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Found 5 papers, 0 papers with code
Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR
We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i. e., requested quality of service (QoS)) and random traffic arrival.
Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things
In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation offloading decision and multichannel access policy with corresponding signaling.
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling
In this paper, we apply an multi-agent reinforcement learning (MARL) framework allowing the base station (BS) and the user equipments (UEs) to jointly learn a channel access policy and its signaling in a wireless multiple access scenario.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning
In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) to come up with a medium access control (MAC) protocol in a multiple access scenario.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks
We design a self-exploratory reinforcement learning (RL) framework, based on the Q-learning algorithm, that enables the base station (BS) to choose a suitable modulation and coding scheme (MCS) that maximizes the spectral efficiency while maintaining a low block error rate (BLER).
