Research Article  Open Access
An Improved Proportionate Normalized LeastMeanSquare Algorithm for Broadband Multipath Channel Estimation
Abstract
To make use of the sparsity property of broadband multipath wireless communication channels, we mathematically propose an normconstrained proportionate normalized leastmeansquare (LPPNLMS) sparse channel estimation algorithm. A general norm is weighted by the gain matrix and is incorporated into the cost function of the proportionate normalized leastmeansquare (PNLMS) algorithm. This integration is equivalent to adding a zero attractor to the iterations, by which the convergence speed and steadystate performance of the inactive taps are significantly improved. Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMSbased algorithm for sparse channel estimation applications.
1. Introduction
Broadband signal transmission is becoming a commonly used highdatarate technique for nextgeneration wireless communication systems, such as 3 GPP longterm evolution (LTE) and worldwide interoperability for microwave access (WiMAX) [1]. The transmission performance of coherent detection for such broadband communication systems strongly depends on the quality of channel estimation [2–5]. Fortunately, broadband multipath channels can be accurately estimated using adaptive filter techniques [6–10] such as the normalized leastmeansquare (NLMS) algorithm, which has low complexity and can be easily implemented at the receiver. On the other hand, channel measurements have shown that broadband wireless multipath channels can often be described by only a small number of propagation paths with long delays [4, 11, 12]. Thus, a broadband multipath channel can be regarded as a sparse channel with only a few active dominant taps, while the other inactive taps are zero or close to zero. This inherent sparsity of the channel impulse response (CIR) can be exploited to improve the quality of channel estimation. However, such classical NLMS algorithms with a uniform step size across all filter coefficients have slow convergence when estimating sparse impulse response signals such as those in broadband sparse wireless multipath channels [11]. Consequently, corresponding algorithms have recently received significant attention in the context of compressed sensing (CS) [5, 12–14] and were already considered for channel estimation prior to the CS era [5, 12]. However, these CS channel estimation algorithms are sensitive to the noise in wireless multipath channels.
Inspired by the CS theory [12–14], several zeroattracting (ZA) algorithms have been proposed and investigated by combining the CS theory and the standard leastmeansquare (LMS) algorithm for echo cancellation and system identification, which are known as the zeroattracting LMS (ZALMS) and reweighted ZALMS (RZALMS) algorithms, respectively [15]. Recently, this technique has been expanded to the NLMS algorithm and other adaptive filter algorithms to improve their convergence speed in a sparse environment [9, 16–18]. However, these approaches are mainly designed for nonproportionate adaptive algorithms. On the other hand, to utilize the advantages of the NLMS algorithm, such as stable performance and low complexity, the proportionate normalized leastmeansquare (PNLMS) algorithm has been proposed and studied to exploit the sparsity in nature [19] and has been applied to echo cancellation in telephone networks. Although the PNLMS algorithm can utilize the sparsity characteristics of a sparse signal and obtain faster convergence at the initial stage by assigning independent magnitudes to the active taps, the convergence speed is reduced by even more than that of the NLMS algorithm for the inactive taps after the active taps converge. Consequently, several algorithms have been proposed to improve the convergence speed of the PNLMS algorithm [20–27], which include the use of the norm technique and a variable step size. Although these algorithms have significantly improved the convergence speed of the PNLMS algorithm, they still converge slowly after the active taps converge. In addition, some of them are inferior to the NLMS and PNLMS algorithms in terms of the steadystate error when the sparsity decreases. From these previously proposed sparse signal estimation algorithms, we know that the ZA algorithms mainly exert a penalty on the inactive channel taps through the integration of the norm constraint into the cost function of the standard LMS algorithms to achieve better estimation performance, while the PNLMS algorithm updates each filter coefficient with an independent step size, which improves the convergence of the active taps.
Motivated by the CS theory [13, 14] and ZA technique [15–18], we propose an normconstrained PNLMS (LPPNLMS) algorithm that incorporates the norm into the cost function of the PNLMS algorithm, resulting in an improved proportionate adaptive algorithm. The difference between the proposed LPPNLMS algorithm and the ZA algorithms is that the gainmatrixweighted norm is used in our proposed LPPNLMS algorithm instead of the general norm to expand the application of ZA algorithms [15]. Also, this integration is equivalent to adding a zero attractor in the iterations of the PNLMS algorithm to obtain the benefits of both the PNLMS and ZA algorithms. Thus, our proposed LPPNLMS algorithm can achieve fast convergence at the initial stage for the active taps. After the convergence of these active taps, the ZA technique in the LPPNLMS algorithm acts as another force to attract the inactive taps to zero to arrest the slow convergence of the PNLMS algorithm. Furthermore, our proposed LPPNLMS algorithm achieves a lower mean square error than the PNLMS algorithm and its related improved algorithms, such as the improved PNLMS (IPNLMS) [20] and law PNLMS (MPNLMS) [21] algorithms. In this study, our proposed LPPNLMS algorithm is verified over a sparse multipath channel by comparison with the NLMS, PNLMS, IPNLMS, and MPNLMS algorithms. The simulation results demonstrate that the LPPNLMS algorithm achieves better channel estimation performance in terms of both convergence speed and steadystate behavior for sparse channel estimation.
