QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach
QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach
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DOI：
10.1109/tvt.2017.2682236
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发表时间：
2017-03
影响因子：
6.8
通讯作者：
Ning Zhang;Shan Zhang;Jianchao Zheng;Xiaojie Fang;J. Mark;X. Shen
中科院分区：
文献类型：
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作者：
Ning Zhang;Shan Zhang;Jianchao Zheng;Xiaojie Fang;J. Mark;X. Shen
本文研究了频谱共享，以在5G网络中提供更好的体验质量，其特征在于频谱，小区和用户需求方面的多维异构性。具体而言，频谱接入，功率分配和用户调度联合调查和优化问题，制定了最大限度地提高用户的满意度在整个网络的目标。为了降低复杂性和开销，需要具有本地信息的分散式解决方案。为此，我们采用博弈论的方法和干涉图来解决这个问题。所提出的游戏被证明有至少一个纳什均衡（NE），对应于全局或局部最优解的原始优化问题。一个并发的最佳响应迭代算法首先设计找到的解决方案，它可以收敛到NE，但可能不是全局最优的。因此，空间自适应播放迭代（SAPI）学习算法进一步提出了搜索全局最优。理论分析表明，当学习步长足够大时，SAPI算法可以保证以任意大的概率找到全局最优解。仿真结果验证了所提出算法的性能。
This paper studies spectrum sharing for providing better quality of experience in 5G networks, which are characterized by multidimensional heterogeneity in terms of spectrum, cells, and user requirements. Specifically, spectrum access, power allocation, and user scheduling are jointly investigated and an optimization problem is formulated with the objective of maximizing the users’ satisfaction across the network. In order to reduce the complexity and overhead, decentralized solutions with local information are required. To this end, we employ game-theoretic approach and interference graph to solve the problem. The proposed game is proved to have at least one Nash Equilibrium (NE), corresponding to either the globally or locally optimal solution to the original optimization problem. A concurrent best-response iterative algorithm is first devised to find the solution, which can converge to an NE, but may not be globally optimal. Therefore, a spatial adaptive play iterative (SAPI) learning algorithm is further proposed to search the global optimum. Theoretical analysis demonstrates that the SAPI algorithm can guarantee to find the globally optimal solution with an arbitrary large probability, when the learning step is set to be sufficiently large. Simulation results are provided to validate the performance of the proposed algorithms.