The significance of transportation efficiency, safety, and related services
is increasing in urban vehicular networks. Within such networks, roadside units
(RSUs) serve as intermediates in facilitating communication. Therefore, the
deployment of RSUs is of utmost importance in ensuring the quality of
communication services. However, the optimization objectives, such as time
delay and deployment cost, are commonly developed from diverse perspectives. As
a result, it is possible that conflicts may arise among the objectives.
Furthermore, in urban environments, the presence of various obstacles, such as
buildings, gardens, lakes, and other infrastructure, poses challenges for the
deployment of RSUs. Hence, the deployment encounters significant difficulties
due to the existence of multiple objectives, constraints imposed by obstacles,
and the necessity to explore a large-scale optimization space. To address this
issue, two versions of multi-objective optimization algorithms are proposed in
this paper. By utilizing a multi-population strategy and an adaptive
exploration technique, the methods efficiently explore a large-scale
decision-variable space. In order to mitigate the issue of an overcrowded
deployment of RSUs, a calibrating mechanism is adopted to adjust RSU density
during the optimization procedures. The proposed methods also take care of data
offloading between vehicles and RSUs by setting up an iterative best response
sequence game (IBRSG). By comparing the proposed algorithms with several
state-of-the-art algorithms, the results demonstrate that our strategies
perform better in both high-density and low-density urban scenarios. The
results also indicate that the proposed solutions substantially improve the
efficiency of vehicular networks.

提出了两个多目标优化算法以解决城市车辆网络中路侧单元 (RSU) 部署的难题，结果表明这些方法在高密度和低密度城市场景中均表现出更好的性能，从而大大提高了车辆网络的效率。

城市车辆网络中多目标最优路侧单元部署

Multi-objective Optimal Roadside Units Deployment in Urban Vehicular  Networks

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique
for next-generation communication networks due to their flexibility, mobility,
low cost, and the ability to collaboratively and autonomously provide services.
Distributed learning (DL) enables UAV swarms to intelligently provide
communication services, multi-directional remote surveillance, and target
tracking. In this survey, we first introduce several popular DL algorithms such
as federated learning (FL), multi-agent Reinforcement Learning (MARL),
distributed inference, and split learning, and present a comprehensive overview
of their applications for UAV swarms, such as trajectory design, power control,
wireless resource allocation, user assignment, perception, and satellite
communications. Then, we present several state-of-the-art applications of UAV
swarms in wireless communication systems, such us reconfigurable intelligent
surface (RIS), virtual reality (VR), semantic communications, and discuss the
problems and challenges that DL-enabled UAV swarms can solve in these
applications. Finally, we describe open problems of using DL in UAV swarms and
future research directions of DL enabled UAV swarms. In summary, this survey
provides a comprehensive survey of various DL applications for UAV swarms in
extensive scenarios.

本文综述了分布式学习算法在无人机群体中的应用，重点分析了其在通信服务、无线资源分配、用户分配、感知和卫星通信等方面的应用，并介绍了其在无线通信系统中的若干先进应用，并讨论了使用 DL-enable 的 UAV 群组所能解决的问题和挑战，同时提出未来的研究方向和开放性问题。

无人机群的分布式机器学习：计算、感知和语义分析

Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.

本文全面介绍了如何将先进技术应用到无人机与 5G 及更高级别的蜂窝网络进行整合，以满足下一代无线系统的多种服务需求。重点强调了智能反射表面、短数据包传输、能量收集、联合通信和雷达感知以及边缘智能等先进技术的应用，并且提出了未来研究的重要方向。