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

Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems