## 4 Thermal Management of Heat Transfer in 5G Networks Technology
Showing the boundary conditions for the 3D thermal FEM (finite element method) modeling and simulations problem ((**left**): the sandwich wall; (**right**): transmissive wall). Figure shows the outdoor and indoor temperatures and the indoor and outdoor heat transfer coefficients [79]. indicated that the ANFIS-PSO model can achieve commendable prediction accuracy with an R-value exceeding 0.95 for the test data, approaching the theoretical maximum of 1. They showed how, at lower latitudes, the peak heat flow and temperature of the internal air are not observed at noon; instead, the radiation heat flow to the vertical cylinder reaches its maximum between sunrise and noon, as well as between noon and sunset. The integration of AI models into thermal management systems of base stations is thus very promising but it is also associated with the risks of the decision reliability, e.g., AI workloads that may lead to localized hotspots. Thus, the success rate of AI models is limited to the amount of physical sensors that can be installed in the different equipment in order to record and treat as much big data as possible. [PERSON] et al., 2023 [PERSON] et al. (2023) developed a novel active-passive cooling heat sink thermal management system based on PCMs (phase-change materials). Their objective was to enhance the thermal efficiency of the active antenna unit device utilized in 5G base stations. Figure 10 illustrates their computational domain of the proposed innovative active cooling heat sink of phase-change materials (PCMs). Their new management system offered superior performance compared to conventional active cooling heat sinks that do not incorporate PCMs or metal foams. They showed that the maximum temperature of the newly designed heat sink can be reduced by 10 K, while its temperature control efficiency can be improved by as much as 19% compared to conventional heat sinks. Figure 10: Schematic depiction of the computational domain: (**a**) physical model of the PCM-based active cooling heat sink; (**b**) bottom view of the physical model; (**c**) computational unit; (**d**) cross-sectional view of the unit. Adopted, after gaining copyright permission, from ([PERSON] et al., 2023). Figure 9: An intelligent model for predicting the heat flow and maximum plate temperature ([PERSON] et al., 2023). [PERSON], 2021 [82], investigated the potentials of few-layer graphene (FLG) and thermal interface materials (TIMs) in effective device heat dissipation in 5G base station applications. They showed how some adjustments to the concentrations of FLG and TIMs, e.g., from 0 vol.% to a minimum of 7.3 vol.%, can decrease the thermal resistance of the combined TIM and passively cooled heat sink from 4.23 \({}^{\circ}\)C/W to 2.93 \({}^{\circ}\)C/W. They explained that this change corresponds indeed to a reduction in the operating temperature from about 108 \({}^{\circ}\)C to 85 \({}^{\circ}\)C (at a heat dissipation rate of 20 Watts). A coordinated optimization approach for the energy-efficient thermal management of coordinated, optimized HVAC systems and base station equipment using an MILP model was introduced by [PERSON] et al., 2022 [83]. Their application testing was conducted in two base station sites with different types and the application results show that the average energy costs savings in HVAC operation with the approach are 24.8-32.5%; the total BS site costs savings are more than 13%, and the approach is effective in all weather conditions. In addition, the effectiveness of the approach is also demonstrated by the reduction in carbon emissions and the appropriate temperature range of the operating environment. [PERSON] et al., 2023 [84], investigated the heating ventilation and air conditioning (HVAC) system of a 5G base station. They developed a three-stage approach of energy-efficient thermal management by employing Q and imitation learning. Their findings illustrated how the average energy cost of HVAC can be reduced by 18.96%.