## 6 Final Remarks  
EE has recently attracted the attention of researchers in the mobile communication field due to the increasingly disastrous impact on the environment caused by less energy-efficient applications. Generally, increasing the QoS of the users or throughput increases the grid power consumption of the communication system. On the other hand, a massive number of users leads to interference issues as well as increased energy demand. Further, the number of users is not always significant, leading to energy wastage during off-peak hours. Hence, it is crucial to switch off underutilised BSs and allocating UE power, which simultaneously solves throughput as well as power consumption issues. This work proposes an energy-efficient joint BS-Sw and power allocation scheme for a CoMP-based Het-Net architecture with hybrid power sources. By using a combinatorial (sub-gradient-binomial or sub-gradient-heuristic) optimisation technique, joint BS-Sw and power allocation yields about 15-20% higher EE compared to the non-cooperative or non-harvesting systems. The proposed distance-based BS-Sw method is proven to perform better in terms of EE compared to the random method in all tested scenarios. Though its EE performance is around 5% poorer than the optimal binomial method, the computational complexity is proven to be much lesser than the former. Overall, the distance-based BS-Sw method of the cooperative harvesting system performs the best in terms of both performance gain and computational complexity. The application of BS-Sw to MBS would greatly enhance the EE of the system, but in order to avoid a coverage hole, techniques such as cell zooming have to be employed, which is left for future work. Apart from that, the application of a smart grid is not limited to only energy sharing, it can be extended to more advanced time-series-based energy and traffic management. The energy harvesting and traffic model used in this work is based on instantaneous values. For more practical applications, the trending machine learning techniques can be utilised to learn the energy and traffic pattern sampled through the time-series. Consequently, the overall system performance can be enhanced with reduced complexity caused by real-time computations.  
Conceptualisation, S.E.; methodology, S.E.; software, S.E.; validation, S.E.; formal analysis, S.E.; investigation, S.E.; resources, S.E.; data curation, S.E.; writing--original draft preparation, S.E.; writing--review and editing, Y.H.N. and C.K.T.; visualisation, S.E.; supervision, Y.H.N. and C.K.T.; project administration, Y.H.N. and C.K.T.; funding acquisition, Y.H.N. and C.K.T. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Convergence behaviour of [PERSON]’s algorithm for \(E_{M}^{HC}=10\) dB and four SBSs with \(E_{S}^{HC}=2\) dB.  
**Funding:** This research was funded by Multimedia University grant number MMUI180002.02. and the APC was funded by Multimedia University.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy considerations.  
**Conflicts of Interest:** The authors declare no conflict of interest.