RLS Adaptive Algorithms Research Articles

Blind Multiuser Detection in Multirate CDMA Based on Cyclic LMS Adaptation

The problem of blind adaptive multiuser detection in multirate CDMA systems is considered. Indeed, since symbol detection in multirate CDMA systems requires periodically time-varying processing of the observables, classical LMS and RLS adaptive algorithms, which assume that the solution to be tracked is time-invariant or slowly time-varying, are not suited for blind adaptive multiuser detection in a multirate system. While a cyclic RLS algorithm has recently appeared in the literature, this paper focuses on the development of LMS-based cyclic filtering algorithms. In particular, cyclic versions of the standard LMS algorithm, of the LMS algorithm with iterate averaging and of the LMS algorithm with adaptive step-size are derived. Interestingly, the last two algorithms are shown to exhibit a convergence speed close to that of the cyclic RLS procedure, but with an order of magnitude lower computational complexity. An adaptive procedure for the automatic selection of the algorithm periodicity is also presented, which is based on a minimum mean-output-energy criterion, and that obviates the need for knowledge of the transmitted data-rates from the interfering signals. Moreover, the case of known multipath fading channels is also examined. In particular, it is shown that the proposed cyclic LMS algorithms can be used to achieve RLS-like performance also in the presence of multipath distortion. Extensive computer simulation results, along with some analytical convergence results, confirm that the proposed algorithms are effective and achieve very satisfactory performance.

A Multiple Input-Multiple Output (MIMO) Adaptive Decision Feedback Equalizer (DFE) with Cancellation for Wideband Space-Time Communications

A new receiver structure able to deliver high data rates in a multiple input-multiple output (MIMO) frequency selective wireless environment is proposed and investigated in this paper. The optimal solution for the finite-length MIMO decision feedback equalizer (DFE) with cancellation is derived and used to illustrate the potential of this architecture for space-time communications. LMS and RLS adaptive algorithms are also presented for the MIMO architecture. The convergence and performance of these adaptive algorithms is analyzed through simulation results. The proposed adaptive solutions do not require channel identification, are less computationally intensive than their optimal solution counterpart, and allow the proposed MIMO receiver to seamlessly adapt to channel changes.

Neurofuzzy Algorithm For Channel Equalization

In this paper a channel equalization method using a neurofuzzy algorithm is proposed, which is based on the SAM (Standard Additive Model) method, employed in Fuzzy Logic for functions approach by using a set of rules if-then. A comparison of the efficiency of the proposed algorithm with those existent, such as the transversal equaliser using LMS, RLS adaptation algorithms, as well as the decision feedback equalizer DFE, is made with similar conditions. The computer simulations show that proposed provides a better performance of in the reduction of the intersymbol interference (ISI) in comparison with traditional algorithms in nonlinear channels.

Variable forgetting factor nonlinear RLS algorithm in correlated mixture noise

A new nonlinear RLS adaptive algorithm with a variable forgetting factor is introduced. The autocorrelations of nonzero lags, which is insensitive to additive noise, is used to control the forgetting factor of the nonlinear RLS. This scheme provides an algorithm with good tracking capability and small model error in correlated impulsive noise.

Extended fast fixed-order RLS adaptive filters

The existing derivations of conventional fast RLS adaptive filters are intrinsically dependent on the shift structure in the input regression vectors. This structure arises when a tapped-delay line (FIR) filter is used as a modeling filter. We show, unlike what original derivations may suggest, that fast fixed-order RLS adaptive algorithms are not limited to FIR filter structures. We show that fast recursions in both explicit and array forms exist for more general data structures, such as orthonormally based models. One of the benefits of working with orthonormal bases is that fewer parameters can be used to model long impulse responses.

Variable forgetting factor RLS algorithm based on dynamic equation of gradient of mean square error

A new variable forgetting factor RLS adaptive algorithm is introduced. The control of the forgetting factor is based on the dynamic equation of the gradient of the mean square error. Compared with other algorithms, the new algorithm gives fast tracking. Has a small mean square model error, and is insensitive to SNR.

Heart Rate Signal Decomposition

This paper proposes a method for decomposing heart rate fluctuations into background, respiratory and blood pressure oriented fluctuations. A signal cancellation scheme using the adaptive RLS algorithm has been introduced for cancelling respiration and blood pressure oriented changes in the heart rate fluctuations. The computer simulation confirmed the validity of the proposed method. Then, heart rate fluctuations, instantaneous lung volume and blood pressure changes are simultaneously recorded from eight normal subjects aged 20-24 years. It was shown that after signal decomposition, the power spectrum of the heart rate showed a consistent monotonic 1/fa type pattern. The proposed method enables a clear interpretation of heart rate spectrum removing uncertain large individual variations due to the respiration and blood pressure change.

