Multi-hop relay selection is a critical issue in vehicle-to-everything networks. In previous works, the optimal hopping strategy is assumed to be based on the shortest distance. This study proposes a hopping strategy based on the lowest propagation loss, considering the effect of the environment. We use a two-step machine learning routine: improved deep encoder-decoder architecture to generate environmental maps and Q-learning to search for the multi-hopping path with the lowest propagation loss. Simulation results show that our proposed method can improve environmental recognition and extend the reachability of multi-hop communications by up to 66.7%, compared with a shortest-distance selection.
|Number of pages||5|
|Journal||IEEE Communications Letters|
|Publication status||Published - 1 Jul 2020|
- Machine learning
- multi-hop wireless communication