Carbon-Neutral Cellular Network Operation Based on Deep Reinforcement Learning - Hojin Kim; Jaewoo So; Hongseok Kim (Energies, 2022)
With the exponential growth of traffic demand, ultra-dense networks have been proposed to cope with such demand. However, the increase of the network density causes more power use, and carbon neutrality becomes an important concept to decrease the emission and production of carbon. In cellular networks, emission and production can be directly related to power consumption. In this paper, we aim to achieve carbon neutrality, as well as maximize network capacity with given power constraints. We assume that base stations have their own renewable energy sources to generate power. For carbon neutrality, we control the power consumption for base stations by adjusting the transmission power and switching off base stations to balance the generated power. Given such power constraints, our goal is to maximize the network capacity or the rate achievable for the users. To this end, we carefully design the objective function and then propose an efficient Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the objective. A simulation is conducted to validate the benefits of the proposed method. Extensive simulations show that the proposed method can achieve carbon neutrality and provide a better rate than other baseline schemes. Specifically, up to a 63% gain in the reward value was observed in the DDPG algorithm compared to other baseline schemes.