Projects per year
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.
|Number of pages||14|
|Journal||IEEE Transactions on Wireless Communications|
|Publication status||Published - Apr 2020|
|MoE publication type||A1 Journal article-refereed|
- deep reinforcement learning
- long short-term memory
- Markov decision process
- multi-user resource scheduling
- Q-function decomposition
- Vehicular communications
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- 2 Finished
MISSION: Mission-Critical Internet of Things Applications over Fog Networks
Chen, X., Forsell, M., Chen, T. & Räty, T.
1/01/19 → 31/12/21
Project: Academy of Finland project
5G-DRIVE: 5G HarmoniseD Research and TrIals for serVice Evolution between EU and China
Chen, T., Horsmanheimo, S., Tuomimäki, L., Chen, X., Zidbeck, J., Kutila, M., Kauvo, K., Mehnert, S., Pyykönen, P. & Jokela, M.
1/09/18 → 30/06/21
Project: EU project