# Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/1807.09350 · HTML: https://mlsanity.com/paper/1807.09350/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
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- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Collaborative Perception for Autonomous Driving: Current Status and Future Trend](https://mlsanity.com/paper/2208.10371/) (2022)
- [Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2211.12201/) (2022)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty](https://mlsanity.com/paper/2106.08477/) (2021)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks](https://mlsanity.com/paper/2208.12466/) (2022)
- [Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning](https://mlsanity.com/paper/2209.08947/) (2022)

## Abstract (arXiv)

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.