- Research Article
- Open Access
A Fast Soft Bit Error Rate Estimation Method
© Samir Saoudi et al. 2010
- Received: 9 March 2010
- Accepted: 30 August 2010
- Published: 5 September 2010
We have suggested in a previous publication a method to estimate the Bit Error Rate (BER) of a digital communications system instead of using the famous Monte Carlo (MC) simulation. This method was based on the estimation of the probability density function (pdf) of soft observed samples. The kernel method was used for the pdf estimation. In this paper, we suggest to use a Gaussian Mixture (GM) model. The Expectation Maximisation algorithm is used to estimate the parameters of this mixture. The optimal number of Gaussians is computed by using Mutual Information Theory. The analytical expression of the BER is therefore simply given by using the different estimated parameters of the Gaussian Mixture. Simulation results are presented to compare the three mentioned methods: Monte Carlo, Kernel and Gaussian Mixture. We analyze the performance of the proposed BER estimator in the framework of a multiuser code division multiple access system and show that attractive performance is achieved compared with conventional MC or Kernel aided techniques. The results show that the GM method can drastically reduce the needed number of samples to estimate the BER in order to reduce the required simulation run-time, even at very low BER.
- Monte Carlo
- Gaussian Mixture Model
- Code Division Multiple Access
- Expectation Maximization Algorithm
- Code Division Multiple Access System
To study the performance of a digital communications system, we need to use, in general, the Monte Carlo (MC) method to estimate the BER. A tutorial exposition of different techniques is provided in  with particular reference to four other specific methods: modified Monte Carlo simulation (importance sampling), extreme value theory, tail extrapolation, and quasianalytical method. The modified Monte Carlo is achieved by importance sampling which means that important events, and then errors, are artificially generated by biasing the noise process. At the end of simulation, the error count must be properly unbiased (see also ). The extreme value theory (see ) assumes that the pdf can be approximated by exponential function. The tail extrapolation method, which is a subset of the previous one, is based on the assumption that only the tail region of the pdf can be described by a generalized exponential class. The quasianalytical method combines noiseless simulation with analytical representation of noise. In , High-Order Statistics (HOS) of the bit Log-Likelihood-Ratio (LLR) are used for evaluating performance of turbo-like codes. In this case, the characteristic function of the bit LLR is estimated by using its first cumulants (or moments). The pdf is therefore computed by using the inverse Fourier transform. The reader can find other recent papers on this topic (see [5–8]).
In , we have suggested a soft BER estimation based on the nonparametric computation of the pdf of the received data. We have shown, in this last case, that hard decision is not needed to compute the BER and that the total necessary number of transmitted data is very small compared to the classical MC simulation. This allows a significant reduction in run time for computer simulations and may also be used as the basis for rapid real-time BER estimation in real communication systems.
In this paper, we suggest to use a Gaussian Mixture (GM) model to estimate the pdf of the observed samples. Two conditional pdfs are computed corresponding to the transmitted bits equal to . The Expectation Maximisation (EM) algorithm is used to estimate, in an iterative fashion, the different parameters of this mixture, that is, the means, the variances, and the a priori probabilities. The different parameters are estimated by using the Maximum Expectation of joint Likelihood criterion. The analytical expression of the BER is therefore simply given by using the different estimated parameters of the Gaussian Mixture. The choice of the number of Gaussians for each pdf is very important. In , a method, based on Mutual Information Theory, was presented to find the optimal number of Gaussians in order to give an accurate estimation of the pdf. Our suggested analytical expression of the BER, based on the Gaussian Mixture model, where parameters are jointly estimated by EM algorithm and Mutual Information theory leads to an efficient fast way to estimate the performance of a digital communications system. Simulation results are carried out to compare the three mentioned methods: Monte Carlo, Kernel, and Gaussian Mixture. We analyze the performance of the proposed BER estimator in the framework of a multiuser code division multiple access (CDMA) system with single user detection and show that attractive performance is achieved compared with conventional Monte Carlo (MC) or Kernel aided techniques. The results show that the GM method can drastically reduce the needed number of samples to estimate the BER in order to reduce the simulation run-time even at very low BER.
The main idea of this paper is the use, in an iterative way, of the Expectation Maximisation (EM) Algorithm, for the pdf estimation with Gaussian Mixture model, jointly with the Mutual Information Theory, for the computation of the optimal number of Gaussians. The analytical expression of the BER is therefore given by using the different estimated parameters of the Gaussian Mixture.
