Broadband power line communication (BPLC) gained a lot of interest because of low cost and high performance communication network in access area. In this paper physical (PHY) layer and medium access control (MAC) sub-...Broadband power line communication (BPLC) gained a lot of interest because of low cost and high performance communication network in access area. In this paper physical (PHY) layer and medium access control (MAC) sub-layer of BPLC are considered. Furthermore, effects of bit error rate (BER) are analyzed in MAC sub-layer. Powerful turbo convolutional code (TCC) and wideband orthogonal frequency division multiplexing (OFDM) are used in PHY layer. Carrier sense multiple access (CSMA) and virtual slot multiple access (VSMA) are taken into consideration in MAC sub-layer. Multilayered perceptrons neural network with backpropagation (BP) learning channel estimator algorithm compare to classic algorithm in for channel estimating. The simulation results show that the proposed neural network estimation decreases bit error rate then in MAC sub-layer throughput increases and access delay is decreased.展开更多
In this paper pilot based channel estimation is being considered for broadband power line communication (BPLC) networks witch used orthogonal frequency division multiplexing (OFDM) in order to transmit high rate data....In this paper pilot based channel estimation is being considered for broadband power line communication (BPLC) networks witch used orthogonal frequency division multiplexing (OFDM) in order to transmit high rate data. To estimate channel in time or frequency some pilot must be used. Number of these pilots and deployment of them is very important for proper estimation in different channel with varying time and frequency. Carrier sense multiple access (CSMA) and hybrid multiple access protocol are taken into consideration in MAC sub-layer. Multilayered perceptions neural network with backpropagation (BP) learning channel estimator algorithm with different pilot deployment compare to classic algorithm in for channel estimating. Simulation results show the proposed neural network estimation decreases bit error rate and therefore network throughput increases.展开更多
文摘选择性集成通过选择部分基分类器参与集成,从而提高集成分类器的泛化能力,降低预测开销.但已有的选择性集成算法普遍耗时较长,将数据挖掘的技术应用于选择性集成,提出一种基于FP-Tree(frequent pattern tree)的快速选择性集成算法:CPM-EP(coverage based pattern mining for ensemble pruning).该算法将基分类器对校验样本集的分类结果组织成一个事务数据库,从而使选择性集成问题可转化为对事务数据集的处理问题.针对所有可能的集成分类器大小,CPM-EP算法首先得到一个精简的事务数据库,并创建一棵FP-Tree树保存其内容;然后,基于该FP-Tree获得相应大小的集成分类器.在获得的所有集成分类器中,对校验样本集预测精度最高的集成分类器即为算法的输出.实验结果表明,CPM-EP算法以很低的计算开销获得优越的泛化能力,其分类器选择时间约为GASEN的1/19以及Forward-Selection的1/8,其泛化能力显著优于参与比较的其他方法,而且产生的集成分类器具有较少的基分类器.
文摘Broadband power line communication (BPLC) gained a lot of interest because of low cost and high performance communication network in access area. In this paper physical (PHY) layer and medium access control (MAC) sub-layer of BPLC are considered. Furthermore, effects of bit error rate (BER) are analyzed in MAC sub-layer. Powerful turbo convolutional code (TCC) and wideband orthogonal frequency division multiplexing (OFDM) are used in PHY layer. Carrier sense multiple access (CSMA) and virtual slot multiple access (VSMA) are taken into consideration in MAC sub-layer. Multilayered perceptrons neural network with backpropagation (BP) learning channel estimator algorithm compare to classic algorithm in for channel estimating. The simulation results show that the proposed neural network estimation decreases bit error rate then in MAC sub-layer throughput increases and access delay is decreased.
文摘In this paper pilot based channel estimation is being considered for broadband power line communication (BPLC) networks witch used orthogonal frequency division multiplexing (OFDM) in order to transmit high rate data. To estimate channel in time or frequency some pilot must be used. Number of these pilots and deployment of them is very important for proper estimation in different channel with varying time and frequency. Carrier sense multiple access (CSMA) and hybrid multiple access protocol are taken into consideration in MAC sub-layer. Multilayered perceptions neural network with backpropagation (BP) learning channel estimator algorithm with different pilot deployment compare to classic algorithm in for channel estimating. Simulation results show the proposed neural network estimation decreases bit error rate and therefore network throughput increases.
