  Spectrum prediction is considered as a key technology to assist spectrum
decision. Despite the great efforts that have been put on the construction of
spectrum prediction, achieving accurate spectrum prediction emphasizes the need
for more advanced solutions. In this paper, we propose a new multichannel
multi-step spectrum prediction method using Transformer and stacked
bidirectional LSTM (Bi- LSTM), named TSB. Specifically, we use multi-head
attention and stacked Bi-LSTM to build a new Transformer based on
encoder-decoder architecture. The self-attention mechanism composed of multiple
layers of multi-head attention can continuously attend to all positions of the
multichannel spectrum sequences. The stacked Bi-LSTM can learn these focused
coding features by multi-head attention layer by layer. The advantage of this
fusion mode is that it can deeply capture the long-term dependence of
multichannel spectrum data. We have conducted extensive experiments on a
dataset generated by a real simulation platform. The results show that the
proposed algorithm performs better than the baselines.