The EM algorithm was introduced for the first time by Dempster et al. . It is an iterative computation method of maximum likelihood estimates of missing data from observable variables. In this paper observable variables are simply given by soft output values at the receiver of a digital communications system. Missing data is given by unknown true component (Gaussian) from which the observation comes. Two conditional pdfs, according to the transmitted bits , must be estimated. For each pdf, a Gaussian Mixture model, with a large enough initial number of components, is used. Then, the EM algorithm performs, in an iterative way, the estimation of the parameters for each component, that is, means, variances, and a priori probabilities. The Mutual Information (MI), according to Shannon Theory, is computed. A component with positive MI is assumed to be dependent on others components and could be removed without damaging the pdf estimation. The EM algorithm can be performed with a new decreased value of the number of Gaussians. The algorithm stops when the maximum of the computed MI, over all the components, is nonpositive which means that all the reached components are likely independent and therefore gives an optimal structure of the Gaussian Mixture model. The two conditional pdfs are then estimated, in a parallel fashion, by using the Mutual Information Theory to compute iteratively the optimal number of components and a subiteration for the EM algorithm to estimate the different parameters of each component. An analytical expression of the BER is therefore obtained by using all parameters of the Gaussian Model at the last iteration.
Let us recall that the EM algorithm has mainly been used, in the past twenty last years, in image processing or more precisely in image segmentation for different applications such as image or video compression. The reader can find in  an example of an application of SEM (Stochastic version of EM) algorithm in SPOT satellite image segmentation where a Gaussian distribution is assumed for each class. In , a hybrid version of SEM is used assuming a generalized Dirichlet distribution.
Nonparametric pdf estimation has also been used in different applications such as speech coding and pattern recognition [14, 15]. The Gaussian Mixture model has also been used in speaker identification .
Let be a set of transmitted bits. The are assumed to be independent and identically distributed with and , where . Let us note that the corresponding soft output at the receiver such as the hard decision is taken by using its sign: . All the received soft output decisions are random variables having the same pdf, .
Throughout the paper, the following notation is used. The output decisions are random variables having the same pdf, . The cardinality of set is denoted . When is a random variable, and denote the mathematical expectation and variance of , respectively. When is a second derivative function, and denote its first and second derivatives at point , respectively. denotes the sign of the argument, and is the natural log function. is the probability of a given event, and superscript denotes the transpose.
The paper is organized as follows: Section 2 briefly shows how the probability density function (pdf) of the soft output signal at the receiver is estimated using the Kernel method in a nonparametric way by estimating the optimal smoothing parameter. The BER is performed based on all soft observations and the smoothing parameter value. In Section 3, we will show how a Gaussian Mixture model can be performed, for each conditional pdf, by using the Expectation Maximization (EM) algorithm. The Mutual Information is used to compute iteratively the optimal number of Gaussians. The BER is, therefore, simply computed by using all parameters (means, variances, and a priori probabilities) for each conditional pdf given by the EM algorithm at the last iteration. Different simulation results are presented in 4. Finally, a brief conclusion is given in Section 5. Proofs of all theoretical results are given in the appendices.
2.1. Pdf Estimation Based on Kernel Method
Where is the smoothing parameter which depends on the length of the observed samples, . is any pdf (called the kernel) assumed to be an even and regular (i.e, square integrated) function with unit variance and zero mean. For simplicity reasons, we will not give the corresponding equations for the conditional pdf, . The reader can easily find them by replacing " " by " ".
2.2. BER Estimation Based on Kernel Method
where denotes the complementary unit cumulative Gaussian distribution, for example, .
In this section, instead of using the Kernel method (given by (2)), a Gaussian Mixture (GM) model will be used. The mixture model is used in general for its mathematical flexibilities. For example, a mixture of two Gaussian distributions with different means and different variances results in a density with two modes, which is not a standard parametric distribution model. Mixture distributions can model extreme events better than the basic Gaussian ones. More details about Mixture distributions can be found in .
The following sub section will show how to estimate the two conditional pdfs using a Gaussian Mixture model. The Expectation Maximization (EM) algorithm will be performed to compute the mean, the variance and the a priori probability of each component of this mixture. Therfore, Section 3.2 will show how the BER can be simply computed by using these different estimated parameters. For simplicity reason, equations are developped only for one conditional pdf, . The reader can easily find all the corresponding equations for the estimation of .
