分层小小区网络的分布式优化：一个GNEP框架
Distributed Optimization of Hierarchical Small Cell Networks: A GNEP Framework
摘要：
覆盖宏小区BS（MBS）的覆盖区域的小小区基站（SBS）的部署导致两层分层小小区网络。跨层和层间干扰不仅危害主宏小区通信，而且限制小小区通信的频谱效率。本文重点研究下行小小区网络的分布式干扰管理。我们从博弈论和网络效用最大化（NUM）的角度来解决传输策略的优化，并表明它们可以统一在一个广义纳什均衡问题（GNEP）的框架。具体地，小型小区网络设计首先被公式化为GNEP，其中SBS和MBS通过在满足全局服务质量（QoS）约束的同时最大化它们自己的速率来竞争频谱资源。我们通过变分不等式理论分析了GNEP，并提出了分布式算法，它只需要广播的一些定价信息，以实现广义纳什均衡（GNE）。然后，我们还考虑了一个非凸NUM问题，其目的是最大化的总和速率的所有BS的全局QoS约束。我们建立了NUM问题和惩罚GNEP之间的联系，并表明其固定的解决方案可以通过一个不动点迭代的GNE。我们提出了基于GNEP的分布式算法，以额外的信令开销和复杂性为代价实现了NUM问题的静态解决方案。所提出的算法的收敛性证明和保证适当选择的算法参数。所提出的GNEP框架可以通过权衡信令开销和复杂性来从QoS约束的游戏扩展到用于小小区网络的NUM设计。
英文摘要：
Deployment of small cell base stations (SBSs) overlaying the coverage area of a macrocell BS (MBS) results in a two-tier hierarchical small cell network. Cross-tier and inter-tier interference not only jeopardize primary macrocell communication but also limit the spectral efficiency of small cell communication. This paper focuses on distributed interference management for downlink small cell networks. We address the optimization of transmit strategies from both the game theoretical and the network utility maximization (NUM) perspectives and show that they can be unified in a generalized Nash equilibrium problem (GNEP) framework. Specifically, the small cell network design is first formulated as a GNEP, where the SBSs and MBS compete for the spectral resources by maximizing their own rates while satisfying global quality of service (QoS) constraints. We analyze the GNEP via variational inequality theory and propose distributed algorithms, which only require the broadcasting of some pricing information, to achieve a generalized Nash equilibrium (GNE). Then, we also consider a nonconvex NUM problem that aims to maximize the sum rate of all BSs subject to global QoS constraints. We establish the connection between the NUM problem and a penalized GNEP and show that its stationary solution can be obtained via a fixed point iteration of the GNE. We propose GNEP-based distributed algorithms that achieve a stationary solution of the NUM problem at the expense of additional signaling overhead and complexity. The convergence of the proposed algorithms is proved and guaranteed for properly chosen algorithm parameters. The proposed GNEP framework can scale from a QoS constrained game to a NUM design for small cell networks by trading off signaling overhead and complexity.
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