The research in the sixth generation of communication networks needs to tackle new challenges in order to meet the requirements of emerging applications in terms of high data rate, low latency, high reliability, and massive connectivity. To this end, the entire communication chain needs to be optimized, including the channel and the surrounding environment, as it is no longer sufficient to control the transmitter and/or the receiver only. Investigating large intelligent surfaces, ultra massive multiple-input multiple-output, and smart constructive environments will contribute to this direction. In addition, to allow the exchange of high dimensional sensing data between connected intelligent devices, semantic and goal oriented communications need to be considered for a more efficient and context-aware information encoding. In particular, for multi-agent systems, where agents are collaborating together to achieve a complex task, emergent communications, instead of hard coded communications, can be learned for more efficient task execution and communication resources use. Moreover, new physics phenomenon should be exploited such as the thermodynamics of communication as well as the the interaction between information theory and electromagnetism to better understand the physical limitations of different technologies, e.g, holographic communications. Another new communication paradigm is to consider the end-to-end approach instead of block-by-block optimization, which requires exploiting machine learning theory, non-linear signal processing theory, and non-coherent communications theory. Within this context, we identify twelve scientific challenges for rebuilding the theoretical foundations of communications, and we overview each of the challenges while providing research opportunities and open questions for the research community.

翻译：第六代通信网络的研究需要应对新挑战，以满足新兴应用对高数据速率、低延迟、高可靠性和大规模连接的需求。为此，整个通信链路（包括信道及周围环境）需要优化，仅控制发射机和/或接收机已不再足够。研究大型智能表面、超大规模多输入多输出以及智能构造环境将有助于这一方向的发展。此外，为在连接的智能设备间交换高维传感数据，需考虑语义通信与目标导向通信，以实现更高效且具备上下文感知能力的信息编码。特别是在多智能体系统中，当智能体协作完成复杂任务时，可采用涌现通信替代硬编码通信，通过自主学习实现更高效的任务执行和通信资源利用。同时，应挖掘新的物理现象，例如通信热力学和电磁学与信息论的相互作用，以更深入理解不同技术（如全息通信）的物理限制。另一种新通信范式是采用端到端方法替代逐块优化，这需借助机器学习理论、非线性信号处理理论及非相干通信理论。在此背景下，我们确定了重建通信理论基础的十二项科学挑战，并对每项挑战进行概述，同时为研究界提供研究机遇与开放性问题。