Network slicing is a key technique in 5G and beyond for efficiently
supporting diverse services. Many network slicing solutions rely on deep
learning to manage complex and high-dimensional resource allocation problems.
However, deep learning models suffer limited generalization and adaptability to
dynamic slicing configurations. In this paper, we propose a novel framework
that integrates constrained optimization methods and deep learning models,
resulting in strong generalization and superior approximation capability. Based
on the proposed framework, we design a new neural-assisted algorithm to
allocate radio resources to slices to maximize the network utility under
inter-slice resource constraints. The algorithm exhibits high scalability,
accommodating varying numbers of slices and slice configurations with ease. We
implement the proposed solution in a system-level network simulator and
evaluate its performance extensively by comparing it to state-of-the-art
solutions including deep reinforcement learning approaches. The numerical
results show that our solution obtains near-optimal quality-of-service
satisfaction and promising generalization performance under different network
slicing scenarios.

我们提出了一种新颖的框架，将约束优化方法和深度学习模型相结合，从而实现了强大的泛化能力和优越的逼近能力，基于该框架，我们设计了一种新的神经辅助算法，以在切片之间的资源限制下最大化网络效用，该算法具有高可扩展性，轻松适应不同数量的切片和切片配置，通过与深度强化学习方法等最先进的解决方案进行比较，我们在系统级网络模拟器中实施了该解决方案，并通过大量评估结果表明，在不同网络切片场景下，我们的解决方案获得了近乎最优的服务质量满意度和有希望的泛化性能。

快速可扩展的深度学习与拉格朗日方法集成的网络切片

Fast and Scalable Network Slicing by Integrating Deep Learning with  Lagrangian Methods

This paper considers the problem of cost-aware downlink sum-rate maximization
via joint optimal radio access technologies (RATs) assignment and power
allocation in next-generation heterogeneous wireless networks (HetNets). We
consider a future HetNet comprised of multi-RATs and serving multi-connectivity
edge devices (EDs), and we formulate the problem as mixed-integer non-linear
programming (MINP) problem. Due to the high complexity and combinatorial nature
of this problem and the difficulty to solve it using conventional methods, we
propose a hierarchical multi-agent deep reinforcement learning (DRL)-based
framework, called DeepRAT, to solve it efficiently and learn system dynamics.
In particular, the DeepRAT framework decomposes the problem into two main
stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q
Network (DQN) algorithm, and the power allocation stage, which utilizes a
multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using
simulations, we demonstrate how the various DRL agents efficiently interact to
learn system dynamics and derive the global optimal policy. Furthermore, our
simulation results show that the proposed DeepRAT algorithm outperforms
existing state-of-the-art heuristic approaches in terms of network utility.
Finally, we quantitatively show the ability of the DeepRAT model to quickly and
dynamically adapt to abrupt changes in network dynamics, such as EDs mobility.

本文提出了一个基于分阶段多代理深度强化学习框架 DeepRAT 来解决下一代异构无线网络中成本感知的下行总速率最大化的问题。通过模拟实现，我们证明各种 DRL 代理的有效互动，学习系统动态并导出全局最优策略，同时也证明了 DeepRAT 算法在网络效用方面优于现有的最先进的启发式方法，并进一步量化了 DeepRAT 模型在快速适应网络动态、例如设备的移动方面的能力。

面向下一代 HetNets 的多层次多智能体 DRL 框架：联合多 RAT 分配和动态资源分配

Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

In the context of Fifth Generation (5G) mobile networks, the concept of
"Slice as a Service" (SlaaS) promotes mobile network operators to flexibly
share infrastructures with mobile service providers and stakeholders. However,
it also challenges with an emerging demand for efficient online algorithms to
optimize the request-and-decision-based inter-slice resource management
strategy. Based on genetic algorithms, this paper presents a novel online
optimizer that efficiently approaches towards the ideal slicing strategy with
maximized long-term network utility. The proposed method encodes slicing
strategies into binary sequences to cope with the request-and-decision
mechanism. It requires no a priori knowledge about the traffic/utility models,
and therefore supports heterogeneous slices, while providing solid
effectiveness, good robustness against non-stationary service scenarios, and
high scalability.

本文利用遗传算法编码切片策略，提出一种无先验知识的在线优化器，以最大化长期网络效用，具有良好的有效性、鲁棒性和可扩展性，以解决第五代移动通信网络下服务提供商与利益相关者之间的资源管理挑战。