Fast Topology Inference of Wireless Networks Based on Hawkes Process

Based on the reasonable equivalent assumption of communication events, using the Hawkes process to model the information interaction in wireless communication networks is an emerging direction in the field of non-cooperative topology inference. At present, topology inference algorithms based on the Hawkes process mainly use a fixed sample size for inference, considering only its reliability, but not regarding its effectiveness. In this paper, we consider introducing a sample size as a new performance indicator. For small sample size scenarios in wireless networks, a kind of fast topology inference algorithm is proposed, which uniformly represents parameters belonging to different dimensions, and thoroughly mines topological information from different batches to increase the speed and effectiveness of inference. Experimental simulations show that compared with the existing algorithm, our algorithm has better performance in small sample size scenarios.