Physical Layer Multi-Core Prototyping
Base stations developed according to the 3GPP Long Term Evolution (LTE) standard require unprecedented processing power. 3GPP LTE enables data rates beyond hundreds of Mbits/s by using advanced technologies, necessitating a highly complex LTE physical layer. The operating power of base stations is a significant cost for operators, and is currently optimized using state-of-the-art hardware solutions, such as heterogeneous distributed systems. The traditional system design method of porting algorithms to heterogeneous distributed systems based on test-and-refine methods is a manual, thus time-expensive task.
Programming Multi-Core LTE Base Stations: A Dataflow-Based Approach provides a clear introduction to the 3GPP LTE physical layer and to dataflow-based prototyping and programming. The difficulties in the process of 3GPP LTE physical layer porting are outlined, with particular focus on automatic partitioning and scheduling, load balancing and computation latency reduction, specifically in systems based on heterogeneous multi-core Digital Signal Processors. Multi-core prototyping methods based on algorithm dataflow modeling and architecture system-level modeling are assessed with the goal of automating and optimizing algorithm porting.
With its analysis of physical layer processing and proposals of parallel programming methods, which include automatic partitioning and scheduling, Programming Multi-Core LTE Base Stations: A Dataflow-Based Approach is a key resource for researchers and students. This study of LTE algorithms which require dynamic or static assignment and dynamic or static scheduling, allows readers to reassess and expand their knowledge of this vital component of LTE base station design.
Introduces innovative methodologies such as a top-down approach to tackle multi-core programming issues and the Parallel and Real-time Embedded Executive Scheduling Method (PREESM)
Covers important advancements to the state of the art in design methodologies for embedded signal processing systems which can be implemented to lower design, deployment and maintenances costs across a range of wireless data networks
Includes models and techniques for adaptive scheduling of dataflow graphs to provide robust execution of dataflow tasks on targeted devices