A Subspace Blind Channel Estimation Method for Distributed MISO Systems

Abstract

We propose a subspace-based blind channel estimation method for distributed multi-input single-output systems, where one side of the link constitutes a largely spaced antenna array. The proposed approach does not necessarily require that the number of receive antennas should be no less than that of transmit antennas and reduces the computational complexity. Sufficient identifiability conditions are established for the distributed channel up to an ambiguity matrix. Simulation results demonstrate the channel estimation performance is influenced by the difference of channel delays for neighboring transmit antennas to the receive antenna, and the increase of difference can result in estimation performance improvement.

Cumulant based maximum likelihood classification for overlapped signals

“…C 4,2 is adopted for classification for CMLC. The subspace blind channel estimation (SBCE) algorithm in [6] is utilised for channel estimation and the oversampling factor is set as 10. Each point in the figures below is obtained via 100,000 Monte Carlo trails.…”

“…In CMLC, the classification process is conducted via three steps. First, the number of overlapping sources and their respective channels are estimated using the methods in [5,6]. Secondly, one composite cumulant is calculated using N symbols of the overlapped signal.…”