  Cloud-radio access network (C-RAN) can enable cell-less operation by
connecting distributed remote radio heads (RRHs) via fronthaul links to a
powerful central unit. In conventional C-RAN, baseband signals are forwarded
after quantization/ compression to the central unit for centralized processing
to keep the complexity of the RRHs low. However, the limited capacity of the
fronthaul is thought to be a significant bottleneck in the ability of C-RAN to
support large systems (e.g. massive machine-type communications (mMTC)).
Therefore, in contrast to the conventional C-RAN, we propose a learning-based
system in which the detection is performed locally at each RRH and only the
likelihood information is conveyed to the CU. To this end, we develop a general
set-theoretic learningmethod to estimate likelihood functions. The method can
be used to extend existing detection methods to the C-RAN setting.
