1 research outputs found
Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems
We consider downlink (DL) channel estimation for frequency division duplex
based massive MIMO systems under the multipath model. Our goal is to provide
fast and accurate channel estimation from a small amount of DL training
overhead. Prior art tackles this problem using compressive sensing or classic
array processing techniques (e.g., ESPRIT and MUSIC). However, these methods
have challenges in some scenarios, e.g., when the number of paths is greater
than the number of receive antennas. Tensor factorization methods can also be
used to handle such challenging cases, but it is hard to solve the associated
optimization problems. In this work, we propose an efficient channel estimation
framework to circumvent such difficulties. Specifically, a structural training
sequence that imposes a tensor structure on the received signal is proposed. We
show that with such a training sequence, the parameters of DL MIMO channels can
be provably identified even when the number of paths largely exceeds the number
of receive antennas---under very small training overhead. Our approach is a
judicious combination of Vandermonde tensor algebra and a carefully designed
conjugate-invariant training sequence. Unlike existing tensor-based channel
estimation methods that involve hard optimization problems, the proposed
approach consists of very lightweight algebraic operations, and thus real-time
implementation is within reach. Simulation results are carried out to showcase
the effectiveness of the proposed methods