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Review

A Review on Thermal Management and Heat Dissipation Strategies for 5G and 6G Base Stations: Challenges and Solutions

by
Talib Dbouk
1,* and
Oumar Mourad
2,†
1
Complexe de Recherche Interprofessionnel en Aérothermochimie (CORIA)–Unité Mixte de Recherche (UMR) 6614, Centre National de la Recherche Scientifique (CNRS), University of Rouen Normandy, 76000 Rouen, France
2
Faculty of Engineering, Holy Spirit University of Kaslik, Kaslik P.O. Box 446, Lebanon
*
Author to whom correspondence should be addressed.
†
Current address: CNRS UMR 6614, CORIA, Université de Rouen, Site Universitaire du Madrillet 675, Avenue de l’Université, BP 12, 76801 Saint-Étienne-du-Rouvray, France.
Energies 2025, 18(6), 1355; https://doi.org/10.3390/en18061355
Submission received: 16 February 2025 / Revised: 4 March 2025 / Accepted: 5 March 2025 / Published: 10 March 2025
(This article belongs to the Special Issue Heat Transfer Principles and Applications)
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Abstract

:
A literature review is presented on energy consumption and heat transfer in recent fifth-generation (5G) antennas in network base stations. The review emphasizes on the role of computational science in addressing emerging design challenges for the coming 6G technology, such as reducing energy consumption and enhancing equipment thermal management in more compact designs. It examines the contributions of (i) advanced modeling and simulation sciences, including antenna modeling and design, the use of (ii) computational fluid dynamics (CFD) and heat transfer, and (iii) the application of artificial intelligence (AI) in these settings. The scientific interactions and collaborations between these scientific multidisciplinary approaches are vital in the effort to develop innovative 6G thermal equipment designs. This is essential if we are to overcome the current scientific barriers and challenges faced by this evolving technology, where the rapid transition from 5G to 6G will shape the expanding fields of deploying smaller satellites into lower orbits in outer space.

1. Introduction

Energy consumption, intelligent thermal management, and the cooling down of electronic devices in last-generation mobile telecommunication networks and base station antennas are all serious topics for research and development in the coming years.
To begin with some history, the beginning of voice services in the 1980s marked the first generation (1G) of mobile communication antennas networks. This technology faced many barriers such as unreliable delivery, reduced capacity, and security weakness. Second-generation (2G) networks were then developed in the 1990s to overcome the challenges of 1G by employing a global system for mobile communications (GSM) and by introducing encrypted data services such as the Short Message Service (SMS) interface. With an increasing demand for internet browsing, 3G mobile communication networks arrived by 2000, employing the Code Division Multiple Access (CDMA) and Frequency Division Multiple Access (FDMA) technologies, which allowed 3G networks to achieve data rate speeds of up to 14 megabytes per second [1]. The year 2009 then witnessed the arrival of 4G network technologies [2] as long-term evolution (LTE) networks. The arrival of 4G significantly improved data rates in comparison to previous network generations and enabled mobile broadband connections with enhanced transmission bandwidths.
Today, the present 5G technology employs non-orthogonal multiple access (NOMA) and enables data rates up to 100 gigabytes per second [3]. Future 6G networks will embed AI-based transceivers that can reach up to one terabyte per second [4,5] and will thus offer ultra-high data rate speeds with widespread connectivity overall. Thus, future thermal management challenges will evolve with the advent of 6G due to the continuous increase in data rates and device density. This will lead to higher heat flux and thermal power that should be better removed with enhanced thermal management systems to achieve good control of critical temperatures and operating conditions.
Energy consumption and equipment overheating [6,7] presently pose serious questions for research and development, especially when it comes to reliable 5G and 6G networks. This is due to the spacial dispersive installation and the density of structures, in addition to the continuous rising demand among the population for higher-speed data rates [8].
Equipment overheating and the high energy consumption of electric devices will need further research and development; it is important that we seek enhanced solutions for a better, more sustainable future [9]. In fact, the rapid transition from 5G to 6G networks will bring changes in energy consumption and heat transfer, pushing the boundaries of mobile telecommunication networks through faster data rates, higher frequencies, and a tremendous number of devices that are connected over the net. This will induce a noticeable increase in the energy demands and the overall heat production from associated equipment and electronic devices.
Future solutions must rely on advanced heat transfer and electronic device cooling methods [10]. AI-based thermal management protocols [11,12] should be integrated with energy-efficiency strategies [13]. The degree to which we can retain the high performance level of electronic devices, along with reduced reliable power consumption, will define the overall efficiency of future 6G networks, and thus will shape their level of sustainability in the coming future.
This review of the scientific literature is developed and presented in order to explore various aspects of energy consumption and thermal management strategies in last-generation mobile telecommunication base station antennas and networks. A focus will be given to the major factors contributing to energy consumption in last-generation mobile telecommunication networks by emphasizing the role of advanced computational sciences like modeling, simulation with computational fluid dynamics (CFD), and artificial intelligence (AI) in overcoming future challenges in terms of the enhanced thermal management of electric devices in 5G and 6G networks.

