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Flexible functional split in Cloud Radio Access Network (CRAN) greatly overcomes fronthaul capacity and latency challenges. In such architecture, part of the baseband processing is done locally and the remaining is done remotely in the central cloud. On the other hand, Energy Harvesting (EH) technologies are increasingly adopted due to sustainability and economic advantages. Power consumption due to baseband processing has a huge share in the total power consumption breakdown of smaller base stations. Given that such base stations are powered by EH, in addition to QoS constraints, energy availability also conditions the decision on where to place each baseband function in the system. This work focuses on determining the performance bounds of an optimal placement of baseband functional split option in virtualized small cells that are solely powered by EH. The work applies Dynamic Programming (DP), in particular, Shortest Path search is used to determine the optimal functional split option considering traffic QoS requirements and available energy budget.

To meet the growing mobile data traffic demand, Mobile Network Operators (MNOs) are deploying dense infrastructures of small cells as a solution for capacity enhancement. This densification increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering base stations with ambient energy sources to achieve both environmental sustainability and cost reductions. In addition, flexible functional split in Cloud Radio Access Network (CRAN) is a promising solution to overcome the capacity and latency challenges in the fronthaul. In such architecture, local base stations perform partial baseband processing while the remaining part will take place at the central cloud. As the cells become smaller and deployed in a densified manner, it is evident that baseband processing power consumption has a huge share in the total base station power consumption breakdown.In this paper, we propose a network scenario where the baseband processes of the virtual small cells powered solely by energy harvesters and batteries can be opportunistically executed in a grid-connected edge computing server, co-located at the macro base station site. We state the corresponding energy minimization problem and propose multi-agent Reinforcement Learning (RL) to solve it. Distributed Fuzzy Q-Learning and Q-Learning on-line algorithms are tailored for our purposes. Coordination among the multiple agents is favored by broadcasting system level information to the independent learners. The evaluation of the network performance confirms that favoring coordination among the agents via broadcasting may achieve higher system level gains and cumulative rewards closer to the off-line bounds than solutions that are unaware of system level information. Finally, our analysis permits to evaluate the benefits of continuous state/action representation for the learning algorithms in terms of faster convergence, higher cumulative reward and adaptivity to changing environments.

Flexible functional split in Cloud Radio Access Network (CRAN) is a promising approach to overcome the capacity and latency challenges in the fronthaul. In such architecture, the baseband processing takes place partially at local base stations and the remaining processes are executed at the central cloud. On the other hand, we have seen a recent trend of powering base stations with ambient energy sources to achieve both environmental sustainability and profit advantages. As the base stations become smaller and deployed in densified manner, it is evident that baseband processing power consumption has a huge share in the total base station power consumption breakdown. Given that such base stations are powered by energy harvesting sources, energy availability conditions the decision on where to place each baseband function in the system. This work focuses on applying reinforcement learning techniques, in particular Q-learning and SARSA, for optimal placement of baseband functional split options in virtualized small cells that are solely powered by energy harvesting sources. In addition, a comparison of such online optimization solution with respect to offline performance bounds is provided.

Abstract--Due to the tremendous growth in mobile data traffic, cellular networks are witnessing architectural evolutions. Future cellular networks are expected to be extremely dense and complex systems, supporting a high variety of end-devices (e.g., smartphone, sensors, machines) with very diverse QoS requirements. S uch amount of network and end-user devices will consume a high percentage of electricity from the power grid to operate, thus increasing the carbon footprint and the operational expenditures of mobile operators. Therefore, environmental and economical sustainability have been included in the roadmap towards a proper design of the next-generation cellular system. This paper focuses on softwarization paradigm, energy harvesting technologies and optimization tools as enablers of future cellular networks for achieving diverse system requirements, including energy saving. The paper surveys the state-of-the-art literatures embedding softwarization paradigm in densely deployed Radio Access Network (RAN). In addition, the need for energy harvesting technologies in densified RAN is provided with the review of the state-of-the-art proposals on the interaction between softwarization and e nergy harvesting technology. Moreover, the role of optimization tools, such as machine learning, in future RAN with densification paradigm is stated. We have classified available literature that balances these three pillars namely, softwarization, energy harvesting and optimization with densification, being a common RAN deployment trend. Open issues that require further research efforts are also included.