1 research outputs found
Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach
Intelligent reflecting surface (IRS) is an emerging technology for future
wireless communications including 5G and especially 6G. It consists of a large
2D array of (semi-)passive scattering elements that control the electromagnetic
properties of radio-frequency waves so that the reflected signals add
coherently at the intended receiver or destructively to reduce co-channel
interference. The promised gains of IRS-assisted communications depend on the
accuracy of the channel state information. In this paper, we address the
receiver design for an IRS-assisted multiple-input multiple-output (MIMO)
communication system via a tensor modeling approach aiming at the channel
estimation problem using supervised (pilot-assisted) methods. Considering a
structured time-domain pattern of pilots and IRS phase shifts, we present two
channel estimation methods that rely on a parallel factor (PARAFAC) tensor
modeling of the received signals. The first one has a closed-form solution
based on a Khatri-Rao factorization of the cascaded MIMO channel, by solving
rank-1 matrix approximation problems, while the second one is an iterative
alternating estimation scheme. The common feature of both methods is the
decoupling of the estimates of the involved MIMO channel matrices (base
station-IRS and IRS-user terminal), which provides performance enhancements in
comparison to competing methods that are based on unstructured LS estimates of
the cascaded channel. Design recommendations for both methods that guide the
choice of the system parameters are discussed. Numerical results show the
effectiveness of the proposed receivers, highlight the involved trade-offs, and
corroborate their superior performance compared to competing LS-based
solutions.Comment: arXiv admin note: text overlap with arXiv:2001.0655