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Cet article est extrait de la revue Gestion 2000 Volume 20 - numéro 6 - 2003

Cet article est extrait de la revue Gestion 2000 Volume 19 - numéro 4 - 2002

Code-division multiple access (CDMA) has recently found widespread applications in various applications such as advanced wireless technologies and digital cellular communications, In this paper, we consider the error-rate performance of asynchronous direct-sequence CDMA in a wireless radio communications environment that is characterized by Rayleigh multipath fading channels, Because of the system complexity, the performance of CDMA systems in such an environment is difficult to assess analytically, Monte Carlo simulations can offer an alternative performance evaluation tool, Unfortunately, in practice, such an alternative is often limited by an excessive computational burden, Importance sampling is a variance reduction technique which can significantly reduce the computational cost required by Monte Carlo simulations, This paper will describe the baste principles behind this powerful technique, In particular, it will illustrate its use as a computationally efficient and accurate tool for computer-aided design and analysis of uncoded as well as coded CDMA in wireless communications.

Code-division multiple access (CDMA) has recently found widespread applications in various applications such as advanced wireless technologies and digital cellular communications, In this paper, we consider the error-rate performance of asynchronous direct-sequence CDMA in a wireless radio communications environment that is characterized by Rayleigh multipath fading channels, Because of the system complexity, the performance of CDMA systems in such an environment is difficult to assess analytically, Monte Carlo simulations can offer an alternative performance evaluation tool, Unfortunately, in practice, such an alternative is often limited by an excessive computational burden, Importance sampling is a variance reduction technique which can significantly reduce the computational cost required by Monte Carlo simulations, This paper will describe the baste principles behind this powerful technique, In particular, it will illustrate its use as a computationally efficient and accurate tool for computer-aided design and analysis of uncoded as well as coded CDMA in wireless communications.

Code-division multiple access (CDMA) has recently found widespread applications in various applications such as advanced wireless technologies and digital cellular communications, In this paper, we consider the error-rate performance of asynchronous direct-sequence CDMA in a wireless radio communications environment that is characterized by Rayleigh multipath fading channels, Because of the system complexity, the performance of CDMA systems in such an environment is difficult to assess analytically, Monte Carlo simulations can offer an alternative performance evaluation tool, Unfortunately, in practice, such an alternative is often limited by an excessive computational burden, Importance sampling is a variance reduction technique which can significantly reduce the computational cost required by Monte Carlo simulations, This paper will describe the baste principles behind this powerful technique, In particular, it will illustrate its use as a computationally efficient and accurate tool for computer-aided design and analysis of uncoded as well as coded CDMA in wireless communications.

This paper considers the performance of code division multiple access (CDMA) communications. Of particular interest is the evaluation of the average bit error rate (BER) which cannot be performed analytically in most practical situations. Conventional Monte Carlo simulation can provide an alternative performance evaluation tool. Unfortunately, such an approach is often limited by the excessive computational burden. Importance Sampling is a modified Monte Carlo technique which can significantly reduce the computational cost required to achieve accurate Monte Carlo estimates. In this paper, we will describe the basic principles behind this method. In particular, we will illustrate its use for the efficient simulation of asynchronous direct-sequence (DS) based CDMA systems employing binary phase shift keying modulation along with specific signature sequences for a variety of system parameters.

This paper considers the performance of code division multiple access (CDMA) communications. Of particular interest is the evaluation of the average bit error rate (BER) which cannot be performed analytically in most practical situations. Conventional Monte Carlo simulation can provide an alternative performance evaluation tool. Unfortunately, such an approach is often limited by the excessive computational burden. Importance Sampling is a modified Monte Carlo technique which can significantly reduce the computational cost required to achieve accurate Monte Carlo estimates. In this paper, we will describe the basic principles behind this method. In particular, we will illustrate its use for the efficient simulation of asynchronous direct-sequence (DS) based CDMA systems employing binary phase shift keying modulation along with specific signature sequences for a variety of system parameters.

This paper considers the performance of code division multiple access (CDMA) communications. Of particular interest is the evaluation of the average bit error rate (BER) which cannot be performed analytically in most practical situations. Conventional Monte Carlo simulation can provide an alternative performance evaluation tool. Unfortunately, such an approach is often limited by the excessive computational burden. Importance Sampling is a modified Monte Carlo technique which can significantly reduce the computational cost required to achieve accurate Monte Carlo estimates. In this paper, we will describe the basic principles behind this method. In particular, we will illustrate its use for the efficient simulation of asynchronous direct-sequence (DS) based CDMA systems employing binary phase shift keying modulation along with specific signature sequences for a variety of system parameters.

Two importance sampling techniques are presented for estimating the distribution of computation of sequential decoding for specific convolutional codes (not ensemble averages). Only stack algorithm decoders are considered. Numerical examples are presented for both the BSC and the AWGN channel. © 1993 IEEE

Two importance sampling techniques are presented for estimating the distribution of computation of sequential decoding for specific convolutional codes (not ensemble averages). Only stack algorithm decoders are considered. Numerical examples are presented for both the BSC and the AWGN channel. © 1993 IEEE

Two importance sampling techniques are presented for estimating the distribution of computation of sequential decoding for specific convolutional codes (not ensemble averages). Only stack algorithm decoders are considered. Numerical examples are presented for both the BSC and the AWGN channel. © 1993 IEEE

Importance Sampling (IS) is a Monte Carlo (MC) technique that employs a" biased" distribution to improve computational efficiency. In this paper, we present two IS schemes (including some variations) for estimating the distribution of computation for stack decoders. The fiist general method is called the reference path method. This method biases noise inputs using the weight distribution of the associated convolutional code (CC). The second method is the partitioning method. This method uses a stationary biasing of noise inputs.

Importance sampling is a Monte Carlo simulation technique which as been recognised as a powerful tool for simulating low probability events. This letter presents new efficient importance sampling simulation schemes for estimating the distribution of computation for sequential decoders that employ the stack algorithm.

Importance Sampling (IS) is a Monte Carlo (MC) technique that employs a" biased" distribution to improve computational efficiency. In this paper, we present two IS schemes (including some variations) for estimating the distribution of computation for stack decoders. The fiist general method is called the reference path method. This method biases noise inputs using the weight distribution of the associated convolutional code (CC). The second method is the partitioning method. This method uses a stationary biasing of noise inputs.

Importance sampling is a Monte Carlo simulation technique which as been recognised as a powerful tool for simulating low probability events. This letter presents new efficient importance sampling simulation schemes for estimating the distribution of computation for sequential decoders that employ the stack algorithm.

Importance sampling is a Monte Carlo simulation technique which as been recognised as a powerful tool for simulating low probability events. This letter presents new efficient importance sampling simulation schemes for estimating the distribution of computation for sequential decoders that employ the stack algorithm.

Importance Sampling (IS) is a Monte Carlo (MC) technique that employs a" biased" distribution to improve computational efficiency. In this paper, we present two IS schemes (including some variations) for estimating the distribution of computation for stack decoders. The fiist general method is called the reference path method. This method biases noise inputs using the weight distribution of the associated convolutional code (CC). The second method is the partitioning method. This method uses a stationary biasing of noise inputs.

Two importance sampling techniques are presented for estimating the distribution of computation of sequential decoding for specific convolutional codes (not ensemble averages). Only stack algorithm decoders are considered. Numerical examples are presented for both the BSC and the AWGN channel

Two importance sampling techniques are presented for estimating the distribution of computation of sequential decoding for specific convolutional codes (not ensemble averages). Only stack algorithm decoders are considered. Numerical examples are presented for both the BSC and the AWGN channel

Two importance sampling techniques are presented for estimating the distribution of computation of sequential decoding for specific convolutional codes (not ensemble averages). Only stack algorithm decoders are considered. Numerical examples are presented for both the BSC and the AWGN channel