This article presents a new Domain Specific Embedded Language (DSEL)
dedicated to Software-Defined Radio (SDR). From a set of carefully designed
components, it enables to build efficient software digital communication
systems, able to take advantage of the parallelism of modern processor
architectures, in a straightforward and safe manner for the programmer. In
particular, proposed DSEL enables the combination of pipelining and sequence
duplication techniques to extract both temporal and spatial parallelism from
digital communication systems. We leverage the DSEL capabilities on a real use
case: a fully digital transceiver for the widely used DVB-S2 standard designed
entirely in software. Through evaluation, we show how proposed software DVB-S2
transceiver is able to get the most from modern, high-end multicore CPU
targets.

本研究提出一个新的领域特定嵌入式语言（DSEL），专门用于软件定义无线电（SDR），它允许构建高效的软件数字通信系统，可以利用现代处理器架构的并行性，提取数字通信系统的时间和空间并行性，以实现全数字卫星电视广播标准 (DVB-S2) 的接受和发射。

基于多核 CPU 的高吞吐量、低延迟软件无线电领域领域特定语言

A DSEL for High Throughput and Low Latency Software-Defined Radio on Multicore CPUs

Communications are realized as a result of successive decisions at the
physical layer, from modulation selection to multi-antenna strategy, and each
decision affects the performance of the communication systems. Future
communication systems must include extensive capabilities as they will
encompass a wide variety of devices and applications. Conventional physical
layer decision mechanisms may not meet these requirements, as they are often
based on impractical and oversimplifying assumptions that result in a trade-off
between complexity and efficiency. By leveraging past experiences,
learning-driven designs are promising solutions to present a resilient decision
mechanism and enable rapid response even under exceptional circumstances. The
corresponding design solutions should evolve following the lines of
learning-driven paradigms that offer more autonomy and robustness. This
evolution must take place by considering the facts of real-world systems and
without restraining assumptions. In this paper, the common assumptions in the
physical layer are presented to highlight their discrepancies with practical
systems. As a solution, learning algorithms are examined by considering the
implementation steps and challenges. Furthermore, these issues are discussed
through a real-time case study using software-defined radio nodes to
demonstrate the potential performance improvement. A cyber-physical framework
is presented to incorporate future remedies.

本文介绍了物理层中的典型假设及其与实际系统的差异，并考虑到实现步骤和挑战，系统地讨论了基于学习算法的决策机制设计。此外，通过使用软件定义无线电节点进行实时案例研究，论述了潜在的性能改进和未来措施的物理层的学习驱动框架。