A Framework for Exploiting Hard and Soft LLRs for Low Complexity Decoding in VRAN Systems

Owing to improved coverage and flexibility, the radio access network (RAN) functionalities are being virtualized in a sense that the base station will merely act as a radio unit, and all the baseband processing will occur in the cloud. Therefore, the baseband signal-processing algorithms need to be designed in a way that it can match the latency requirements. In this paper, we address one of the inherent but complex issues in baseband signal processing, namely, the log-likelihood ratio (LLR) computation. In general, soft-decision rules are used for calculating the LLRs, which is computationally expensive. Thus, we attempt to exploit the benefits of hard-decision based LLRs for proposing a framework that uses soft decision only when the received symbols are closed to the decision boundary; otherwise, the framework uses hard decision. This helps us to keep the complexity low while meeting the desirable error performance. These schemes are suitable for incorporation in virtual RAN systems while considering appropriate QoS requirements.