TY - GEN
T1 - A Gauss-Seidel approach to precoding design for joint transmission of distributed correlated sources
AU - Fang, Jun
AU - Li, Hongbin
PY - 2012
Y1 - 2012
N2 - We consider the problem of transmitting multiple spatially distributed correlated sources to a common destination (e.g. a fusion center or an access point) in wireless sensor networks (WSNs). The correlated data from multiple sensors are jointly transmitted to the destination via orthogonal channels. We assume that the channel between each sensor and the receiver is multiple-input multiple-output (MIMO), with each sensor and the receiver equipped with multiple transmit/receive antennas. In this framework, we study the problem of joint design of linear precoders for all sensors by assuming the knowledge of the instantaneous channel state information (CSI), with the objective of maximizing the mutual information between the sources and the destination. We propose a Gauss-Seidel iterative approach which successively optimizes the precoding matrix associated with each sensor, while fixing the other precoding matrices. Numerical results show that the proposed algorithm that takes into account the spatial correlations across sensors can achieve higher capacity than conventional methods that neglect the spatial correlations.
AB - We consider the problem of transmitting multiple spatially distributed correlated sources to a common destination (e.g. a fusion center or an access point) in wireless sensor networks (WSNs). The correlated data from multiple sensors are jointly transmitted to the destination via orthogonal channels. We assume that the channel between each sensor and the receiver is multiple-input multiple-output (MIMO), with each sensor and the receiver equipped with multiple transmit/receive antennas. In this framework, we study the problem of joint design of linear precoders for all sensors by assuming the knowledge of the instantaneous channel state information (CSI), with the objective of maximizing the mutual information between the sources and the destination. We propose a Gauss-Seidel iterative approach which successively optimizes the precoding matrix associated with each sensor, while fixing the other precoding matrices. Numerical results show that the proposed algorithm that takes into account the spatial correlations across sensors can achieve higher capacity than conventional methods that neglect the spatial correlations.
UR - http://www.scopus.com/inward/record.url?scp=84876206436&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876206436&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2012.6489276
DO - 10.1109/ACSSC.2012.6489276
M3 - Conference contribution
AN - SCOPUS:84876206436
SN - 9781467350518
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1496
EP - 1500
BT - Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
T2 - 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Y2 - 4 November 2012 through 7 November 2012
ER -