  In this paper, we study the equalization design for multiple-input
multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)
systems with insufficient cyclic prefix (CP). In particular, the signal
detection performance is severely impaired by inter-carrier interference (ICI)
and inter-symbol interference (ISI) when the multipath delay spread exceeding
the length of CP. To tackle this problem, a deep learning-based equalizer is
proposed for approximating the maximum likelihood detection. Inspired by the
dependency between the adjacent subcarriers, a computationally efficient joint
detection scheme is developed. Employing the proposed equalizer, an iterative
receiver is also constructed and the detection performance is evaluated through
simulations over measured multipath channels. Our results reveal that the
proposed receiver can achieve significant performance improvement compared to
two traditional baseline schemes.
