Wireless-Powered Machine-to-Machine Multicasting in Cellular Networks
Wireless-Powered Machine-to-Machine Multicasting in Cellular Networks
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DOI：
10.1109/tgcn.2020.2986216
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发表时间：
2020-04
影响因子：
4.8
通讯作者：
A. Almasoud;A. Kamal
中科院分区：
文献类型：
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作者：
A. Almasoud;A. Kamal
在未来的蜂窝网络中，预计数据流量将由于大量物联网（IoT）对象的部署而显著增加。IoT对象在蜂窝网络下操作，并且它们使用机器对机器（M2M）通信来传输多播消息。我们建议使用射频（RF）能量发射器（ET）来补偿物联网对象在转发多播消息时消耗的能量。我们的目标是支持物联网对象的多播服务，并向它们传输能量，从而使物联网传输的总能量最小化。我们制定的问题数学上作为一个非凸的混合非线性规划（MINLP）。由于最优解的困难，我们将原问题分解成两个子问题，使用广义Bender分解连续凸规划（GBD-SCP）。虽然这种方法有助于找到问题的解决方案，但由于二进制变量，问题仍然很难。因此，我们提出了约束分解与SCP和二进制变量松弛（CDR）算法来解决这个问题更有效。仿真结果表明，当网络规模较大时，该算法的性能接近GBD-SCP算法，而计算时间明显减少。
In future cellular networks, it is expected that data traffic will increase significantly due to deployments of large numbers of Internet of Things (IoT) objects. The IoT objects operate underlaying a cellular network, and they use Machine-to-Machine (M2M) communication to transmit multicst messages. We propose to use Radio Frequency (RF) Energy Transmitters (ET) to compensate the IoT objects with the energy consumed in forwarding multicast messages. Our goal is to support multicast service for IoT objects and transmit energy to them such that the total transferred energy by the ETs is minimized. We formulated the problem mathematically as a non-convex Mixed Integer Nonlinear Program (MINLP). Due to the difficulty of solving the problem optimally, we decompose the original problem into two sub-problems using Generalized Bender Decomposition with Successive Convex programming (GBD-SCP). Although this method facilitates finding a solution for the problem, the problem is still hard due to binary variables. Hence, we propose the Constraints Decomposition with SCP and Binary Variable Relaxation (CDR) algorithm to solve the problem more efficiently. Simulation results show that the proposed algorithm achieves a performance close to the GBD-SCP algorithm while the computation time is reduced significantly when the network size is larger.