Abstract
We introduce a reinforcement learning algorithm inspired by the combinatorial multi-armed bandit problem to minimize the time-averaged energy cost at individual base stations (BSs), powered by various energy markets and local renewable energy sources, over a finite-time horizon. The algorithm sustains traffic demands by enabling sparse beamforming to schedule dynamic user-to-BS allocation and proactive energy provisioning at BSs to make ahead-of-time price-aware energy management decisions. Simulation results indicate a superior performance of the proposed algorithm in reducing the overall energy cost, as compared with recently proposed cooperative energy management designs.
| Original language | English |
|---|---|
| Article number | 7887725 |
| Pages (from-to) | 1609-1612 |
| Number of pages | 4 |
| Journal | IEEE COMMUNICATIONS LETTERS |
| Volume | 21 |
| Issue number | 7 |
| Early online date | 27 Mar 2017 |
| DOIs | |
| Publication status | Published - 1 Jul 2017 |
Keywords
- CMAB
- Energy management
- online learning
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- A Bandit Approach to_ZHANG_Publishedonline27March2017_GREEN AAM (non-CC)
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