Blind Adaptive Constrained Reduced-Rank Parameter Estimation Based on Constant Modulus Design for CDMA Interference Suppression

Abstract

This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems.

An Improved Interference Suppression Algorithm Based on Regular WPT in the DSSS Satellite Communication System

“…It is of great significance to employ signal processing techniques to improve the anti-jamming performance of DSSS system. Currently, two classes of interference suppression schemes have been extensively used: time domain processing techniques [1,2] and transform domain processing structures [3,4]. Time domain processing techniques can eliminate the narrowband interference completely because it estimates the interference exactly and is minused from the received signal and the interference free DSSS signal is left.…”

New rank detection methods for reduced-rank MIMO systems

“…When the channel matrix has reduced rank, the number of its entries is larger than its real dimension, and thus designs based on full-rank channels become inefficient. This motivates the research on reduced-rank technologies for MIMO systems [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

“…In [6][7][8][9][10][11][12][13][14], various reduced-rank filtering technologies were proposed, where a reduced-rank transformation is first used on the observed signal vector to obtain a lowerdimension vector, then a filter is designed to estimate the desired signal vector. The reduced-rank transformation can lower the order of the filter and thus require less computation complexity and shorter training length.…”

“…To overcome this drawback, a reduced-rank multistage Wiener filter design was proposed in [6], which relaxes the requirement on explicit estimate of the signal subspace. It was later extended to its adaptive versions in [7,8] and applied to MIMO equalization in [9]. Another related design is the auxiliary-vector filtering algorithm [10].…”

A class of constant modulus algorithms for uniform linear arrays with a conjugate symmetric constraint

“…A large stepsize leads to a fast convergence speed, but also with a loss of the output signal-to-interference-plusnoise ratio (SINR) and even causes the algorithm unstable. Many variations have been proposed to achieve a faster convergence speed, such as the well-known least squares CMA (LSCMA) [6,7], the one based on recursive least squares (RLS) [8][9][10] and those with variable stepsizes [11][12][13]. Given some additional information about the signal, such as the direction of arrival (DOA) angle of the desired signal, linear constraints can be imposed, leading to an improved performance [9,10,12,14].…”