  Network-assisted full-duplex (NAFD) distributed massive multiple input
multiple output (M-MIMO) enables the in-band full-duplex with existing
half-duplex devices at the network level, which exceptionally improves spectral
efficiency. This paper analyzes the impact of low-resolution analog-to-digital
converters (ADCs) on NAFD distributed M-MIMO and designs an efficient bit
allocation algorithm for low-resolution ADCs. The beamforming training
mechanism relieves the heavy pilot overhead for channel estimation, which
remarkably enhances system performance by guiding the interference cancellation
and coherence detection. Furthermore, closed-form expressions for spectral and
energy efficiency with low-resolution ADCs are derived. The multi-objective
optimization problem (MOOP) for spectral and energy efficiency is solved by the
deep Q network and the non-dominated sorting genetic algorithm II. The
simulation results corroborate the theoretical derivation and verify the
effectiveness of introducing low-resolution ADCs in NAFD distributed M-MIMO
systems. Meanwhile, a set of Pareto-optimal solutions for ADC accuracy flexibly
provide guidelines for deploying in a practical NAFD distributed M-MIMO system.
