  To improve the poor performance of distributed operation and non-scalability
of centralized operation in traditional cell-free massive MIMO, we propose a
cell-free distributed collaborative (CFDC) massive multiple-input
multiple-output (MIMO) system based on a novel two-layer model to take
advantages of the distributed cloud-edge-end collaborative architecture in
beyond 5G (B5G) internet of things (IoT) environment to provide strong
flexibility and scalability. We further ultilize the proposed CFDC massive MIMO
system to support the low altitude three-dimensional (3-D) coverage scenario
with unmanned aerial vehicles (UAVs), while accounting for 3-D Rician channel
estimation, user-centric association and different scalable receiving schemes.
Since coexisted UAVs and ground users (GUEs) cause greater interference, we
ultilize user-centric association strategy and minimum-mean-square error (MMSE)
channel state information (CSI) estimation to obtain the estimated CSI of UAVs
and GUEs. Under the CFDC scenarios, scalable receiving schemes as maximum ratio
combing (MRC), partial zero-forcing (P-ZF) and partial minimum-mean-square
error (P-MMSE) can be performed at edge servers and the closed-form expressions
for uplink spectral efficiency (SE) are derived. Based on the derived
expressions, we propose an efficient power control algorithm by solving a
multi-objective optimization problem (MOOP) between maximizing the average SE
of UAVs and GUEs simultaneously with Deep Q-Network (DQN). Numerical results
verify the accuracy of the derived closed-form expressions and the
effectiveness of the coexisted UAVs and GUEs transmission scheme in CFDC
massive MIMO systems. The SE analysis under various system parameters offers
numerous flexibilities for system optimization.
