Dynamic Subcarrier Assignment in OFDMA-PONs Based on Deep Reinforcement Learning

Abstract

Orthogonal Frequency Division Multiplexing Access Passive Optical Networks (OFDMA-PONs), a solution for the next-generation optical access network, allows multiple optical network units (ONUs) to dynamically share subcarriers (SCs) to support efficient bandwidth allocation. In uplink transmission, multiple ONUs can share orthogonal low bit rate SCs to transmit data at different time slots (TSs) during the transmission cycle. In this paper, the dynamic subcarrier allocation (DSA) scheme based on deep reinforcement learning (DRL) is proposed for various ONU bandwidth requests. The novel scheme jointly allocates time slots, subcarriers, and modulation formats in a dynamic and flexible manner. The ONU can save transmit power by using a lower order modulation format while meeting the delay requirement. The simulation part demonstrates how the proposed DRL-based DSA scheme can be adapted to various situations, including 1) variation in the size of ONU bandwidth requests, and 2) variation in the weight of different indicators. The extensive simulation results show that, for the first time, the proposed DRL-based DSA algorithm achieves optimal traffic latency with substantial power saving, compared with the traditional two-dimensional DSA algorithms.

Keywords

• Energy-saving
• deep reinforcement learning
• dynamic subcarrier assignment (DBA)
• orthogonal frequency division multiplexing access passive optical network (OFDMA- PON)