Experience-Driven Wireless D2D Network Link Scheduling: A Deep Learning Approach

摘要

The protocol design of device-to-device (D2D) networks have regained research interest in recent years, due to the increasing number of networking devices and the diverse deployment settings. Most of the network optimization tasks are fundamentally difficult NP-hard problems in wireless settings, because managing interference introduces combinatorial complexity. Existing approaches use general heuristic algorithms for the underlying graph problems. While efficient and simple, they are not adaptive to the changing requirement and priorities of the service providers, and make no use of the past data to recognize and exploit the information within. In this paper, we study a representative network optimization task of maximizing the throughput-based system utility through link scheduling in a single-radio, single-channel D2D networks, and propose a learning-based method to leverage past experience to generate a good scheduling policy. We combine the pattern matching capabilities provided from recurrent neural networks (RNN) and the flexibility in changing environment from reinforcement learning (RL). The algorithm is implemented with existing software frameworks and tested with numerical experiments. We find that its overall solution quality is comparable to existing heuristics with various network scales, and report an improved system throughput with significant lower computation time.