  In this paper, a time-varying channel prediction method based on conditional
generative adversarial network (CPcGAN) is proposed for time division
duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a
discriminator to calculate the divergence between the predicted downlink
channel state information (CSI) and the real sample distributions under a
conditional constraint that is previous uplink CSI. The generator of CPcGAN
learns the function relationship between the conditional constraint and the
predicted downlink CSI and reduces the divergence between predicted CSI and
real CSI. The capability of CPcGAN fitting data distribution can capture the
time-varying and multipath characteristics of the channel well. Considering the
propagation characteristics of real channel, we further develop a channel
prediction error indicator to determine whether the generator reaches the best
state. Simulations show that the CPcGAN can obtain higher prediction accuracy
and lower system bit error rate than the existing methods under the same user
speeds.
