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how to design the deep learning structure to construct the routing table on a GPU-accelerated SDR. First, we present the detailed characterization of the input and output of the deep learning structure, then we describe our chosen architecture, DBA. Next, how the proposed routing table construction method works on a GPU-accelerated SDR is discussed.neighbors. Then, every router can utilize the information to compute the next nodes for sending data packets to the destination routers. This method works well in most cases since every router can make the best decision according to the obtained information of all the network links. However, when some routers in the network are congested because of the overwhelming traffic demand, conventional methods to compute the next nodes suffer from slow convergence. At the same time, the periodical signaling exchange aggravates the traffic congestion. Furthermore, the traditional routing methods are unable to deal with scenarios where the network environment continues to become more complex, which requires the network operators to consider various unrelated parameters to determine the routing rules. As the deep learning method has been applied to many complex activities to automatically explore the relationships among various inputs, we attempt to adopt deep learning for routing in the remainder of this section. Since the traffic pattern observed at each router is a direct indication of the traffic situation of that router, we adopt traffic patterns as the input of our deep learning model. As mentioned in Section 1, the deep learning structure is utilized to compute the routing path. Therefore, we choose the routing path as the output of the model. Accordingly, Fig. 3a demonstrates that the traffic pattern is served as the input to the deep learning structure and processed for the routing path decision as the output.