Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix
This addresses interference issues in wireless communication systems, but it is incremental as it builds on existing deep learning methods for a specific domain problem.
The paper tackled the problem of signal detection in MIMO-OFDM systems with insufficient cyclic prefix, which causes interference, by proposing a deep learning-based equalizer that approximates maximum likelihood detection. The results showed significant performance improvement over traditional baseline schemes in simulations over measured channels.
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.