The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-knowledge on the correlation structure for arbitrarily correlated sparse signal. Numerical results verify that the propose algorithm reduces the required number of measurements for correlated sparse signal detection compared to the existing DCS algorithm.
|Title of host publication||Big Data Technologies and Applications - 7th International Conference, BDTA 2016, Proceedings|
|Editors||Jason J. Jung, Pankoo Kim|
|Number of pages||8|
|Publication status||Published - 2017|
|Event||7th International Conference on Big Data Technologies and Applications, BDTA 2016 - Seoul, Korea, Republic of|
Duration: 2016 Nov 17 → 2016 Nov 18
|Name||Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST|
|Other||7th International Conference on Big Data Technologies and Applications, BDTA 2016|
|Country/Territory||Korea, Republic of|
|Period||16/11/17 → 16/11/18|
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02037515), and (2012R1A2A2A01047554).
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications