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In the Cloud Radio Access Network (C-RAN) architecture, the baseband signalsfrom multiple remote radio heads are processed in a centralized baseband unit(BBU) pool. This architecture allows network operators to adapt the BBU'scomputational resources to the aggregate access load experienced at the BBU,which can change in every air-interface access frame. The degree of savingsthat can be achieved by adapting the resources is a tradeoff between savings,adaptation frequency, and increased queuing time. If the time scale foradaptation of the resource multiplexing is greater than the access frameduration, then this may result in additional access latency and limit theenergy savings. In this paper we investigate the tradeoff by considering twoextreme time-scales for the resource multiplexing: (i) long-term, where thecomputational resources are adapted over periods much larger than the accessframe durations; (ii) short-term, where the adaption is below the access frameduration. We develop a general C-RAN queuing model that describes the accesslatency and show, for Poisson arrivals, that long-term multiplexing achievessavings comparable to short-term multiplexing, while offering lowimplementation complexity.

Statistical Multiplexing of Computations in C-RAN with Tradeoffs in Latency and Energy