Distributed Deep Learning for Power Control in D2D Networks With Outdated Information

Traditional D2D power control methods require instantaneous interference information and so are difficult to implement in a real network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core design ideas are described as follows. First, we design linear filters with various sizes termed IFEs to extract the different local interference patterns from outdated channel information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, so as to provide more effective power allocation strategies. Second, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network. Third, an input reduction process is also designed to reduce the input size from O(N²) to O(1), which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.