This paper considers the problem of cost-aware downlink sum-rate maximization
via joint optimal radio access technologies (RATs) assignment and power
allocation in next-generation heterogeneous wireless networks (HetNets). We
consider a future HetNet comprised of multi-RATs and serving multi-connectivity
edge devices (EDs), and we formulate the problem as mixed-integer non-linear
programming (MINP) problem. Due to the high complexity and combinatorial nature
of this problem and the difficulty to solve it using conventional methods, we
propose a hierarchical multi-agent deep reinforcement learning (DRL)-based
framework, called DeepRAT, to solve it efficiently and learn system dynamics.
In particular, the DeepRAT framework decomposes the problem into two main
stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q
Network (DQN) algorithm, and the power allocation stage, which utilizes a
multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using
simulations, we demonstrate how the various DRL agents efficiently interact to
learn system dynamics and derive the global optimal policy. Furthermore, our
simulation results show that the proposed DeepRAT algorithm outperforms
existing state-of-the-art heuristic approaches in terms of network utility.
Finally, we quantitatively show the ability of the DeepRAT model to quickly and
dynamically adapt to abrupt changes in network dynamics, such as EDs mobility.

本文提出了一个基于分阶段多代理深度强化学习框架 DeepRAT 来解决下一代异构无线网络中成本感知的下行总速率最大化的问题。通过模拟实现，我们证明各种 DRL 代理的有效互动，学习系统动态并导出全局最优策略，同时也证明了 DeepRAT 算法在网络效用方面优于现有的最先进的启发式方法，并进一步量化了 DeepRAT 模型在快速适应网络动态、例如设备的移动方面的能力。

面向下一代 HetNets 的多层次多智能体 DRL 框架：联合多 RAT 分配和动态资源分配

Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

6G will likely be the first generation of mobile communication that will
feature tight integration of localization and sensing with communication
functionalities. Among several worldwide initiatives, the Hexa-X flagship
project stands out as it brings together 25 key players from adjacent
industries and academia, and has among its explicit goals to research
fundamentally new radio access technologies and high-resolution localization
and sensing. Such features will not only enable novel use cases requiring
extreme localization performance, but also provide a means to support and
improve communication functionalities. This paper provides an overview of the
Hexa-X vision alongside the envisioned use cases. To close the required
performance gap of these use cases with respect to 5G, several technical
enablers will be discussed, together with the associated research challenges
for the coming years.

本文概述了 Hexa-X 旗舰计划的愿景和预期应用场景，着重就 6G 的实现中出现的新无线接入技术、极高精度定位和感知等技术提出了若干技术支持和研究方面的问题。

6G 通信与感知的集成：产学合作视角

Integration of Communication and Sensing in 6G: a Joint Industrial and  Academic Perspective

With rising interest in autonomous vehicles, developing radio access
technologies (RATs) that enable reliable and low latency vehicular
communications has become of paramount importance. Dedicated Short Range
Communications (DSRC) and Cellular V2X (C-V2X) are two present-day technologies
that are capable of supporting day-1 vehicular applications. However, these
RATs fall short of supporting communication requirements of many advanced
vehicular applications, which are believed to be critical in enabling fully
autonomous vehicles. Both DSRC and C-V2X are undergoing extensive enhancements
in order to support advanced vehicular applications that are characterized by
high reliability, low latency and high throughput requirements. These RAT
evolutions---IEEE 802.11bd for DSRC and NR V2X for C-V2X---can supplement
today's vehicular sensors in enabling autonomous driving. In this paper, we
briefly describe the two present-day vehicular RATs. In doing so, we highlight
their inability to guarantee quality of service requirements of many advanced
vehicular applications. We then look at the two RAT evolutions, i.e., IEEE
802.11bd and NR V2X and outline their objectives, describe their salient
features and provide an in-depth description of key mechanisms that enable
these features. While both, IEEE 802.11bd and NR V2X, are in their initial
stages of development, we shed light on their preliminary performance
projections and compare and contrast the two evolutionary RATs with their
respective predecessors.

本研究旨在描述当今的两种车载无线电路（DSRC 和 C-V2X），以及它们在支持先进自动驾驶应用方面的限制。同时，我们还介绍了正在发展的 IEEE 802.11bd 和 NR V2X，它们旨在弥补 DSRC 和 C-V2X 的局限性，并为实现无人驾驶提供支持。