As a specific category of artificial intelligence (AI), generative artificial
intelligence (GenAI) generates new content that resembles what is created by
humans. The rapid development of GenAI systems has created a huge amount of new
data on the Internet, posing new challenges to current computing and
communication frameworks. Currently, GenAI services rely on the traditional
cloud computing framework due to the need for large computation resources.
However, such services will encounter high latency because of data transmission
and a high volume of requests. On the other hand, edge-cloud computing can
provide adequate computation power and low latency at the same time through the
collaboration between edges and the cloud. Thus, it is attractive to build
GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this
overview paper, we review recent developments in GenAI and edge-cloud
computing, respectively. Then, we use two exemplary GenAI applications to
discuss technical challenges in scaling up their solutions using edge-cloud
collaborative systems. Finally, we list design considerations for training and
deploying GenAI systems at scale and point out future research directions.

本文概述了生成人工智能和边缘云计算的最新发展，并利用两个示例应用程序讨论了使用边缘云协作系统扩大其解决方案的技术挑战。最后，我们列出了在规模上训练和部署 GenAI 系统的设计注意事项，并指出了未来的研究方向。

边缘云计算的规模化生成人工智能概览

An Overview on Generative AI at Scale with Edge-Cloud Computing

With the growing interest in the deployment of massive
multiple-input-multiple-output (MIMO) systems and millimeter wave technology
for fifth generation (5G) wireless systems, the computation power to the total
power consumption ratio is expected to increase rapidly due to high data
traffic processing at the baseband unit. Therefore in this paper, a joint
optimization problem of computation and communication power is formulated for
multi-user massive MIMO systems with partially-connected structures of radio
frequency (RF) transmission systems. When the computation power is considered
for massiv MIMO systems, the results of this paper reveal that the energy
efficiency of massive MIMO systems decreases with increasing the number of
antennas and RF chains, which is contrary with the conventional energy
efficiency analysis results of massive MIMO systems, i.e., only communication
power is considered. To optimize the energy efficiency of multi-user massive
MIMO systems, an upper bound on energy efficiency is derived. Considering the
constraints on partially-connected structures, a suboptimal solution consisting
of baseband and RF precoding matrices is proposed to approach the upper bound
on energy efficiency of multi-user massive MIMO systems. Furthermore, an
oPtimized Hybrid precOding with computation and commuNication powEr (PHONE)
algorithm is developed to realize the joint optimization of computation and
communication power. Simulation results indicate that the proposed algorithm
improves energy and cost efficiencies and the maximum power saving is achieved
by 76.59\% for multi-user massive MIMO systems with partially-connected
structures.

本文针对具有部分连接结构的多用户大规模 MIMO 系统，提出了能耗优化模型，并设计了一种新的算法 PHONE 实现计算与通信功率的联合优化问题。仿真结果表明，所提出的算法可显著提高能源和成本效率，对具有 partially-connected structures 的多用户大规模 MIMO 系统最大功率节省达到了 76.59％。