Analyzing synchronous dataflow scenarios for dynamic software-defined radio applications

Contemporary embedded systems for wireless communications support various radios. A software-defined radio (SDR) is a radio implemented as concurrent software processes that typically run on a multiprocessor system-on-chip (MPSoC). SDRs are real-time streaming applications with throughput requirements. One efficient approach for timing analysis of concurrent real-time applications is the dataflow model of computation (MoC). Nonetheless, the dataflow modeling of SDRs is challenging due to their dynamically changing data processing workload. A dataflow MoC that is not expressive enough to capture this dynamism gives pessimistic throughput results. On the other hand, if it is too expressive and detailed, it may not be analyzable at all. In this paper, we address the challenge of dataflow modeling of SDRs such that their timing behavior can be accurately analyzed to guarantee real-time requirements without unnecessarily over-allocating MPSoC resources. The basis of our modeling approach is splitting the dynamic data processing behavior of a SDR into a group of static modes of operation. Each static mode of operation is then modeled by a Synchronous Dataflow (SDF), which we refer to as scenario. This paper has two main contributions: 1) a scenario-based dataflow model of Long Term Evolution (LTE), which is the latest standard in cellular communication, and 2) investigation of existing throughput analysis techniques of SDF scenarios for our LTE model. Our results show that scenario-based worst-case throughput computation is 2 to 3.4 times more accurate than a state-of-the-art SDF analysis technique. Our investigation also shows that existing timing analysis techniques of SDF scenarios have very low run-time that scales very well with increase in graph size. This makes SDF scenarios suitable in practice for modeling and analyzing SDRs as well as similar dynamic applications.