Reinforcement Learning-based Dynamic Resource Allocation For Grant-Free Access

Cellular networks have evolved to deliver high-speed broadband services to support the requirements of IoT applications, which demand high speed, low latency, and massive capacity. A primary market goal is to provide support for ultra-reliable low latency communication (URLLC). URLLC requires sub-milliseconds-level latencies as defined by the third generation partnership project (3GPP). One of the promising technologies to achieve the aforementioned specifications is grant-free (GF) access for uplink resources. The GF scheme enables the user equipment (UE) to transmit data over pre-allocated resources which reduces communication latency. This paper proposes an intelligent Reinforcement Learning (RL) based allocator of grants trained via Deep Q-Learning. The experimental results show effect of the number of UEs in the network, and the percentage of unstable UEs on the speed of the RL agent's convergence.