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DRL-Based Sequential Scheduling for IRS-Assisted MIMO Communications
- Source :
- IEEE Transactions on Vehicular Technology; 2024, Vol. 73 Issue: 6 p8445-8459, 15p
- Publication Year :
- 2024
-
Abstract
- Efficient resource allocation strategies are pivotal in vehicular communications as connected devices steeply increase in scenarios with much more stringent requirements. In this work, we propose a deep reinforcement learning (DRL)-based sequential scheduling approach for sum-rate maximization in the uplink of intelligent reflecting surface (IRS)-assisted multi-user (MU) multiple-input multiple-output (MIMO) vehicular communications. We formulate the scheduling task as a partially observable Markov decision process (POMDP) and propose a novel stream-level sequential solution based on the proximal policy optimization (PPO) algorithm. We consider a realistic imperfect channel state information (ICSI) model and assess the proposal in several communication setups comprising both spatially uncorrelated and correlated links. Simulation results show that the proposed DRL-based sequential scheduling approach is a robust alternative to more computationally demanding benchmarks.
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 73
- Issue :
- 6
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Periodical
- Accession number :
- ejs66693181
- Full Text :
- https://doi.org/10.1109/TVT.2024.3359117