2 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
A Tensor-based Approach to Joint Channel Estimation / Data Detection in Flexible Multicarrier MIMO Systems
Filter bank-based multicarrier (FBMC) systems have attracted increasing
attention recently in view of their many advantages over the classical cyclic
prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM)
modulation. However, their more advanced structure (resulting in, for example,
self interference) complicates signal processing tasks at the receiver,
including synchronization, channel estimation and equalization. In a
multiple-input multiple-output (MIMO) configuration, the multi-antenna
interference has also to be taken into account. (Semi-) blind receivers, of
increasing interest in (massive) MIMO systems, have been little studied so far
for FBMC and mainly for the single-antenna case only. The design of such
receivers for flexible MIMO FBMC systems, unifying a number of existing FBMC
schemes, is considered in this paper through a tensor-based approach, which is
shown to encompass existing joint channel estimation and data detection
approaches as special cases, adding to their understanding and paving the way
to further developments. Simulation-based results are included, for realistic
transmission models, demonstrating the estimation and detection performance
gains from the adoption of these receivers over their training only-based
counterparts.Comment: arXiv admin note: text overlap with arXiv:1609.0966