Fast communicationVariable step-size normalized LMS algorithm by approximating correlation matrix of estimation error
Introduction
The normalized least mean square (NLMS) algorithm is an adaptive filter algorithm that is simple and easy to implement. There have been several studies on improving its performance [1]. A variable step-size is one of the improvements suggested for the NLMS algorithm [2], [3].
In this letter, we propose a variable step-size NLMS algorithm and the motivation of the proposed algorithm is the state-space approach for adaptive filter algorithms [4], [5]. According to this approach, the adaptive filter algorithm can be derived by state-space equations; the NLMS algorithm is a special case of the state-space approach for adaptive filter algorithms. The relationship between the recursive least square (RLS) algorithm and the Kalman filter has been studied in [6]. We summarize the relationships and develop the proposed variable step-size NLMS algorithm by considering the relationship among the NLMS, RLS and Kalman filter algorithms.
Conventionally, most variable step-size algorithms are derived by minimizing a criterion or cost function to determine the step-size value [3], [5]. In contrast, the proposed variable step-size algorithm is derived by approximating the correlation matrix of the estimation error. It does not require a differentiation operation to minimize the criterion for step-size. Moreover, the step-size calculation of the proposed algorithm is simple, and therefore, it does not pose a serious computational burden. The convergence of the proposed algorithm is confirmed by using the relationship that the summation of the excess mean-square error and variance of the measurement noise is equal to the mean-square error [7].
This letter is organized as follows. In Section 2, we summarize the relationship among the NLMS, RLS, and Kalman filter algorithms. In Section 3, we present the proposed variable step-size algorithm and verify the convergence. In Section 4, we show computer simulation results of the proposed algorithm and compare them to the variable step-size algorithm in [3], [5] to verify the performance. In Section 5, we conclude this letter.
Section snippets
Relationship among NLMS, RLS, and Kalman filter algorithms
A state-space equation without input force is given bywhere xi, yi, wi and vi are the state, measurement, process noise, and measurement noise vectors, respectively, at time instant i [6]. We assume that all vectors are column vectors. The matrices Ai and Ci are the state transition and measurement matrices, respectively, at time instant i [6]. The Kalman filter is an algorithm to estimate the state vector when process and measurement noise exist. For i=1,2,…, the Kalman
Derivation of proposed algorithm
As stated in the previous section, the NLMS algorithm assumes Pi=I for all time instants i. This assumption is a rough approximation of Pi. For a better approximation of Pi, we set , where is a diagonal matrix whose diagonal terms are all . We try to calculate at each time instant i. Although our treatment is relatively poor compared to the RLS algorithm, it provides a better solution compared to the NLMS algorithm. Moreover, the proposed treatment has less
Simulation results
The simulation is executed under a channel estimation scenario. We randomly generate an optimal coefficient vector ho for estimation. For the input signals, we use filtered zero-mean white Gaussian random signals through the AR modelThe signal-to-noise ratio (SNR) was defined as 10 log10 (E[y2(i)]/E[v2(i)]), and we set the SNR for the simulations to 30 dB. To evaluate the performance, the mean square deviation (MSD) defined as is calculated by averaging over 100
Conclusion
In this letter, we propose a variable step-size NLMS algorithm. The proposed algorithm is developed based on the relationship among the NLMS, RLS and Kalman filter algorithms. We suggest the correlation matrix of the estimation error to be a diagonal matrix having same diagonal terms, which is a better approximation compared to the approximation of the identity matrix adopted in the standard NLMS. We also propose an equation for determining the same diagonal terms by using an equation from the
Acknowledgments
This research was supported by the Ministry of Knowledge Economy (MKE), Korea, under the Information Technology Research Center (ITRC) support program supervised by the National IT Industry Promotion Agency (NIPA) (NIPA-2009-C1090-0902-0004).
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