Paper Info
Title
Spectrally Efficient CSI Acquisition for Power Line Communications: A Bayesian Compressive Sensing Perspective.
Abstract
Power line communication (PLC) techniques present a “no extra wires” solution for the communication purpose in 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, network tomography and etc. 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, 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 to the existing sparse CSI acquisition schemes for PLC, such as the annihilating filter based and the estimating signal parameters via rotational invariance technique (ESPRIT) based ones, the proposed scheme has better mean square error performance and noise robustness.
Year
DOI
Venue
2016
10.1109/JSAC.2016.2566140
IEEE Journal on Selected Areas in Communications
Keywords
Field
DocType
Bayesian compressive sensing (BCS),Power line communication (PLC),multiple input multiple output (MIMO),sparse channel state information (CSI) acquisition,spatial correlation
Frequency domain,Telecommunications,Computer science,Power-line communication,Communication channel,Robustness (computer science),Electronic engineering,Real-time computing,Network tomography,Spectral efficiency,Compressed sensing,Channel state information
Journal
Volume
Issue
ISSN
34
7
0733-8716
Citations 
PageRank 
References 
9
0.53
20
Authors
7
Name
Order
Citations
PageRank
Wenbo Ding119317.71
Yang Lu291.88
Fang Yang349068.41
Wei Dai4139091.40
Pan Li54111.95
Sicong Liu6242.50
Jian Song71404171.45