  In this paper, we consider a joint unicast and multi-group multicast
cell-free distributed massive multiple-input multiple-output (MIMO) system,
while accounting for co-pilot assignment strategy based channel estimation,
pilot contamination and different precoding schemes. Under the co-pilot
assignment strategy, we derive the minimum-mean-square error (MMSE) channel
state information (CSI) estimation for unicast and multicast users. Given the
acquired CSI, the closed-form expressions for downlink achievable rates with
maximum ratio transmission (MRT), zero-forcing (ZF) and MMSE beamforming are
derived. Based on these expressions, we propose an efficient power allocation
scheme by solving a multi-objective optimization problem (MOOP) between
maximizing the minimum spectral efficiency (SE) of multicast users and
maximizing the average SE of unicast users with non-dominated sorting genetic
algorithm II (NSGA-II). Moreover, the MOOP is converted into a deep learning
(DL) problem and solved by an unsupervised learning method to further promote
computational efficiency. Numerical results verify the accuracy of the derived
closed-form expressions and the effectiveness of the joint unicast and
multigroup multicast transmission scheme in cell-free distributed massive MIMO
systems. The SE analysis under various system parameters and the trade-off
regions between these two conflicting optimization objectives offers numerous
flexibilities for system optimization.
