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Resource Allocation for Real-Time D2D Communications Underlaying Cellular Networks

机译:支持蜂窝网络的实时D2D通信的资源分配

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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 Malloy 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.
机译:实时设备到设备(D2D)通信对于智能运输,物联网等应用非常重要。最近,研究人员专注于提高支持蜂窝网络的D2D通信的吞吐量,而他们却忽略了实时分组传输的要求。在本文中,我们调查了实时D2D通信的资源分配问题,旨在最大程度地满足数据包截止日期的总利用率。首先,我们采用Malloy决策过程(MDP)对问题进行建模。基于该模型,我们提出了一种最优的离线信道和时隙分配算法。考虑到最佳离线算法的高时间复杂度,我们提出了一种在线联合分组接纳控制,信道和时隙分配算法。在线算法具有O(log(2)(mu))竞争性,其中mu与数据包的期限有关。我们已经证明了在线算法在所有在线算法之间的竞争比方面的最优性。另外,我们提出了一种减少在线算法悲观的方法。仿真结果表明,最优离线算法比在线算法具有更好的性能。就总效用而言,在线算法优于著名的实时任务调度算法EDF。

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