  Limited capacity of fronthaul links in a cell-free massive multiple-input
multiple-output (MIMO) system can cause quantization errors at a central
processing unit (CPU) during data transmission, complicating the centralized
rate optimization problem. Addressing this challenge, we propose a harmony
search (HS)-based algorithm that renders the combinatorial non-convex problem
tractable. One of the distinctive features of our algorithm is its hierarchical
structure: it first allocates resources at the access point (AP) level and
subsequently optimizes for user equipment (UE), ensuring a more efficient and
structured approach to resource allocation. Our proposed algorithm deals with
rigorous conditions, such as asymmetric fronthaul bit allocation and distinct
quantization error levels at each AP, which were not considered in previous
works. We derive a closed-form expression of signal-to-interference-plusnoise
ratio (SINR), in which additive quantization noise model (AQNM) based
distortion error is taken into account, to define the mathematical expression
of spectral efficiency (SE) for each UE. Also, we provide analyses on
computational complexity and convergence to investigate the practicality of
proposed algorithm. By leveraging various performance metrics such as total SE
and max-min fairness, we demonstrate that the proposed algorithm can adaptively
optimize the fronthaul bit allocation depending on system requirements.
Finally, simulation results show that the proposed algorithm can achieve
satisfactory performance while maintaining low computational complexity, as
compared to the exhaustive search method
