基于分布式随机学习的网络MIMO系统动态分簇与功率分配
Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning
摘要：
在本文中，我们提出了一个双时标延迟最优的动态分簇和功率分配的下行网络MIMO系统的设计。动态分簇控制仅适应于全局队列状态信息（GQSI），并且在较长的时间尺度上在基站控制器（BSC）处计算。另一方面，一个集群中的所有BS的功率分配适应于集群内信道状态信息（CCSI）和集群内队列状态信息（CQSI）两者，并且在集群管理器（CM）处在较短的时间尺度上计算。我们证明了两时标时滞最优控制可以表示为一个无限时域平均费用约束部分观测马尔可夫决策过程（CPOMDP）。通过利用特殊的问题结构，我们将推导出一个等价的Bellman方程的模式选择Q因子来解决CPOMDP。为了解决分布式需求和指数存储器需求和计算复杂性的问题，我们近似的模式选择Q因子的每集群势函数的总和，并提出了一种新的分布式在线学习算法来估计每集群势函数（在每个CM）以及拉格朗日乘子（LM）（在每个BS）。我们表明，建议的分布式在线学习算法几乎肯定收敛（概率为1）。通过利用队列动力学的生灭结构，我们进一步将每簇势函数分解为每簇每用户势函数之和，并将瞬时功率分配公式化为所有CM之间的每阶段QSI感知干扰博弈。我们还提出了一个QSI感知的同时迭代注水算法（QSIWFA），并表明它可以实现纳什均衡（NE）。
英文摘要：
In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and computed at the base station controller (BSC) over a longer time scale. On the other hand, the power allocations of all the BSs in one cluster are adaptive to both intra-cluster channel state information (CCSI) and intra-cluster queue state information (CQSI), and computed at the cluster manager (CM) over a shorter time scale. We show that the two-timescale delay-optimal control can be formulated as an infinite-horizon average cost Constrained Partially Observed Markov Decision Process (CPOMDP). By exploiting the special problem structure, we shall derive an equivalent Bellman equation in terms of Pattern Selection Q-factor to solve the CPOMDP. To address the distributive requirement and the issue of exponential memory requirement and computational complexity, we approximate the Pattern Selection Q-factor by the sum of Per-cluster Potential functions and propose a novel distributive online learning algorithm to estimate the Per-cluster Potential functions (at each CM) as well as the Lagrange multipliers (LM) (at each BS). We show that the proposed distributive online learning algorithm converges almost surely (with probability 1). By exploiting the birth-death structure of the queue dynamics, we further decompose the Per-cluster Potential function into sum of Per-cluster Per-user Potential functions and formulate the instantaneous power allocation as a Per-stage QSI-aware Interference Game played among all the CMs. We also propose a QSI-aware Simultaneous Iterative Water-filling Algorithm (QSIWFA) and show that it can achieve the Nash Equilibrium (NE).
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