  Facing a vast amount of connections, huge performance demands, and the need
for reliable connectivity, the sixth generation of communication networks (6G)
is envisioned to implement disruptive technologies that jointly spur
connectivity, performance, and reliability. In this context, this paper
proposes, and evaluates the benefit of, a hybrid central cloud (CC) computing
and mobile edge computing (MEC) platform, especially introduced to balance the
network resources required for joint computation and communication. Consider a
hybrid cloud and MEC system, where several power-hungry multi-antenna unmanned
aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC
connectivity and relieve part of its computation burden. While the
multi-antenna base stations are connected to the cloud via capacity-limited
fronthaul links, the UAVs serve the cell-edge users with limited power and
computational capabilities. The paper then considers the problem of maximizing
the weighted network sum-rate subject to per-user delay, computational
capacity, and power constraints, so as to determine the beamforming vectors and
computation allocations. Such intricate non-convex optimization problem is
tackled using an iterative algorithm that relies on $\ell_0$-norm relaxation,
successive convex approximation, and fractional programming, and has the
compelling ability to be implemented in a distributed fashion across the
multiple UAVs and the CC. The paper results illustrate the numerical prospects
of the proposed algorithm for enabling joint communication and computation, and
highlight the appreciable improvements of data processing delays and
throughputs as compared to conventional system strategies.
