Q-Learning Based Joint Allocation of Fronthaul and Radio Resources in Multiwavelength-Enabled C-RAN

Multi-wavelengths passive optical networks (PONs) such as wavelength division multiplexing (WDM) and time wavelength division multiplexing (TWDM) PONs are outstanding solutions for providing a sufficient bandwidth for mobile front-haul to support C-RAN architecture in 5G mobile network. In this paper a joint allocation framework for multi-wavelength PONs mobile front-haul and C-RAN air interface uplink resources is proposed. From the principle that uplink resource allocation in mobile networks (e.g. 4G and 5G) is an NP-hard optimization problem, this paper contributes with a novel method for uplink scheduling based on a reinforcement learning (RL) algorithm known as Q-Learning. The performance of the algorithm is evaluated with numerical simulations and compared with some other relevant work from the literature such as genetic algorithm (GA) and tabu search (TS). The simulation results show that the new algorithm achieves faster convergence, higher throughput, and minimum scheduling time compared to the two other algorithms. The results also show that RL-based dynamic allocation of front-haul transport block capacity based on actual radio resource block size can greatly reduce front-haul capacity requirement and minimize total end to end uplink scheduling latency.