3.1. Pdf Estimation Based on Gaussian Mixture Method
where is the a priori probability of the th component for the Gaussian mixture and is a Gaussian pdf with as a mean and as a variance. We have the constraint that .
Let be the soft observed samples corresponding to the transmitted bits equal to . As the pdf of the obseved samples is a mixture of Gaussians, this means (see ) that each is produced by one component of this mixture ( ). We have to find the value of this component: this is the missing data that we will try to compute. Let be the missing data which is a sequence of variables that determines the component from which the observations originate. means that is generated by the th component of the Gaussian mixture, that is, . The a priori probability represents the probability that , that is, .
In this section, we will use the Expection Maximization algorithm to estimate, in an iterative way, the unknown parameter . For each new iteration and for a given estimate of the paramater , computed at a previous iteration, two steps are performed. In the first one, that is, Estimation step, we will compute the different a posteriori probabilities (APP): . In the second one, that is, Maximization step, we will compute the new parameter by maximizing the conditional expectation of the Joint Log likelihood of observed samples, , and missing data, .
3.1.1. Estimation Step
3.1.2. Maximization Step
Where is the joint likelihood event given by (7).
3.2. BER Estimation Based on Gaussian Mixture Method
In this section, we derive the expression of BER estimate assuming Gaussian Mixture based pdf estimator. Let and the reached values at the last iteration of the EM algorithm described in the previous Section 3.1. The parameter (resp., ) allows the estimation of the conditional pdf (resp., ). Let us underline that we need to perform the EM algorithm two times and in independent way. At each time, a different data base is used: (resp., ), of soft observations corresponding to the transmitted bits equal (resp., ) to estimate (resp., ).
Where denotes the classical complementary unit cumulative Gaussian distribution. Details regarding the derivation of (15) are provided in Appendix D.
3.3. Optimal Choice of the Number of Components
The choice of and is very important. It is clear that if this number of components is too low, the corresponding pdf will be too smooth and then the BER less reliable. On the other hand, if this number is too high, this means that the same class of observed samples comes from different components and then these components should be correlated which is not useful for simulation since all the observed data are assumed to be independent. Consequently, the optimal number of components has to be the largest one such that all the components are independent. Similar method has been suggested by  for speaker identification applications by increasing the number of classes in the -means algorithm. More mathematical details can be found in . Here, we suggest to initialize the algorithm with a high enough value, to perform the EM algorithm to estimate the different components and test their independence after the last iteration. If it is not the case, we have to decrease iteratively the number of components until the independence is reached.
The quantity presents the mutual information for the component and denotes whether this component has a significant and independent contribution to the pdf estimation. The biggest positive value has a less and dependent contribution to the GM estimation and should therefore be removed. The proposed Gaussian Mixture based BER estimation using EM algorithm and Mutual Information theory can now be summarized in Algorithm 1. Figure 2 gives the flow chart of the suggested algorithm.
Algorithm 1: Summary of the Gaussian Mixture based BER estimation using EM algorithm and Mutual Information theory. and are computed in a parallel fashion.
1.1. Classify soft outputs according to their transmitted bits, that is,
1.2. Deduce the two priori probabilities:
1.3. Choose a large enough number of the Gaussian mixture: and .
2. EM algorithm for :
2.1. Initialization of
2.2. At each iteration ,
2.2.1 Estimation step:
Estimate APPs, using (10).
2.2.2 Maximization step:
Computation of by using (12), (13) and (14).
3. Verification of optimality of :
3.1. Compute (19)
3.2. If , save go to step 4. Otherwise, : , go to 2.
4. EM algorithm for :
4.1. Initialization of
4.2. At each iteration ,
4.2.1 Estimation step:
Estimate APPs, using (10).
4.2.2 Maximization step:
Computation of by using (12), (13) and (14).
5. Verification of optimality of :
5.1. Compute (19)
5.2. If , save go to step 6. Otherwise, : , go to 4.
6. BER Computation:
6.1. Compute the BER estimate from (15) using and .
To evaluate the performance of the three methods, we consider the framework of a synchronous CDMA system with two users using binary phase-shift keying (BPSK) and operating over an additive white Gaussian noise (AWGN) channel. We restrict ourselves to the conventional single user CDMA detector. Performance assessment in the case of advanced signaling/receivers is not reported in this paper due to space limitation and is left for future contributions.
where denotes the spreading factor, and is the spreading code corresponding to user . is the amplitude of user , is the information bit value of user at time instant , and is the temporally and spatially white Gaussian noise, that is, . The a priori probabilities of information bits are supposed to be identical and uniform for both users, that is, .
