Name
Abstract | ||
|---|---|---|
As modern communication systems become indispensable, the requirements for communication systems such as delay and power get more stringent. In this paper, we adopt a Reinforcement Learning (RL) based approach to obtain the optimal trade-off between delay and power consumption for a given power constraint in a communication system whose conditions (e.g., channel conditions, traffic arrival rates) can change over time. To this end, we first formulate this problem as an infinite-horizon Markov Decision Process (MDP) and then Q-learning is adopted to solve this problem. To handle the given power constraint, we apply the Lagrange multiplier method that transforms a constrained optimization problem into a non-constrained problem. Finally, via simulation, we show that Q-learning achieves the optimal policy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICTC46691.2019.8939680 | 2019 International Conference on Information and Communication Technology Convergence (ICTC) |
Keywords | Field | DocType |
Reinforcement Learning,delay-power tradeoff,adaptive transmission,infinite-horizon Markov Decision Process | Transmission (mechanics),Mathematical optimization,Lagrange multiplier,Scheduling (computing),Computer science,Markov decision process,Communications system,Communication channel,Low delay,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
2162-1233 | 978-1-7281-0894-0 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhao | 1 | 0 | 0.34 |
| Joohyun Lee | 2 | 4 | 2.79 |