Abstract
Power line communication (PLC) techniques present a no extra wire solution for the communication purpose in a smart grid due to the ubiquity and low cost. Moreover, the through-the-grid property of PLC has naturally extended its possible applications, including but not limited to the automatic meter reading, line quality monitoring, online diagnostics, and network tomography. To guarantee the performance of communications as well as other applications in PLC systems, accurate channel state information (CSI) acquisition should be performed regularly. However, the conventional pilot-based CSI acquisition approaches in PLC systems have not made full use of the channel characteristics and hence suffer from a low spectral efficiency. In this paper, by exploiting the parametric sparsity and discretizing the electrical length in the well-known PLC channel model, we formulate the non-sparse (either time domain or frequency domain) PLC channel into a compressive sensing (CS) applicable problem. Furthermore, we propose a spectrally efficient CSI acquisition scheme under the framework of Bayesian CS and extend it to the multiple-input multiple-output PLC by investigating the channel spatial correlation. Compared with the existing sparse CSI acquisition schemes for PLC, such as the annihilating filter-based and the estimating signal parameters via rotational invariance technique-based ones, the proposed scheme has better mean square error performance and noise robustness.
Published Version
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