Abstract

A channel and latency aware radio resource allocation algorithm based on deep reinforcement learning (DRL) is proposed and evaluated. The proposed scheme aims to optimize the uplink scheduling for service-oriented multi-user millimeter wave (mmWave) radio access networks (RAN) in the 5G era. In the DRL system, multiple application flows are implemented with various statistical models and the key function modules of the system are designed to reflect the operation and requirements of service-oriented RANs. In particular, the mmWave channel characteristics utilized in the system are collected experimentally and verified via a radio-over-fiber (RoF)-mmWave testbed with dynamic channel variations. Results show that the proposed DRL algorithm can operate adaptively to channel variations and achieve at least 12% average reward improvement compared to conventional single-rule schemes, providing joint improvement of bit error rate and latency performance.

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