The remainder of this paper is organized as follows. Section 2 briefly reviews the standard NLMS, PNLMS, and improved PNLMS algorithms, including the IPNLMS and MPNLMS algorithms. In Section 3, we describe in detail the proposed LPPNLMS algorithm, which employs the Lagrange multiplier method. In Section 4, the estimation performance of the proposed LPPNLMS algorithm is verified over sparse channels and compared with other commonly used algorithms. Finally, this paper is concluded in Section 5.
2. Related Channel Estimation Algorithms
2.1. Normalized LeastMeanSquare Algorithm
In this section, we first consider the sparse multipath communication system shown in Figure 1 to discuss the channel estimation algorithms. The input signal containing the most recent samples is transmitted over a finite impulse response (FIR) channel with channel impulse response (CIR) , where denotes the transposition operation. Then the output signal of the channel is written as follows: where is a sparse channel vector with dominant active taps whose magnitudes are larger than zero and inactive taps whose magnitudes are zero or close to zero with . To estimate the unknown sparse channel , an NLMS algorithm uses the input signal , the output signal , and the instantaneous estimation error , which is given by where is the NLMS adaptive channel estimator at instant , , and is an additive noise at the receiver. The update function of the NLMS channel estimation algorithm is expressed as where is the step size with and is a small positive constant used to avoid division by zero.
2.2. Proportionate Normalized LeastMeanSquare Algorithm
The PNLMS algorithm, which is an NLMS algorithm improved by the use of a proportionate technique, has been proposed for sparse system identification and echo cancellation. In this algorithm, each tap is assigned an individual step size, which is obtained from the previous estimation of the filter coefficient. According to the gain allocation rule in this algorithm, the greater the magnitude of the tap, the larger the step size assigned to it, and hence the active taps converge quickly. The update function of the PNLMS algorithm [19] is described by the following equation with reference to Figure 1: Here, , which denotes as the gain matrix, is a diagonal matrix that modifies the step size of each tap, is the global step size of the PNLMS algorithm, and is a regularization parameter to prevent division by zero at the initialization stage, where is the power of the input signal . In the PNLMS algorithm, the gain matrix is given by where the individual gain is defined as with where the parameters and are positive constants with typical values of and . is used to regularize the updating at the initial stage when all the taps are initialized to zero, and is used to prevent from stalling when it is much smaller than the largest coefficient.
2.3. Improved Proportionate Normalized LeastMeanSquare Algorithms
2.3.1. IPNLMS Algorithm
The IPNLMS algorithm is a type of PNLMS algorithm used to improve the convergence speed of the PNLMS algorithm. It is a combination of the PNLMS and NLMS algorithms with the relative significance of each coefficient controlled by a factor . The IPNLMS algorithm [20] adopts the norm to enable the smooth selection of (7), and the update equation of the IPNLMS algorithm is expressed as where is a diagonal matrix used to adjust the step size of the IPNLMS algorithm, where for a small positive constant and . At the initial stage, the step size is multiplied by , since all the filter coefficients are initialized to zero. Thus, in the IPNLMS algorithm, a regularization parameter is introduced, which is given by
We can see that the IPNLMS is identical to the NLMS algorithm for , while the IPNLMS behaves identically to the PNLMS algorithm when . In practical engineering applications, a suitable value for is 0 or .
2.3.2. MPNLMS Algorithm
The law PNLMS algorithm (MPNLMS) is another enhancement of the PNLMS algorithm that utilizes the logarithm of the magnitudes of the filter coefficients instead of using the magnitudes directly in the PNLMS algorithm [21]. The update equation is the same as that in the PNLMS algorithm given by (4). In the MPNLMS algorithm, where where is a large positive constant related to the estimation accuracy requirement, typically .
