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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Optimal Resource Allocation in Multicast Device-to-Device Communications Underlaying LTE Networks
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Optimal Resource Allocation in Multicast Device-to-Device Communications Underlaying LTE Networks

机译:支持LTE网络的组播设备间通信中的最佳资源分配

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摘要

In this paper, we present a framework for resource allocations for multicast device-to-device (D2D) communications underlaying the uplink of a Long-Term Evolution (LTE) network. The objective is to maximize the sum throughput of active cellular users (CUs) and feasible D2D multicast groups in a cell, while meeting a certain signal-to-interference-plus-noise ratio (SINR) constraint for both the CUs and the D2D groups. We formulate the general problem of power and channel allocation as a mixed integer nonlinear programming (MINLP) problem, where one D2D group can reuse the channels of multiple CUs and where the channel of each CU can be reused by multiple D2D groups. Distinct from existing approaches in the literature, our formulation and solution methods provide an effective and flexible means to utilize radio resources in cellular networks and share them with multicast groups without causing harmful interference to each other. The MINLP problem is transformed so that it can be solved optimally by a variant of the generalized Bender decomposition method with provable convergence. A greedy algorithm and a low-complexity heuristic solution are then devised. The performance of all schemes is evaluated through extensive simulations. Numerical results demonstrate that the proposed greedy algorithm can achieve close-to-optimal performance and that the heuristic algorithm provides good performance, even though it is inferior than that of the greedy, with much lower complexity.
机译:在本文中,我们提出了一个用于长期演进(LTE)网络上行链路的多播设备到设备(D2D)通信资源分配的框架。目的是最大化活动蜂窝用户(CU)和小区中可行的D2D多播组的总吞吐量,同时满足CU和D2D组的特定信号干扰加噪声比(SINR)约束。我们将功率和信道分配的一般问题表述为混合整数非线性规划(MINLP)问题,其中一个D2D组可以重用多个CU的信道,而每个CU的信道可以被多个D2D组重用。与文献中的现有方法不同,我们的制定和解决方案方法提供了一种有效且灵活的方法来利用蜂窝网络中的无线电资源并与多播组共享它们,而不会造成彼此的有害干扰。对MINLP问题进行了转换,以便可以通过具有可证明收敛性的广义Bender分解方法的一种变体来最佳地解决它。然后设计了贪婪算法和低复杂度启发式解决方案。所有方案的性能均通过广泛的仿真进行评估。数值结果表明,所提出的贪婪算法可以达到接近最佳的性能,并且启发式算法即使不如贪婪算法,也具有良好的性能,且复杂度低得多。

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