Reconfigurable and Traffic-Aware MAC Design for Virtualized Wireless Networks via Reinforcement Learning
| Autor: | Mahsa Derakhshani, Atoosa Dalili Shoaei, Tho Le-Ngoc |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computational complexity theory
Wireless network Iterative method Computer science Distributed computing Approximation algorithm 020206 networking & telecommunications 020302 automobile design & engineering Regret Throughput 02 engineering and technology 0203 mechanical engineering 0202 electrical engineering electronic engineering information engineering Reinforcement learning Electrical and Electronic Engineering Thompson sampling |
| Zdroj: | IEEE Transactions on Communications. 67:5490-5505 |
| ISSN: | 1558-0857 0090-6778 |
| DOI: | 10.1109/tcomm.2019.2913413 |
| Popis: | In this paper, we present a reconfigurable MAC scheme where the partition between contention-free and contention-based regimes in each frame is adaptive to the network status leveraging reinforcement learning. In particular, to support a virtualized wireless network consisting of multiple slices, each having heterogeneous and unsaturated devices, the proposed scheme aims to configure the partition for maximizing network throughput while maintaining the slice reservations. Applying complementary geometric programming and monomial approximations, an iterative algorithm is developed to find the optimal solution. For a large number of devices, a scalable algorithm with lower computational complexity is also proposed. The partitioning algorithm requires the knowledge of the device traffic statistics. In the absence of such knowledge, we develop a learning algorithm employing Thompson sampling to acquire packet arrival probabilities of devices. Furthermore, we model the problem as a thresholding multi-armed bandit and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound. |
| Databáze: | OpenAIRE |
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