  We consider the problem of downlink channel estimation for intelligent
reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency
division multiplexing (OFDM) systems. By exploring the inherent sparse
scattering characteristics of mmWave channels, we show that the received
signals can be expressed as a low-rank third-order tensor that admits a tensor
rank decomposition, also known as canonical polyadic decomposition (CPD). A
structured CPD-based method is then developed to estimate the channel
parameters. Our analysis reveals that the training overhead required by our
proposed method is as low as O(U^2), where U denotes the sparsity of the
cascade channel. Simulation results are provided to illustrate the efficiency
of the proposed method.
