple access interference suppression in DS/CDMA communication systems

This paper presents a new adaptive algorithm for interference suppression in DS/CDMA communication systems. A constrained SGD algorithm is proposed to adaptively adjust the filter parameter by minimizing a properly chosen cost function while satisfying a set of constraints. The algorithm incorporates some a priori knowledge of the desired signal for filter adaptation instead of a training sequence. The algorithm improves the steady state performance by jointly estimating the reference parameter as well as the filter parameters. An iterative maximum likelihood absolute mean estimation algorithm is devised to estimate the reference parameter. The sequential EM algorithm is used to monotonically decrease the cost function. For each input, the algorithm updates the parameters by iterating between estimating the cost function (E step), and minimizing the cost function with respect to each unknown parameter (M step). In the M step of the filter update algorithm, a set of constraints is applied to guarantee convergence. Thus, the algorithm asymptotically converges to the constrained optimum solution. The proposed algorithm is tested by computer simulations and is shown to significantly improve the steady state performance compared to the existing adaptive algorithm.