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Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication
| Content Provider | MDPI |
|---|---|
| Author | Khan, Muhidul Islam Reggiani, Luca Alam, Muhammad Mahtab Moullec, Yannick Le Sharma, Navuday Yaacoub, Elias Magarini, Maurizio |
| Copyright Year | 2020 |
| Description | In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately |
| Starting Page | 6692 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20226692 |
| Journal | Sensors |
| Issue Number | 22 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-23 |
| Access Restriction | Open |
| Subject Keyword | Sensors Telecommunications Joint Energy-spectral Efficiency (ese) Device-to-device (d2d) Public Safety Networks Pervasive Public Safety Communication Internet of Things (iot) |
| Content Type | Text |
| Resource Type | Article |
