Modern telecommunication and mobile networks are increasingly complex from a resource management perspective, with diverse combinations of software and infrastructure elements that need to be configured and tuned for efficient operation with high quality of service. Increased real-time automation at all levels and time-frames is a critical tool in controlling this complexity. A key component in automation is practical and accurate simulation methods that can be used in live traffic scenarios. This paper introduces a new method with supporting algorithms for sampling key parameters from live or recorded traffic which can be used to generate large volumes of synthetic traffic with very similar rate distributions and temporal characteristics. Multiple spatial renewal processes are used to generate fractional Gaussian noise, which is scaled and transformed into a log-normal rate distribution with discrete arrival events, fitted to the properties observed in given recorded traces. This approach works well for modelling large user aggregates but is especially useful for medium sized and relatively small aggregates, where existing methods struggle to reproduce the most important properties of recorded traces. The technique is demonstrated through experimental comparisons with data collected from an operational LTE network to be highly useful in supporting self-learning and automation algorithms which can ultimately reduce complexity, increase energy efficiency, and reduce total network operation costs.