Collaborative Research: MLWiNS:Physical Layer Communication revisited via Deep Learning

可靠的通信是现代信息时代的主力。通信、编码和信息论的学科通过设计有效的代码来推动创新，这些代码允许传输被稳健地解码。近乎最优码的进步是由人类个人的聪明才智取得的，而突破一直是零星的，几十年来一直是零星的。深度学习最近在算法选择空间巨大的问题(例如围棋)中显示出强大的前景。这一情景同样也是传播理论的特征。深度学习方法可以在实现上述目标方面发挥关键作用。所有产生的算法都将在一个带有完整源代码和文档的在线储存库中维护。新代码的实际应用将在无线部署的背景下进行探索。研究成果将用于制定新的本科生和研究生课程。这项研究的基本性质跨越两个独立的科学和技术兴趣领域：有限块长度信息理论和深度学习的数学。这个项目的目的是利用深度学习的工具，为规范的通信模型设计一系列新的编码和解码方法；这样生成的代码自然是为有限块长度构建的。同时，神经网络结构作为编码和解码过程的观点为研究其数学特性提供了一个独特的有利位置。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Reliable communication is a workhorse of the modern information age. The disciplines of communication, coding, and information theory drive the innovation by designing efficient codes that allow transmissions to be robustly decoded. Progress in near optimal codes is made by individual human ingenuity and breakthroughs have been, befittingly, sporadic, spread over several decades. Deep learning has recently shown strong promise in problems where the space of algorithmic choices is enormous (e.g., Go). This scenario likewise characterizes communication theory. Deep learning methods can play a crucial role in achieving the aforementioned goals. All resulting algorithms will be maintained on an online repository with full source code and documentation. The practical applications of the new codes will be explored in the context of wireless deployments. The research outcomes will be used to develop new undergraduate and graduate curricula. The fundamental nature of the research spans two areas of independent scientific and technical interest: finite block length information theory and the mathematics of deep learning. This project aims to bring the tools of deep learning to design a new family of encoding and decoding methods for canonical communication models; the codes so generated are naturally built for finite block lengths. In parallel, the viewpoint of neural network architectures as encoding and decoding procedures provides a unique vantage point to study their mathematical properties.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.