No abstract

System-level simulations have become an indispensable tool for predicting the behavior of wireless cellular systems. As exact link-level modeling is unfeasible due to its huge complexity, mathematical abstraction is required to obtain equivalent results by less complexity. A particular problem in such approaches is the modeling of multiple coherent transmissions. Those arise in multiple-input-multipleoutput transmissions at every base station but nowadays so-called coordinated multipoint (CoMP) techniques have become very popular, allowing to allocate two or more spatially separated transmission points. Also, multimedia broadcast single frequency networks (MBSFNs) have been introduced recently in long-term evolution (LTE), which enables efficient broadcasting transmission suitable for spreading information that has a high user demand as well as simultaneously sending updates to a large number of devices. This paper introduces the concept of runtime-precoding, which allows to accurately abstract many coherent transmission schemes while keeping additional complexity at a minimum. We explain its implementation and advantages. For validation, we incorporate the runtime-precoding functionality into the Vienna LTE-A downlink systemlevel simulator, which is an open source tool, freely available under an academic noncommercial use license. We measure simulation run times and compare them against the legacy approach as well as link-level simulations. Furthermore, we present multiple application examples in the context of intrasite and intersite CoMP for train communications and MBSFN.INDEX TERMS Link abstraction, link quality model, runtime-precoding, 3GPP, LTE-A, Vienna LTE-A downlink system level simulator, MIESM, Vienna LTE-A downlink link level simulator, LTE transmission modes, coordinated multipoint, multimedia broadcast single frequency networks, high-user mobility.

We propose a fast labeled multi-Bernoulli (LMB) filter that uses belief propagation for probabilistic data association. The complexity of our filter scales only linearly in the numbers of Bernoulli components and measurements, while the performance is comparable to or better than that of the Gibbs sampler-based LMB filter.