The high-speed railway wireless communication network architecture is gradually transforming from a traditional GSM-Railway (GSM-R) cellular network to a 5G heterogeneous ultra-dense communication network. How to effectively deal with the interference while obtaining the capacity gain has become an inevitable problem. This article proposes a power allocation interference alignment algorithm based on game equilibrium with the goal of joint optimization about throughput and energy efficiency for imperfect channels in high-speed railway communication. Firstly, the imperfect Channel State Information including delay and fading is calculated. Then, in order to improve system throughput and energy efficiency, we establish a game model and prove the existence of Nash equilibrium in the model. At the same time, a power allocation iterative algorithm based on Recurrent Neural Network is proposed. Finally, combined with the interference alignment algorithm based on a maximal signal-to-noise ratio, the optimal power matrix is calculated by iteration to achieve interference management optimization. The simulation results prove that our algorithm has superior performance in improving the system throughput, energy efficiency and transmission reliability in high-speed railway wireless communication with imperfect channels.

中文翻译：

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基于博弈论的HSR 5G异构超密集网络多目标优化干扰对齐算法

高铁无线通信网络架构正逐步从传统的GSM-Railway（GSM-R）蜂窝网络向5G异构超密集通信网络转变。如何在获得容量增益的同时有效处理干扰成为一个不可避免的问题。本文提出了一种基于博弈均衡的功率分配干扰对齐算法，目标是在高速铁路通信中对不完善信道的吞吐量和能量效率进行联合优化。首先，计算不完善的信道状态信息，包括延迟和衰落。然后，为了提高系统吞吐量和能源效率，我们建立了博弈模型并证明了模型中纳什均衡的存在。同时，提出了一种基于循环神经网络的功率分配迭代算法。最后结合基于最大信噪比的干扰对齐算法，通过迭代计算最优功率矩阵，实现干扰管理优化。仿真结果证明，本文算法在提高高铁无线通信信道不完善的系统吞吐量、能量效率和传输可靠性方面具有优越的性能。