Abstract
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.
Highlights
Nowadays high-speed communication on low-voltage power lines is getting more attention
The simulation results show that the proposed neural network estimation decreases bit error rate in medium access control (MAC) sub-layer throughput increases and access delay is decreased
Priced broadband Internet communication to residential customers is available via cable modems and various flavors of Digital Subscriber Line (DSL)
Summary
Nowadays high-speed communication on low-voltage power lines is getting more attention. Orthogonal Frequency Multiple Access presents a good performance in BPLC channel which is time and frequency variant. Channel impulse response (CIR) for BPLC channel can be estimated using predetermined pilot symbols in real time in exchange for information rate reduction. Channel estimation with pilot is the approach in which predetermined data are transmitted at the beginning of a frame data This method has better performance than blind estimation in varying time and frequency channels [1]. In this paper we analysis three types of pilot base channel estimation, least square, minimum mean square error, and neural network. MMSE implementation needs noise and correlation computation, but it has respectable performance in time and frequency varying channel. Bold letters are chosen for notation of matrix and vector variables
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