Where the cross-correlation is . We consider the case where the two users have equal powers . The SNR at the output of the MF of each user is therefore .
4.1. Output Pdf Estimation Comparison
First of all, in this simulation, we would like to compare the three different pdf estimation: Histogram method which leads to MC method, Kernel method and Gaussian Mixture method. In order to make a fair comparison, the three methods are used in optimal conditions. In particular, the length of the bins of the Histogram is chosen equal to the smoothing parameter computed for the kernel method so as the convergence of the histogram in the MSE and IMSE criterion can be guaranteed.
Mean and Standard deviation of Integrated Square Error pdf estimation for the three methods by using 1,000 different trials. samples are used for each trials.
Mean and Standard deviation of error estimation of using the three methods with 1,000 different trials. samples are used for each trial.
4.2. Performance Comparison for the Three Methods
Mean and Standard deviation of BER estimation with the three methods (MC, Kernel, GM) at different SNR. samples are used for each simulation. 1,000 different trials are performed to compute the Mean and the Standard deviation.
On the other hand, for GM method and for a fixed SNR value ( ), we have computed the Mean BER and the Standard deviation for different number of soft outputs (between and ) with different trials.
Mean, Standard deviation and precision of BER estimation GM method, for SNR = 10 dB, at different number of samples are used for each simulation. 100 different trials are performed to compute the Mean, the Standard deviation and the precision.
Nb of samples
Mean of BER
4.3. Performance of GM Method in the High SNR Region
CPU time for Monte Carlo simulation at different BER, assuming using computers with equivalent power to the one we used for Gaussian Mixture simulation.
4.4. Comparison with Importance Sampling Technique
Importance sampling technique, also known as Modified Monte Carlo method, was introduced by Shanmugam and Balaban (see ) to estimate error probabilities in digital communications systems. The importance sampling technique is used to modify the probability density function of the noise process in a way to make simulation possible. An estimate of the pdf of soft decision is constructed in a histogram form. The idea is to modify this pdf, that is, the statistical properties of the soft decision sequence, in such a way that higher rate of errors occur in the simulation process. Therefore, the error count has to be modified appropriately to obtain an unbiased estimate of the true error probability. The main drawback of the importance sampling technique is the difficulty, for complex systems, of determining which regions of the pdf to bias and how to bias these regions.
Compared to classical Monte Carlo method, the importance sampling technique, reduces the sample size requirement by a factor ranging from up to . Let us assume that this factor is equal to in order to compare importance sampling technique with our suggested GM method. In this case, the CPU time given in Table 5 for Monte Carlo simulation has to be divided by a factor of to obtain the CPU time for the importance sampling technique simulation. Our suggested GM method has still a huge advantage as the run time does not depend on the value of SNR. In fact, for each SNR point, the simulation takes less than seconds (see Section 4.2). Another advantage of our suggested GM method is the possibility to estimate the performance of a system at a very very low BER (down to for a CDMA system case in this paper, see Figure 6). The only limitation is given by the precision of the used computer.
In this paper, we considered the problem of BER estimation for a digital communications system using any transmission technology or channel coding. BPSK modulation is used. The receiver is assumed to be able to compute soft decision. We proposed a BER estimation algorithm where only soft observations that serve for computing hard decisions about information bits are used. First of all, we provided a formulation of the problem where we showed that BER estimation is equivalent to the estimation of conditional pdfs of soft observations corresponding to the transmitted bits equal to . We then proposed a BER computation technique using Gaussian Mixture-based pdf estimation. The Expectation Maximisation (EM) algorithm was used to estimate, in an iterative way, the different parameters of this mixture, that is, the means, the variances and the a priori probabilities. The analytical expression of the BER was therefore simply given by using the different estimated parameters of the Gaussian Mixture. The optimal number of Gaussians was computed using Mutual Information Theory. Finally, we evaluated the performance of the proposed BER estimation technique in the framework of CDMA systems. Performance comparison with MC techniques and Kernel method was simulated. Interestingly, we showed that while classical MC method fails to perform BER estimation in the region of high SNR, the proposed GM estimator provides reliable estimates and better, in the sense of minimum Mean Squared Error, than Kernel method using only few soft observations. A measure of BER down to has been reached in less than seconds using only soft outputs samples.
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