Transmit delay structure design for blind channel estimation over multipath channels
EURASIP Journal on Wireless Communications and Networking
Code combination for blind channel estimation in general MIMO-STBC systems
EURASIP Journal on Advances in Signal Processing
Blind channel estimation in orthogonally coded MIMO-OFDM systems: a semidefinite relaxation approach
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Blind recognition of linear space-time block codes: a likelihood-based approach
IEEE Transactions on Signal Processing
PARAFAC2 receivers for orthogonal space-time block codes
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Maximum-likelihood noncoherent OSTBC detection with polynomial complexity
IEEE Transactions on Wireless Communications
Efficient computation of the binary vector that maximizes a rank-deficient quadratic form
IEEE Transactions on Information Theory
Blind MIMO using the golden code
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Optimal OSTBC sequence detection over unknown correlated fading channels
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
IEEE Transactions on Signal Processing
A linear programming receiver for blind detection of full rate space-time block codes
IEEE Transactions on Signal Processing
| Hi-index | 35.76 |
Orthogonal space-time block codes (OSTBCs) have attracted much attention owing to their simple code construction, maximal diversity gain, and low maximum-likelihood (ML) detection complexity when channel state information (CSI) is available at the receiver. This paper addresses the problem of ML OSTBC detection with unknown CSI. Focusing on the binary and quaternary PSK constellations, we show that blind ML OSTBC detection can be simplified to a Boolean quadratic program (BQP). From an optimization viewpoint the BQP is still a computationally hard problem, and we propose two alternatives for dealing with this inherent complexity. First, we consider the semidefinite relaxation (SDR) approach, which leads to a suboptimal, but accurate, blind ML detection algorithm with an affordable worst-case computational cost. We also consider the sphere decoding approach, which leads to an exact blind ML detection algorithm that remains computationally expensive in the worst case, but generally incurs a reasonable average computational cost. For the two algorithms, we study implementation methods that can significantly reduce the computational complexity. Simulation results indicate that the two blind ML detection algorithms are competitive, in that the bit error performance of the two algorithms is almost the same and is noticeably better than that of some other existing blind detectors. Moreover, numerical studies show that the SDR algorithm provides better complexity performance than the sphere decoder in the worst-case sense, and vice versa in the average sense.