  With the successful application of deep learning in communications systems,
deep neural networks are becoming the preferred method for signal
classification. Although these models yield impressive results, they often come
with high computational complexity and large model sizes, which hinders their
practical deployment in communication systems. To address this challenge, we
propose a novel layer pruning method. Specifically, we decompose the model into
several consecutive blocks, each containing consecutive layers with similar
semantics. Then, we identify layers that need to be preserved within each block
based on their contribution. Finally, we reassemble the pruned blocks and
fine-tune the compact model. Extensive experiments on five datasets demonstrate
the efficiency and effectiveness of our method over a variety of
state-of-the-art baselines, including layer pruning and channel pruning
methods.
