Subspace-Based Channel Estimation for MIMO-OFDM Systems With Few Received Blocks

Cramér-Rao bound for blind channel estimation in cyclic prefixed MIMO-OFDM systems with few received symbols

“…3, we consider the case where the receiver and the transmitter has the same number, and both of them are more than single antenna: N T = 2, N R = 2. In this case, the method in [2] is not applicable due to its requirement on receive antennas to outcome transmit antennas. The available number of received blocks is N B = 8.…”

“…So far, channel estimation based on pilots is one of the most common methods, but using pilots will reduce the bandwidth efficiency. For this reason, blind channel estimation has been studied extensively [1], [2], [3], [4], [5].…”

“…In [1], an asymptotic CRB (ACRB) was derived considering usage of received CP in blind channel estimation, but it is only valid in the case of a large number of symbols. However, in recent years, several new methods have been proposed which require only a limited amount of received data with the number of received symbols raging from 1 to at most half symbol size, while providing a reasonably sound performance [2], [3]. The aforementioned CRBs consider scenarios with a large amount of received data and are not suitable for a performance bound of these methods.…”

A Low Complexity Subspace-Based Blind Channel Estimation For MIMO-OFDM

“…Kim .J .G et al proposed a blind channel estimation method based on the cyclic prefix of the signal, for an improved method without the need for continuous excitation. It can achieve better performance, but this method was applied only in SISO-OFDM system [6]. There are some subspace-based blind channel estimation algorithms based on reorganization of the time-domain signal, these methods can achieve better system accuracy and perform with fast convergence, but have high complexity [2,4,5].…”

Effective Identification of the MIMO-OFDM Space-Time-Frequency Channel

“…Some paper proposed that the MIMO channel has sparse characteristic [3], channels are individually sparse and at the same time share a common support set, therefore, few channel parameters are decomposed from generalization continuous channels. But the appropriate order of channels is a main problem, there are some papers adopting subspace algorithm to estimate the channels [4], it has quite complexity.…”