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Found 7 papers, 0 papers with code
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning
Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach
In this paper, we devise algorithms learning optimal tilt control policies from existing data (in the so-called passive learning setting) or from data actively generated by the algorithms (the active learning setting).
A Graph Attention Learning Approach to Antenna Tilt Optimization
GAQ relies on a graph attention mechanism to select relevant neighbors information, improve the agent state representation, and update the tilt control policy based on a history of observations using a Deep Q-Network (DQN).
Multi-agent deep reinforcement learning (MADRL) meets multi-user MIMO systems
A multi-agent deep reinforcement learning (MADRL) is a promising approach to challenging problems in wireless environments involving multiple decision-makers (or actors) with high-dimensional continuous action space.
Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness
To demonstrate the optimality of the proposed DRL-based precoding framework, we explicitly consider a simple MIMO environment for which the optimal solution can be obtained analytically and show that DQN- and DDPG-based agents can learn the near-optimal policy to map the environment state of MIMO system to a precoder that maximizes the reward function, respectively, in the codebook-based and non-codebook based MIMO precoding systems.
Off-policy Learning for Remote Electrical Tilt Optimization
In this paper, we circumvent these issues by learning an improved policy in an offline manner using existing data collected on real networks.
Cluster-Aided Mobility Predictions
In this paper, we develop cluster-aided predictors that exploit past trajectories collected from all users to predict the next location of a given user.
