The Complexity–Rate Tradeoff of Centralized Radio Access Networks


During a centralized radio access network (RAN), the signals from multiple radio access points (RAPs) are centrally processed in an exceedingly knowledge center. A centralized RAN enables advanced interference coordination methods whereas leveraging the elastic provisioning of information processing resources. It is particularly well suited to dense deployments, such as at intervals a giant building where the RAPs are connected via fiber and where many cells are underutilized. This paper considers the computational requirements of a centralized RAN with the goal of illuminating the advantages of pooling computational resources. A new analytical framework is proposed for quantifying the computational load related to the centralized processing of uplink signals in the presence of block Rayleigh fading, a distance-dependent path loss, and fractional power management. Many new performance metrics are outlined, together with the computational outage likelihood, the outage complexity, the computational gain, the computational diversity, and therefore the complexity–rate tradeoff. The validity of the analytical framework is confirmed by numerically comparing it with a simulator compliant with the 3GPP LTE normal. Using the developed metrics, it's shown that centralizing computing resources provides a better internet throughput per computational resource as compared with native processing.

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