3. Proposed LPPNLMS Algorithm
In this section, we propose an LPPNLMS algorithm by incorporating the norm into the cost function of the PNLMS algorithm to create a zero attractor, making it a type of ZA algorithm. The difference between the LPPNLMS algorithm and general ZA algorithms is that the gainmatrixweighted norm is taken into account in designing the zero attractor. On the other hand, the proposed LPPNLMS algorithm is based on the commonly used PNLMS algorithm, which is also a sparse channel estimation algorithm and can improve the convergence for the active taps. Regarding channel estimation, the purpose of the LPPNLMS algorithm is to minimize where is the inverse of the gain matrix in the PNLMS algorithm, is a very small constant used to balance the estimation error and the sparse norm penalty of , is the norm defined as , and . Note that in (13), we introduce an norm penalty to after scaling the gain matrix by , which is different from the previously proposed ZA LMS algorithms.
To minimize (13), the Lagrange multiplier method is adopted, and the cost function of the proposed LPPNLMS algorithm is expressed as where is the Lagrange multiplier.
By calculating the gradient of the cost function of the LPPNLMS algorithm and assuming in the steady stage, we have
In practice, we need to introduce a small positive constant into the final term in (16) to cope with the situation that an entry of approaches zero, which is the case for a sparse CIR at initialization. Then the update equation (16) of the LPPNLMS algorithm is modified to where is a small value to prevent division by zero. By multiplying both sides of (17) by , we obtain
From (2), (15), and (17), we obtain
Then, the Lagrange multiplier is given as follows by solving (19):
Substituting (20) into (17), we have
It was found that the magnitudes of the elements in the matrix are much smaller than 1 for broadband multipath channel estimation. Therefore, the update equation (21) of the proposed LPPNLMS algorithm is rewritten as Here, we neglect the effects of the matrix and assume that the filter order is large. Similarly to the PNLMS algorithm, a step size is introduced to balance the convergence speed and the steadystate error of the proposed LPPNLMS algorithm, and a small positive constant is employed to prevent division by zero. Thus, the update function (22) can be modified to where and . Comparing the update function (23) of the proposed LPPNLMS algorithm with the update function (4) of the PNLMS algorithm, we see that our proposed LPPNLMS algorithm has the additional term , also defined as the zero attractor, which attracts the small channel taps to zero with high probability. Moreover, the ZA strength of this zero attractor is controlled by . In other words, in our proposed LPPNLMS algorithm, the gain matrix assigns a large step size to the active channel taps of the sparse channel, while the zero attractor mainly exerts the penalty on the inactive taps whose taps are zero or close to zero. Thus, our proposed LPPNLMS algorithm can further improve the convergence speed of the PNLMS algorithm after the convergence of the large active taps.
4. Results and Discussions
In this section, we present the results of computer simulations carried out to illustrate the channel estimation performance of the proposed LPPNLMS algorithm over a sparse multipath communication channel and compare it with those of the previously proposed IPNLMS, MPNLMS, PNLMS, and NLMS algorithms. Here, we consider a sparse channel whose length is 64 or and whose number of dominant active taps is set to three different sparsity levels, namely, , and , similar to previous studies [6, 22, 25, 26]. The dominant active channel taps are obtained from a Gaussian distribution with , and the positions of the dominant channel taps are randomly spaced along the length of the channel. The input signal of the channel is a Gaussian random signal while the output of the channel is corrupted by an independent white Gaussian noise . An example of a typical sparse multipath channel with a channel length of and a sparsity level of is shown in Figure 2. In the simulations, the power of the received signal is , while the noise power is given by and the signaltonoise ratio is defined as SNR = . In all the simulations, the difference between the actual and estimated channels based on the sparsityaware algorithms and the sparse channel mentioned above is evaluated by the MSE defined as follows:
In these simulations, the simulation parameters are chosen to be , , , , , , , , , , and SNR = 30 dB. When we change one of these parameters, the other parameters remain constant.
4.1. Estimation Performance of the Proposed LPPNLMS Algorithm
4.1.1. Effects of Parameters on the Proposed LPPNLMS Algorithm
In the proposed LPPNLMS algorithm, there are two extra parameters, and , compared with the PNLMS algorithm, which are introduced to design the zero attractor. Next, we show how these two parameters affect the proposed LPPNLMS algorithm over a sparse channel with or and . The simulation results for different values of and are shown in Figures 3 and 4, respectively. From Figure 3(a), we can see that the steadystate error of the LPPNLMS algorithm decreases with decreasing when , while it increases again when is less than . Furthermore, the convergence speed of the LPPNLMS algorithm rapidly decreases when is less than . This is because a small results in a low ZA strength, which consequently reduces the convergence speed. In the case of shown in Figure 3(b), we observe that both the convergence speed and the steadystate performance are improved with decreasing for . When , the convergence speed of the LPPNLMS algorithm decreases while the steadystate error remains constant.
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Figure 4 demonstrates the effects of the parameter . We can see from Figure 4(a) that the convergence speed of the proposed LPPNLMS algorithm rapidly decreases with increasing for . Moreover, the steadystate error is reduced with ranging from 0.45 to 0.5, while it remains constant for , 0.7, and 0.8. However, the steadystate performance for is inferior to that for . This is because the proposed LPPNLMS algorithm is an normpenalized PNLMS algorithm, which cannot distinguish between active taps and inactive taps, reducing its convergence speed and steadystate performance. When , as shown in Figure 4(b), the steadystate performance is improved as increases from 0.45 to 0.6. Thus, we should carefully select the parameters and to balance the convergence speed and steadystate performance for the proposed LPPNLMS algorithm.
4.1.2. Effects of Sparsity Level on the Proposed LPPNLMS Algorithm
On the basis of the results discussed in Section 4.1.1 for our proposed LPPNLMS algorithm, we choose and to evaluate the channel estimation performance of the LPPNLMS algorithm over a sparse channel with different channel lengths of and , for which the obtained simulation results are given in Figures 5 and 6, respectively. From Figure 5, we see that our proposed LPPNLMS algorithm has the same convergence speed as the PNLMS algorithm at the initial stage. The proposed LPPNLMS algorithm converges faster than the PNLMS algorithm as well as the IPNLMS and NLMS algorithms for all sparsity levels , while its convergence is slightly slower than that of the MPNLMS algorithm before it reaches a steady stage. However, the proposed LPPNLMS algorithm has the smallest steadystate error for . When , we see from Figure 6 that our proposed LPPNLMS algorithm not only has the highest convergence speed but also possesses the best steadystate performance. This is because with increasing sparsity, our proposed LPPNLMS algorithm attracts the inactive taps to zero quickly and hence the convergence speed is significantly improved, while the previously proposed PNLMS algorithms mainly adjust the step size of the active taps and thus they only impact on the convergence speed at the early iteration stage. Additionally, we see from Figures 5 and 6 that both the convergence speed and the steadystate performance of all the PNLMS algorithms deteriorate when the sparsity level increases for both and . In particular, when , the convergence speeds of the PNLMS and IPNLMS algorithms are greater than that of the NLMS algorithm at the early iteration stage, while after this fast initial convergence, their convergence speeds decrease to less than that of the NLMS algorithm before reaching a steady stage. Furthermore, we observe that the MPNLMS algorithm is sensitive to the length of the channel, and its convergence speed for is less than that for at the same sparsity level and less than that of the proposed LPPNLMS algorithm. Thus, we conclude that our proposed LPPNLMS algorithm is superior to the previously proposed PNLMS algorithms in terms of both the convergence speed and the steadystate performance with the appropriate selection of the related parameters and . From the above discussion, we believe that the gainmatrixweighted norm method in the LPPNLMS algorithm can be used to further improve the channel estimation performance of the IPNLMS and MPNLMS algorithms.
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4.2. Computational Complexity
Finally, we discuss the computational complexity of the proposed LPPNLMS algorithm and compare it with those of the NLMS, PNLMS, IPNLMS, and MPNLMS algorithms. Here, the computational complexity is the arithmetic complexity, which includes additions, multiplications, and divisions. The computational complexities of the proposed LPPNLMS algorithm and the related PNLMS and NLMS algorithms are shown in Table 1.

From Table 1, we see that the computational complexity of our proposed LPPNLMS algorithm is slightly higher than those of the MPNLMS and PNLMS algorithms, which is due to the calculation of the gradient of the norm. Furthermore, the MPNLMS algorithm has an additional logarithm operation, which increases its complexity but is not included in the Table 1. However, the LPPNLMS algorithm noticeably increases the convergence speed and significantly improves the steadystate performance of the PNLMS algorithm. In addition, it also has a higher convergence speed and lower steadystate error than the IPNLMS and MPNLMS algorithms when the channel length is large.
5. Conclusion
In this paper, we have proposed an LPPNLMS algorithm to exploit the sparsity of broadband multipath channels and to improve both the convergence speed and steadystate performance of the PNLMS algorithm. This algorithm was mainly developed by incorporating the gainmatrixweighted norm into the cost function of the PNLMS algorithm, which significantly improves its convergence speed and steadystate performance. The simulation results demonstrated that our proposed LPPNLMS algorithm, which has an acceptable increase in computational complexity, increases the convergence speed and reduces the steadystate error compared with the previously proposed PNLMS algorithms.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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Copyright
Copyright © 2014 Yingsong Li and Masanori Hamamura. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.