  Channel modeling has always been the core part in communication system design
and development, especially in 5G and 6G era. Traditional approaches like
stochastic channel modeling and ray-tracing (RT) based channel modeling depend
heavily on measurement data or simulation, which are usually expensive and time
consuming. In this paper, we propose a novel super resolution (SR) model for
generating channel characteristics data. The model is based on multi-task
learning (MTL) convolutional neural networks (CNN) with residual connection.
Experiments demonstrate that the proposed SR model could achieve excellent
performances in mean absolute error and standard deviation of error. Advantages
of the proposed model are demonstrated in comparisons with other
state-of-the-art deep learning models. Ablation study also proved the necessity
of multi-task learning and techniques in model design. The contribution in this
paper could be helpful in channel modeling, network optimization, positioning
and other wireless channel characteristics related work by largely reducing
workload of simulation or measurement.
