Resource Allocation for Real-Time D2D Communications Underlaying Cellular Networks

Real-time device to device (D2D) communications are important for applications of intelligent transportation, Internet of Things, etc. Most recently, researchers have focused on improving the throughput of the D2D communications underlaying cellular networks, while they have ignored the real-time requirements of packet transmissions. In this paper, we investigate the resource allocation problems for real-time D2D communications aiming to maximize the total utility of packets meeting their deadlines. First, we adopt the Markov Decision Process (MDP) to model the problem. Based on this model, we propose an optimal offline channel and slot allocation algorithm. Considering the high time complexity of the optimal offline algorithm, we then propose an online joint packet admission control, channel, and slot assignment algorithm. The online algorithm is $O(\log _2(\mu))$ -competitive, where $\mu$ is related to the deadlines of packets. We have proved the optimality of the online algorithm in terms of the competitive ratio among all of the online algorithms. Additionally, we have proposed a method to reduce the pessimism of the online algorithm. Simulation results show that the optimal offline algorithm achieves better performance than the online algorithm. The online algorithm outperforms the well-known real-time task scheduling algorithm EDF in terms of the total utility.