Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning

Abstract

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).

References in corpus (2)

• Competitive Design of Multiuser MIMO Systems based on Game Theory: A Unified View
• Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning

Cited by in corpus (8)

• Dynamic Power Control for Delay-Aware Device-to-Device Communications
• Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning
• Low Complexity Delay-Constrained Beamforming for Multi-User MIMO Systems with Imperfect CSIT
• Large Deviation Delay Analysis of Queue-Aware Multi-user MIMO Systems with Multi-timescale Mobile-Driven Feedback
• Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO
• Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network
• Delay-Aware Uplink Fronthaul Allocation in Cloud Radio Access Networks
• Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems