We investigate optimal band/channel selection in hybrid radio frequency and visible light communication (RF/VLC) networks. Particularly, we first develop a robust hybrid RF/VLC based system model for the optimal band/channel selection. We then formulate it as an online stochastic budget-constrained multi-armed bandit (MAB) problem. Two online learning algorithms based on different optimal policies are proposed to choose the appropriate band, i.e., energy-aware band selection with upper confidence bound (EABS-UCB) and energy-aware band selection with Thompson sampling (EABS-TS). The cost/budget is the battery consumption of the transmitting device according to the selected band. Through extensive simulations, it is confirmed that the proposed EABS-TS emerges as the superior technique compared with the random, brute-force, and EABS-UCB band selection schemes, in terms of energy efficiency, average throughput, and convergence performance.
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Aerospace Engineering
- Electrical and Electronic Engineering
- Applied Mathematics