We experimentally demonstrated the use of intelligent impairment equalization (IIE) for microwave downconversion link linearization in noncooperative systems. Such an equalizer is realized based on an artificial neural network (ANN). Once the training process is completed, the inverse link transfer function can be determined. With the inverse transformation for the detected signal after transmission, the third-order intermodulation distortion components are suppressed significantly without requiring any prior information from an input RF signal. Furthermore, fast training speed is achieved, since the configuration of ANN-based equalizer is simple. Experimental results show that the spurious-free dynamic range of the proposed link is improved to 106.5 dB · Hz^2/3, which is 11.3 dB higher than that of a link without IIE. Meanwhile, the training epochs reduce to only five, which has the potential to meet the practical engineering requirement.

An adaptive digital backward propagation (ADBP) algorithm is proposed and experimentally demonstrated based on the variance of the intensity noise. The proposed algorithm can self-determine the unknown nonlinear coefficient γ and the nonlinear compensation parameter ξ. Compared to the scheme based on the variance of phase noise, the proposed algorithm can avoid the repeated frequency offset compensation and carrier phase recovery. The simulation results show that the system’s performance compensated by the proposed method is comparable to conventional ADBP schemes. The performance of the proposed algorithm is simulated in 40/112 Gb/s polarization-division multiplexing (PDM)-quadrature phase-shift keying (QPSK) and 224 Gb/s PDM-16-quadrature amplitude modulation (QAM) systems and further experimentally verified in a 40 Gb/s PDM-QPSK coherent optical communication system over a 720 km single-mode fiber.