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Research on cross-layer design and optimization algorithm of network robot 5G multimedia sensor network

机译:网络机器人5G多媒体传感器网络跨层设计与优化算法研究

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Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.
机译:基于最大化网络机器人5G多媒体传感器网络的跨层优化是无线网络跨层设计的系统方法。它摘要协议栈中的图层的功能和性能要求到客观函数和数学优化问题中的约束。在本文中,研究了使用多无线电接口多通道技术的无线网状网络的横向优化问题。基于网络实用程序最大化方法建模优化问题,提出了相应的算法。基于随机网络实用程序的最大化方法,建立了网络机器人5G多媒体传感器网络的跨层优化模型。针对网络机器人5G多媒体传感器网络中随机数据流量和无线传播环境的时变随机性,提出了一种基于机会约束编程的联合拥塞控制和功率控制模型,其遗传算法用于验证。改革研究将有助于加快该领域的实际步伐,具有一定的理论前瞻性和实用价值。

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