We consider a cloud radio access network (C-RAN) where the baseband signal processing servers, named baseband units (BBUs) are separated from the remote radio heads (RRHs). The RRHs form a single cluster while the BBUs form a pool of resources. Each RRH may accommodate random (Poisson) or quasi-random or bursty traffic. The latter is approximated via the compound Poisson process according to which batches of calls, with a generally distributed batch size, follow a Poisson process. A call requires a computational resource and a radio resource unit from the BBUs and the serving RRH, respectively. If any of the two units is unavailable, call blocking occurs. Otherwise, the new call is accepted in the RRH. We model this C-RAN as a loss system and study two different cases: i) all RRHs accommodate bursty traffic and ii) some RRHs accommodate random traffic, some quasi-random traffic and the rest RRHs accommodate bursty traffic. In both cases, we show that a product form solution exists for the steady state probabilities and propose efficient convolution algorithms for the accurate calculation of time and call congestion probabilities. The accuracy of these algorithms is verified via simulation.

我们考虑一个云无线电接入网（C-RAN），其中基带信号处理服务器（称为基带单元（BBU））与远程无线电头端（RRH）分开。RRH形成单个群集，而BBU形成资源池。每个RRH可以容纳随机（泊松）或准随机或突发流量。后者是通过复合Poisson过程进行近似的，根据该过程，通常以批处理大小分布的一批呼叫遵循Poisson过程。呼叫需要分别来自BBU和服务RRH的计算资源和无线电资源单元。如果两个单元中的任何一个都不可用，则会发生呼叫阻塞。否则，新呼叫将在RRH中接受。我们将此C-RAN建模为损失系统，并研究两种不同的情况：i）所有RRH容纳突发流量，并且ii）一些RRH容纳随机流量，一些准随机流量，其余RRH容纳突发流量。在这两种情况下，我们都表明存在一种针对稳态概率的乘积形式解决方案，并提出了有效的卷积算法来准确计算时间和呼叫拥塞概率。通过仿真验证了这些算法的准确性。