A numerically stable fast RLS algorithm for adaptive filtering and prediction based on the UD factorization

The use of UD factorization in adaptive RLS algorithms is interesting for its numeric robustness and because no square-root operations at all are involved. We describe a square root free fast RLS algorithm based on the UD factorization of the autocorrelation matrix. Numerous finite precision simulations tend to indicate that this algorithm is numerically stable. The algorithm requires /spl Oscr/(/spl Nscr/) operations, where /spl Nscr/ is the linear filter order.

RLS adaptive blind beamforming algorithm for cyclostationary signals

A new cost function, which is a modification of the cost function of Castedo and Figugiras-Vidal for the adaptive blind beamforming of cyclostationary signals, is proposed. The proposed cost function enables the well-known recursive least-squares technique to be applied. Simulations demonstrate that the resulting algorithm has a faster convergence speed than the stochastic gradient-based algorithm of Castedo and Figugiras-Vidal.

Sub-array RLS adaptive algorithm

A new sub-array asynchronous recursive least square algorithm (SARLS) is proposed. The number of elements of the smallest sub-array can be less than the number of interferers. The computational load of SARLS can be reduced to o(N) while similar array performance to that of RLS is obtained. When the sub-array size is small, SARLS will converge faster than the conventional RLS.

Fast square-root RLS adaptive filtering algorithms

In this paper two fast RLS adaptive filtering algorithms are described. Both algorithms compute the lattice coefficients and are based on the development of square-root factorizations of the autocorrelation matrix. Due to the square-root nature of the algorithms, the recursion is numerically stable. Experimental evaluations have been performed in limited precision environment, and comparison with the stabilized fast transversal filter algorithm (Slock and Kailath, 1991) has been made. Since the described algorithms require O( N) operations per sample, where N is the filter order, from a computational complexity point of view they represent a substantial advantage over the O( N 2 ) complexity of classical square-root algorithms.

A sliding-exponential window RLS adaptive filtering algorithm: Properties and applications

This paper presents a sliding-exponential window recursive least-squares (SEWRLS) adaptive filtering algorithm, which has the same fast tracking property as the sliding rectangular window RLS (SRWRLS) algorithm, but its tracking accuracy is much higher than that of SRWRLS. The initial convergence and steady-state misadjustment performances of SEWRLS, SRWRLS and exponential window RLS algorithms are analyzed. Simulation results of these algorithms are presented and compared with theoretical ones which show that the simulation results are consistent with the theoretical results. It is also illustrated that SEWRLS algorithm exhibits better numerical stability in a floating point arithmetic than a conventional SRWRLS algorithm. Applications of SEWRLS to system identification and to decision feedback equalization on digital mobile radio channels are also investigated.

A new RLS adaptive algorithm based on the variation characteristics of a room impulse response

A new RLS adaptive algorithm based on the variation characteristics of a room impulse response

A New FTF9N Stabilized Recursive Algorithm, Implementation on Finite-Precision

Long-term numerical instability is a critical problem existing in Fast RLS adaptive algorithms which has received extensive attention in recent years. This paper deals with a new numerically stable algorithm. We also present a method for implementing this algorithm on finite-precision. The reported results show that the implemented algorithm is numerically stable and keeps its performances of convergence and tracking.

Adaptive RLS algorithms under stochastic excitation — strong consistency analysis

This paper deals with the analysis of a general RLS identification algorithm with forgetting factor. Almost sure exponential convergence of the estimates to the true parametrization is established for disturbance-free systems under a persistent excitation condition of stochastic type.

New forms of Levinson and Schur algorithms

The Levinson and Schur solutions to the adaptive filtering and parameter estimation problem of recursive least squares processing are described. Unnormalized versions of a newly developed Schur RLS adaptive filter are presented. A systolic array of the Schur RL adaptive filter is devised and its performance is illustrated with a typical example. The classical Levinson and Schur algorithms drop out as special cases of the more general Levinson and Schur RLS adaptive filtering algorithms. The recently introduced split Levinson and Schur algorithms, which are obtained by exploiting the symmetry in the Toeplitz-structured extended normal equations, are reviewed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An unwindowed RLS adaptive lattice algorithm

A covariance-RLS lattice adaptive-filtering algorithm is presented. The algorithm permits nonzero initial conditions and need not 'warn' N-1 iterations in advance of the first iteration to preserve its low computational requirements. These are both improvements over previous lattice algorithms. The algorithm also has a slight computational advantage over previous solutions, rendering it more applicable to adaptive-filtering applications such as fast-starting adaptive equalizers and echo cancellers, where the initial data in the adaptive filter are not, and cannot be assumed to be, zero. >

RLS適応アルゴリズムの指数収束性

RLS適応アルゴリズムの指数収束性