Resource Allocation for Multi-Tenant Network Slicing: A Multi-Leader Multi-Follower Stackelberg Game Approach

Abstract

The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multifollower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes.

Efficient and Fair Multi-Resource Allocation in Dynamic Fog Radio Access Network Slicing

“…Instead of considering a base station as a resource, Narmanlioglu and Zeydan [29] apply the same framework to allocate RUs to virtual MNOs. Tran and Le [30] employ a Stackelberg game to model the allocation and pricing of resources for network slicing, so as to capture interactions among access / backhaul SPs and their UEs. However, the related solution only considers radio resources in the downlink without discussing scheduling for different service types.…”

Techno-Economic Assessment of 5G Infrastructure Sharing Business Models in Rural Areas

“…The key challenges of this strategy are similar pricing plans, a shift in competition, pricing strategies, resource allocation, and security of data on their slice [60]- [62]. The key to the successful upgrade of the 5G network using "5G NHN" is cooperation among the slice tenants and their corresponding resource allocation schemes [14], [53], [63]. The widespread usage of this technology lies in the usage of slicing capabilities offered by the network, and its security aspects [64], [65].…”

Service Provisioning in Sliced Cloud Radio Access Networks

“…Game theory is an effective tool to analyse the performance of resource allocation. References [19,20] considered the resource allocation among slices. e authors studied the resource allocation and pricing problem for network slicing that captures interactions among access backhaul service providers and their UEs by using the Stackelberg game approach [19].…”