Predicting future values in multivariate time series is vital across various
domains. This work explores the use of large language models (LLMs) for this
task. However, LLMs typically handle one-dimensional data. We introduce
MultiCast, a zero-shot LLM-based approach for multivariate time series
forecasting. It allows LLMs to receive multivariate time series as input,
through three novel token multiplexing solutions that effectively reduce
dimensionality while preserving key repetitive patterns. Additionally, a
quantization scheme helps LLMs to better learn these patterns, while
significantly reducing token use for practical applications. We showcase the
performance of our approach in terms of RMSE and execution time against
state-of-the-art approaches on three real-world datasets.

使用大型语言模型（LLMs）和 MultiCast 方法，可以通过三种新型的令牌复用解决方案，将多变量时间序列预测问题映射为一维数据的处理，提高预测准确性并减少令牌使用量。在三个真实数据集中，MultiCast 方法在 RMSE 和执行时间方面表现出与现有方法相比的优势。

MultiCast：使用 LLM 进行零样本多元时间序列预测

MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs

We study the joint unicast and multi-group multicast transmission in massive
multiple-input-multiple-output (MIMO) systems. We consider a system model that
accounts for channel estimation and pilot contamination, and derive achievable
spectral efficiencies (SEs) for unicast and multicast user terminals (UTs),
under maximum ratio transmission and zero-forcing precoding. For unicast
transmission, our objective is to maximize the weighted sum SE of the unicast
UTs, and for the multicast transmission, our objective is to maximize the
minimum SE of the multicast UTs. These two objectives are coupled in a
conflicting manner, due to their shared power resource. Therefore, we formulate
a multiobjective optimization problem (MOOP) for the two conflicting
objectives. We derive the Pareto boundary of the MOOP analytically. As each
Pareto optimal point describes a particular efficient trade-off between the two
objectives of the system, we determine the values of the system parameters
(uplink training powers, downlink transmission powers, etc.) to achieve any
desired Pareto optimal point. Moreover, we prove that the Pareto region is
convex, hence the system should serve the unicast and multicast UTs at the same
time-frequency resource. Finally, we validate our results using numerical
simulations.

研究了多播和单播传输在大规模 MIMO 系统中的性能，提出了最大化加权和谐单播用户个体频谱效率和最小化多播用户个体频谱效率两个多目标优化问题，并得出了解决这些问题的最优解。

基于 Massive MIMO 的单播和多组多播传输

Joint Unicast and Multi-group Multicast Transmission in Massive MIMO  Systems

Network Function Virtualization (NFV) on Software-Defined Networks (SDN) can
effectively optimize the allocation of Virtual Network Functions (VNFs) and the
routing of network flows simultaneously. Nevertheless, most previous studies on
NFV focus on unicast service chains and thereby are not scalable to support a
large number of destinations in multicast. On the other hand, the allocation of
VNFs has not been supported in the current SDN multicast routing algorithms. In
this paper, therefore, we make the first attempt to tackle a new challenging
problem for finding a service forest with multiple service trees, where each
tree contains multiple VNFs required by each destination. Specifically, we
formulate a new optimization, named Service Overlay Forest (SOF), to minimize
the total cost of all allocated VNFs and all multicast trees in the forest. We
design a new $3\rho_{ST}$-approximation algorithm to solve the problem, where
$\rho_{ST}$ denotes the best approximation ratio of the Steiner Tree problem,
and the distributed implementation of the algorithm is also presented.
Simulation results on real networks for data centers manifest that the proposed
algorithm outperforms the existing ones by over 25%. Moreover, the
implementation of an experimental SDN with HP OpenFlow switches indicates that
SOF can significantly improve the QoE of the Youtube service.

本文提出 Service Overlay Forest (SOF) 优化算法解决了多播中多点到多点服务树的部署问题，得到了 25% 以上的改进并提高了 Youtube 服务的用户体验。

面向软件定义云网络的服务叠加森林嵌入

Service Overlay Forest Embedding for Software-Defined Cloud Networks

In this paper, we present a framework for resource allocations for multicast
device-to-device (D2D) communications underlaying a cellular network. The
objective is to maximize the sum throughput of active cellular users (CUs) and
feasible D2D groups in a cell, while meeting a certain
signal-to-interferenceplus- noise ratio (SINR) constraint for both the CUs and
D2D groups. We formulate the problem of power and channel allocation as a mixed
integer nonlinear programming (MINLP) problem where one D2D group can reuse the
channels of multiple CUs and the channel of each CU can be reused by multiple
D2D groups. Distinct from existing approaches in the literature, our
formulation and solution methods provide an effective and flexible means to
utilize radio resources in cellular networks and share them with multicast
groups without causing harmful interference to each other. A variant of the
generalized bender decomposition (GBD) is applied to optimally solve the MINLP
problem. A greedy algorithm and a low-complexity heuristic solution are then
devised. The performance of all schemes is evaluated through extensive
simulations. Numerical results demonstrate that the proposed greedy algorithm
can achieve closeto- optimal performance, and the heuristic algorithm provides
good performance, though inferior than that of the greedy, with much lower
complexity.

本文介绍了一个用于蜂窝网络下的组播设备对设备通信资源分配的框架，主要通过混合整数非线性规划来解决干扰问题和优化资源利用效率，提供了贪心算法和启发式算法来优化研究结果。