Recent advancements in machine learning for communications show that channel autoencoders could revolutionize conventional communication systems through end-to-end optimization. For channel autoencoders to reliably transmit over the air, a scheme to enable adaptive use of resources is needed. Thus, we propose ConvAE-Advanced, an improved channel autoencoder structure that can adaptively transmit across multiple timeslots. ConvAE-Advanced utilizes an unexploited input dimension in ConvAE by the use of the resource-aware residual block and whole resource power normalization. This enabled ConvAE-Advanced to adaptively transmit information according to channel conditions. Simulations for a 2-by-2 multiple-input multiple-output system under the WINNER2 A1 scenario shows that ConvAE-Advanced outperforms ConvAE across all SNR ranges. Most importantly, ConvAE-Advanced can achieve a better BER and achievable rate performance without additional wireless resource usage compared to ConvAE.