Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

By Yiding Yu, Taotao Wang, Soung Chang Liew. 2018 IEEE International Conference on Communications (ICC), 2017

329 citations recorded by the source corpus.

This paper investigates the use of deep reinforcement learning (DRL) in the design of a "universal" MAC protocol referred to as Deep-reinforcement Learning Multiple Access (DLMA). The design framework is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". While the scope of DARPA SC2 is broad and involves the redesign of PHY, MAC, and Network layers, this paper's focus is narrower and only involves the MAC design. In particular, we consider the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The DRL agents of DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the rewards - in accordance with the DRL algorithmic framework - a DRL agent can learn the optimal MAC strategy for harmonious co-existence with TDMA and ALOHA nodes. In particular, the use of neural networks in DRL (as opposed to traditional reinforcement learning) allows for fast convergence to optimal solutions and robustness against perturbation in hyper- parameter settings, two essential properties for practical deployment of DLMA in real wireless networks.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/953c6f0c1ae5311e1bcc523fbba2de57d751c248

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xuemin Shen
2. Weihua Zhuang
3. Yaohua Sun
4. Paul Patras
5. Mugen Peng
6. Shiwen Mao
7. Jie Gao
8. Hamed Haddadi

In Electrical and Electronic Engineering

1. Xuemin Shen
2. Yaohua Sun
3. Hamed Haddadi
4. Paul Patras
5. Chaoyun Zhang
6. Mugen Peng
7. Yuzhen Huang
8. Yangcheng Zhou

Citations in the corpus

• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “apply deep reinforcement learning to address challenges in wireless multiple access control [389], recognizing that in such tasks DRL agents are fast in terms of con-”
• Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “Second, to deal with network dynamics, reinforcement learning can be applied for generating adaptive resource scheduling strategies [168].”
• Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “Another advantage of deep reinforcement learning is its ability to infer a good action under an unfamiliar state [146].”
• Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks (2018, background): “The experience-replay memory is a FIFO queue [15] with a length of nMm samples where n is the total number of agents, i.”