  In this paper, we propose a two-timescale delay-optimal base station
Discontinuous Transmission (BS-DTX) control and user scheduling for downlink
coordinated MIMO systems with energy harvesting capability. To reduce the
complexity and signaling overhead in practical systems, the BS-DTX control is
adaptive to both the energy state information (ESI) and the data queue state
information (QSI) over a longer timescale. The user scheduling is adaptive to
the ESI, the QSI and the channel state information (CSI) over a shorter
timescale. We show that the two-timescale delay-optimal control problem can be
modeled as an infinite horizon average cost Partially Observed Markov Decision
Problem (POMDP), which is well-known to be a difficult problem in general. By
using sample-path analysis and exploiting specific problem structure, we first
obtain some structural results on the optimal control policy and derive an
equivalent Bellman equation with reduced state space. To reduce the complexity
and facilitate distributed implementation, we obtain a delay-aware distributed
solution with the BS-DTX control at the BS controller (BSC) and the user
scheduling at each cluster manager (CM) using approximate dynamic programming
and distributed stochastic learning. We show that the proposed distributed
two-timescale algorithm converges almost surely. Furthermore, using queueing
theory, stochastic geometry and optimization techniques, we derive sufficient
conditions for the data queues to be stable in the coordinated MIMO network and
discuss various design insights.
