The Metaverse is a new paradigm that aims to create a virtual environment
consisting of numerous worlds, each of which will offer a different set of
services. To deal with such a dynamic and complex scenario, considering the
stringent quality of service requirements aimed at the 6th generation of
communication systems (6G), one potential approach is to adopt self-sustaining
strategies, which can be realized by employing Adaptive Artificial Intelligence
(Adaptive AI) where models are continually re-trained with new data and
conditions. One aspect of self-sustainability is the management of multiple
access to the frequency spectrum. Although several innovative methods have been
proposed to address this challenge, mostly using Deep Reinforcement Learning
(DRL), the problem of adapting agents to a non-stationary environment has not
yet been precisely addressed. This paper fills in the gap in the current
literature by investigating the problem of multiple access in multi-channel
environments to maximize the throughput of the intelligent agent when the
number of active User Equipments (UEs) may fluctuate over time. To solve the
problem, a Double Deep Q-Learning (DDQL) technique empowered by Continual
Learning (CL) is proposed to overcome the non-stationary situation, while the
environment is unknown. Numerical simulations demonstrate that, compared to
other well-known methods, the CL-DDQL algorithm achieves significantly higher
throughputs with a considerably shorter convergence time in highly dynamic
scenarios.

在 6G 通信系统中，Metaverse 为一种新范式，旨在创建由许多世界构成的虚拟环境，其中每个世界都提供不同的服务。本文通过使用自适应人工智能（Adaptive AI）来实现自持续性策略，解决了多通道环境下的多用户接入问题，提出了一种基于连续学习的双重深度 Q 学习（DDQL）技术，该技术可以有效地提高吞吐量并在高度动态场景下快速收敛。

自给自足的多接入机制与连续深度强化学习用于动态元宇宙应用

Self-Sustaining Multiple Access with Continual Deep Reinforcement  Learning for Dynamic Metaverse Applications

The performance of orthogonal and non-orthogonal multiple access is studied
for the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable
Low-Latency Communications (URLLC) users in the uplink of a multi-cell Cloud
Radio Access Network (C-RAN) architecture. While eMBB users can operate over
long codewords spread in time and frequency, URLLC users' transmissions are
random and localized in time due to their low-latency requirements. These
requirements also call for decoding of their packets to be carried out at the
edge nodes (ENs), whereas eMBB traffic can leverage the interference management
capabilities of centralized decoding at the cloud. Using information-theoretic
arguments, the performance trade-offs between eMBB and URLLC traffic types are
investigated in terms of rate for the former, and rate, access latency, and
reliability for the latter. The analysis includes non-orthogonal multiple
access (NOMA) with different decoding architectures, such as puncturing and
successive interference cancellation (SIC). The study sheds light into
effective design choices as a function of inter-cell interference,
signal-to-noise ratio levels, and fronthaul capacity constraints.

本文探讨了在多元背景下不同多址方式使用于增强移动宽带 (eMBB) 和超可靠低延迟通信 (URLLC) 上行链路中的性能表现，并研究了使用中央化解码的干扰管理能力与使用边缘节点解码的低延迟要求之间的性能权衡。同时，文章还包括不同解码结构和干扰管理方法 (NOMA) 的讨论，如中断和连续干扰消除 (SIC)，以及在干扰、信噪比水平和前端容量限制的情况下进行的有效设计选择。

基于信息论的 C-RAN 上行链路中 URLLC 和 eMBB 服务的共存研究

Coexistence of URLLC and eMBB services in the C-RAN Uplink: An  Information-Theoretic Study

Millimeter wave (mmWave) communications have recently attracted large
research interest, since the huge available bandwidth can potentially lead to
rates of multiple Gbps (gigabit per second) per user. Though mmWave can be
readily used in stationary scenarios such as indoor hotspots or backhaul, it is
challenging to use mmWave in mobile networks, where the transmitting/receiving
nodes may be moving, channels may have a complicated structure, and the
coordination among multiple nodes is difficult. To fully exploit the high
potential rates of mmWave in mobile networks, lots of technical problems must
be addressed. This paper presents a comprehensive survey of mmWave
communications for future mobile networks (5G and beyond). We first summarize
the recent channel measurement campaigns and modeling results. Then, we discuss
in detail recent progresses in multiple input multiple output (MIMO)
transceiver design for mmWave communications. After that, we provide an
overview of the solution for multiple access and backhauling, followed by
analysis of coverage and connectivity. Finally, the progresses in the
standardization and deployment of mmWave for mobile networks are discussed.

该研究论文全面调查了毫米波通信在移动网络（5G 及以后）中的应用，包括通道测量，MIMO 信号发射 / 接收设计，多址 / 回返解决方案，覆盖范围及连通性分析，标准化和部署进展等。