  Multiple-input multiple-output orthogonal frequency division multiplexing
(MIMO-OFDM), a fundamental transmission scheme, promises high throughput and
robustness against multipath fading. However, these benefits rely on the
efficient detection strategy at the receiver and come at the expense of the
extra bandwidth consumed by the cyclic prefix (CP). We use the iterative
orthogonal approximate message passing (OAMP) algorithm in this paper as the
prototype of the detector because of its remarkable potential for interference
suppression. However, OAMP is computationally expensive for the matrix
inversion per iteration. We replace the matrix inversion with the conjugate
gradient (CG) method to reduce the complexity of OAMP. We further unfold the
CG-based OAMP algorithm into a network and tune the critical parameters through
deep learning (DL) to enhance detection performance. Simulation results and
complexity analysis show that the proposed scheme has significant gain over
other iterative detection methods and exhibits comparable performance to the
state-of-the-art DL-based detector at a reduced computational cost.
Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver
architecture to remove the CP overhead. This architecture first eliminates the
intersymbol interference by buffering the previously recovered data and then
detects the signal using the proposed detector. Numerical experiments
demonstrate that the designed receiver offers a higher spectral efficiency than
traditional receivers. Finally, over-the-air tests verify the effectiveness and
robustness of the proposed scheme in realistic environments.
