Joint Energy-efficient and Throughput-sufficient Transmissions in 5G Cells with Deep Q-Learning

As a consequence of the 5G network densification and heterogeneity, there is a competitive relationship between the sufficient satisfaction of the cell users and the power-efficiency of 5G transmissions. This paper proposes a Deep Q-Learning (DQL) based power configuration algorithm by jointly optimizing the energy-efficiency (EE) and throughput-adequacy (JET) of 5G cells. The algorithm exploits the user demands to effectively learn-and-improve the user fulfillment rate, while ensuring cost-efficient power adjustment. To evaluate the potency of the developed methodology, several validation setups were conducted comparing the outcomes of the JET-DQL with those derived from conventional power control schemes, namely a Water-filling (WF) algorithm, a weighted minimum mean squared error (WMMSE) method, a heuristic solution and three fixed power allocation policies. JET-DQL algorithm exhibits a remarkable trade-off between the allocated throughput (ensuring high user satisfaction rates and average behavior in total allocated throughput relative to baselines), while resulting into low-valued (almost minimum) power configurations. In particular, even for strict demand scenarios, JET-DQL outperforms the other baselines with respect to EE showing a gain of 2.9-4.5 relative to others, although it does not provide the optimal sum-rate utility and minimum power levels.