User experience on mobile devices is constrained by limited battery capacity
and processing power, but 6G technology advancements are diving rapidly into
mobile technical evolution. Mobile edge computing (MEC) offers a solution,
offloading computationally intensive tasks to edge cloud servers, reducing
battery drain compared to local processing. The upcoming integrated sensing and
communication in mobile communication may improve the trajectory prediction and
processing delays. This study proposes a greedy resource allocation
optimization strategy for multi-user networks to minimize aggregate energy
usage. Numerical results show potential improvement at 33\% for every 1000
iteration. Addressing prediction model division and velocity accuracy issues is
crucial for better results. A plan for further improvement and achieving
objectives is outlined for the upcoming work phase.

移动设备上的用户体验受到有限的电池容量和处理能力的限制，但 6G 技术的进步正在迅速促使移动技术的演进。移动边缘计算（MEC）提供了一种解决方案，将计算密集型任务卸载到边缘云服务器，与本地处理相比减少了电池消耗。即将到来的移动通信的集成感知与通信可能改善轨迹预测和处理延迟。本研究提出了一种贪婪资源分配优化策略，用于多用户网络以最小化聚合能量使用。数值结果显示，每 1000 次迭代，潜在改进可达 33％。解决预测模型划分和速度精确性问题对于获得更好的结果至关重要。为即将到来的工作阶段详述了进一步改进和实现目标的计划。

基于 Koopman 轨迹模型的 ISAC 启用的物联网系统中的高移动性范式和计算卸载

Koopman based trajectory model and computation offloading for high  mobility paradigm in ISAC enabled IoT system

There is an increasing interest in a fast-growing machine learning technique
called Federated Learning, in which the model training is distributed over
mobile user equipments (UEs), exploiting UEs' local computation and training
data. Despite its advantages in data privacy-preserving, Federated Learning
(FL) still has challenges in heterogeneity across UEs' data and physical
resources. We first propose a FL algorithm which can handle the heterogeneous
UEs' data challenge without further assumptions except strongly convex and
smooth loss functions. We provide the convergence rate characterizing the
trade-off between local computation rounds of UE to update its local model and
global communication rounds to update the FL global model. We then employ the
proposed FL algorithm in wireless networks as a resource allocation
optimization problem that captures the trade-off between the FL convergence
wall clock time and energy consumption of UEs with heterogeneous computing and
power resources. Even though the wireless resource allocation problem of FL is
non-convex, we exploit this problem's structure to decompose it into three
sub-problems and analyze their closed-form solutions as well as insights to
problem design. Finally, we illustrate the theoretical analysis for the new
algorithm with Tensorflow experiments and extensive numerical results for the
wireless resource allocation sub-problems. The experiment results not only
verify the theoretical convergence but also show that our proposed algorithm
outperforms the vanilla FedAvg algorithm in terms of convergence rate and
testing accuracy.

提出了一种处理异构用户设备数据的联邦学习算法，同时结合对微波资源分配问题的优化，实现了收敛速率和能耗之间的平衡，并用 Tensorflow 实验验证了该算法的理论分析和优化表现。

无线网络上的联邦学习：收敛分析和资源分配

Federated Learning over Wireless Networks: Convergence Analysis and  Resource Allocation

As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.

本文综合地研究了近年来前传受限云无线接入网络的最新进展，包括系统架构和关键技术，其中涉及缓解前传受限对用户的 SE /EE 和服务质量的影响的关键技术，包括压缩和量化、大规模协同处理和聚类以及资源分配优化。此外，本文还指出软件定义网络、网络功能虚拟化和部分集中化等方面存在的开放问题。