Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network

Abstract

In next-generation centralized or cloud radio access networks (C-RANs), time and wavelength division multiplexed passive optical network (TWDM-PON) has been well recognized as a promising candidate to build the mobile fronthaul. Considering the stringent bandwidth efficiency, latency, and cost requirements in C-RAN, an efficient bandwidth and wavelength allocation scheme is highly desirable for TWDM-PON-based fronthaul. Especially for the massive multiple input multiple outputs (mMIMO) enabled beamforming scenario, the additional radio resource is required to be jointly allocated with bandwidth and wavelength resources in TWDM-PON. In this paper, we formulate the joint allocation problem into an integer linear programming mathematical model and propose a deep reinforcement learning (RL)-based joint allocation scheme with an energy-efficient architecture for the TWDM-PON-based mMIMO fronthaul network. The proposed scheme couples the heuristic radio resource allocation algorithm with the RL-based wavelength allocation model to optimize the fronthaul bandwidth, radio resource, and wavelength utilization efficiencies jointly in the downstream direction. Simulation results show that the proposed scheme achieves a high bandwidth efficiency and high radio resource block utilization simultaneously across different traffic loads and, meanwhile, reduces the wavelength usage compared with the benchmark.

Keywords

• Beamforming
• C-RAN
• pointer network
• resource allocation
• reinforcement learning
• TWDM-PON