TY - GEN
T1 - Bayesian compressive sensing for ultra-wideband channel models
AU - Özgör, Mehmet
AU - Erküçük, Serhat
AU - Çirpan, Hakan Ali
PY - 2012
Y1 - 2012
N2 - Considering the sparse structure of ultra-wideband (UWB) channels, compressive sensing (CS) is suitable for UWB channel estimation. Among various implementations of CS, the inclusion of Bayesian framework has shown potential to improve signal recovery as statistical information related to signal parameters is considered. In this paper, we study the channel estimation performance of Bayesian CS (BCS) for various UWB channel models and noise conditions. Specifically, we investigate the effects of (i) sparse structure of standardized IEEE 802.15.4a channel models, (ii) signal-to-noise ratio (SNR) regions, and (iii) number of measurements on the BCS channel estimation performance, and compare them to the results of l 1-norm minimization based estimation, which is widely used for sparse channel estimation. The study shows that BCS exhibits superior performance at higher SNR regions only for adequate number of measurements and sparser channel models (e.g., CM1 and CM2). Based on the results of this study, BCS method or the l 1-norm minimization method can be preferred over the other for different system implementation conditions.
AB - Considering the sparse structure of ultra-wideband (UWB) channels, compressive sensing (CS) is suitable for UWB channel estimation. Among various implementations of CS, the inclusion of Bayesian framework has shown potential to improve signal recovery as statistical information related to signal parameters is considered. In this paper, we study the channel estimation performance of Bayesian CS (BCS) for various UWB channel models and noise conditions. Specifically, we investigate the effects of (i) sparse structure of standardized IEEE 802.15.4a channel models, (ii) signal-to-noise ratio (SNR) regions, and (iii) number of measurements on the BCS channel estimation performance, and compare them to the results of l 1-norm minimization based estimation, which is widely used for sparse channel estimation. The study shows that BCS exhibits superior performance at higher SNR regions only for adequate number of measurements and sparser channel models (e.g., CM1 and CM2). Based on the results of this study, BCS method or the l 1-norm minimization method can be preferred over the other for different system implementation conditions.
KW - Bayesian compressive sensing (BCS)
KW - IEEE 802.15.4a channel models
KW - l -norm minimization
KW - ultra-wideband (UWB) channel estimation
UR - http://www.scopus.com/inward/record.url?scp=84866944635&partnerID=8YFLogxK
U2 - 10.1109/TSP.2012.6256307
DO - 10.1109/TSP.2012.6256307
M3 - Conference contribution
AN - SCOPUS:84866944635
SN - 9781467311182
T3 - 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings
SP - 320
EP - 324
BT - 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings
T2 - 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012
Y2 - 3 July 2012 through 4 July 2012
ER -