This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of ...This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of them ignore the quality of the source-relay (S-R) channel in the detection at the destination, although this channel can contribute heavily to the performance of cooperation schemes. For optimal detection, the destination has to account all possible error events at the relay as well. Here we present a Maximum Likelihood criterion (ML) at the destination which considers closed-form expressions for each symbol error rate (SER) to facilitate the detection. Computer simulations show that significant diversity gain and Packet Error Rate (PER) performance can be achieved by the proposed scheme with good tolerance to propagation errors from noisy relays. In fact, diversity gain is increased with additional relay nodes. We compare this scheme against the baseline Cooperative-Maximum Ratio Combining (C-MRC).展开更多
针对多径环境下异步长短码直扩码分多址信号(long and short code direct sequence code division multiple access,LSC-DS-CDMA)伪码估计难的问题,提出一种基于张量分解和联合估计的伪码估计方法,采用重叠窗对接收信号进行分段并构建...针对多径环境下异步长短码直扩码分多址信号(long and short code direct sequence code division multiple access,LSC-DS-CDMA)伪码估计难的问题,提出一种基于张量分解和联合估计的伪码估计方法,采用重叠窗对接收信号进行分段并构建张量模型。为改善传统线性步长搜索算法结合梯度下降的方法分解因子矩阵收敛较慢的问题,提出改进的线性步长搜索算法,结合使用动量梯度下降法对各子张量进行Tucker分解得到各因子矩阵,所需的迭代次数大大减少;利用接收增益矩阵和移位相乘解决复合码的排序模糊和幅度模糊问题;利用最大似然准则联合估计复合码和多径信道后,使用梅西算法和相关运算估计每个用户的长码和短码。仿真结果表明,该方法能够有效估计多径异步LSC-DS-CDMA信号的伪码。展开更多
In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likeliho...In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.展开更多
文摘This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of them ignore the quality of the source-relay (S-R) channel in the detection at the destination, although this channel can contribute heavily to the performance of cooperation schemes. For optimal detection, the destination has to account all possible error events at the relay as well. Here we present a Maximum Likelihood criterion (ML) at the destination which considers closed-form expressions for each symbol error rate (SER) to facilitate the detection. Computer simulations show that significant diversity gain and Packet Error Rate (PER) performance can be achieved by the proposed scheme with good tolerance to propagation errors from noisy relays. In fact, diversity gain is increased with additional relay nodes. We compare this scheme against the baseline Cooperative-Maximum Ratio Combining (C-MRC).
文摘针对多径环境下异步长短码直扩码分多址信号(long and short code direct sequence code division multiple access,LSC-DS-CDMA)伪码估计难的问题,提出一种基于张量分解和联合估计的伪码估计方法,采用重叠窗对接收信号进行分段并构建张量模型。为改善传统线性步长搜索算法结合梯度下降的方法分解因子矩阵收敛较慢的问题,提出改进的线性步长搜索算法,结合使用动量梯度下降法对各子张量进行Tucker分解得到各因子矩阵,所需的迭代次数大大减少;利用接收增益矩阵和移位相乘解决复合码的排序模糊和幅度模糊问题;利用最大似然准则联合估计复合码和多径信道后,使用梅西算法和相关运算估计每个用户的长码和短码。仿真结果表明,该方法能够有效估计多径异步LSC-DS-CDMA信号的伪码。
文摘In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.