2. Major Factors Contributing to Energy Consumption in 5G Network Technologies

One of the primary drivers of energy consumption in last-generation mobile telecommunication is the denser deployment of base stations and small cells compared to previous generations [14,15], as shown in Figure 1.
The need for more infrastructure to support higher frequencies and increased data traffic can result in significantly higher energy usage. The following aspects of 5G deployment are the primary factors that are expected to contribute to higher energy consumption in the future:
  • Advanced antenna technologies can enhance network performance but will also lead to greater energy consumption [16]. Denser base station antenna infrastructures, such as the transition from 4–8 antennas to 64–128 antennas, will induce higher energy demands.
  • The increasing demand for faster data transfer in 5G networks will require more advanced components and devices, thus inducing higher energy consumption [17,18]. As an example, one can mention the transition from homogeneous networks (comprising 1 to 3 base stations (BSs) per km2) to heterogeneous networks (comprising 10 to 100 nodes per km2). Furthermore, the growing need for larger storage capacities adds to energy requirements.
  • The shift to higher frequency bands [19,20] (from 100 MHz up to 5 GHz) in 5G networks will require an increase in the number of base station antennas; this, in turn, will increase energy demands.

3. Energy-Efficiency Solutions and Technologies

To overcome the energy consumption challenges of 5G network technologies, researchers have been tackling various angles of the relevant topics, such as:
  • The dynamic power management (DPM) techniques that usually turn off the base stations during low-traffic periods, which can significantly reduce the overall energy consumption [21]. Figure 2 illustrates a power state machine representing a microprocessor with three power states: RUN, IDLE, and SLEEP [22]. CMOS technology induces more power consumption due to the dynamic electronic switching and the static electronic leakage components; here, the aim of DPM is to reduce static power consumption, whereas the aim of DVFS is to minimize dynamic power consumption [23].
  • Energy-efficient hardware components—such as advanced power amplifiers [24], small cells [25], low-power modems [26], edge computing [27], processors [28], cooling systems [29,30], and AI-powered network management [31,32] (Figure 3)—can all significantly contribute to energy savings in 5G networks.
  • Efficient spectrum management strategies can also reduce energy consumption by optimizing the usage of different resources [33,34]. For example, massive MIMO (multiple input–multiple output) technologies are vital for 5G and beyond (Figure 3); these employ a large number of antennas at a given base station. Within the same frequency band, this allows the station to serve multiple users. Thus, massive MIMO technologies can reduce energy consumption by adjusting the overall transmission power with optimal coverage [35].
  • The dense deployment of base stations and interconnected equipment and devices is well-suited for integrating renewable energy sources [36,37], such as wind and solar energy sources; this is especially the case in regions where power grid extension is not feasible. Industries that corporate renewable energy sources into their 5G networks may importantly reduce their dependence on fossil fuels in addition to reducing their overall carbon footprints.
Many researchers have been developing several advanced modeling and simulation methods in order to better quantify the energy consumption of 5G networks under different conditions. Kamal et al., 2021 [38], addressed resource allocation and the management of dynamic power via multiple strategies such as small cell deployment and the integration of scheduling algorithms [39]. This will provide telecommunication operators and network designers with tools that can enhance overall energy efficiency. For the operators of 5G networks, the objective was to create a balance between the demands for faster speeds, massive connectivity, and lower latency, all accompanied by the aim of minimizing overall operational costs. These primary modeling and simulation works [38,39] provide significant insights for future energy-efficient new-generation network infrastructures.
Today, the ease of access to big data from diverse sources allow telecommunication and network designers and operators to better integrate artificial intelligence (AI) tools in their practices, enabling them to better enhance energy consumption while satisfying network reliability and ensuring correct functioning. Such AI-data-driven methods are vital for constituting energy-efficient 5G and future 6G networks.
The complexity of 5G networks, with denser deployment of small cells (e.g., advanced antenna equipment and higher-frequency bands) contribute to larger power demands compared to previous G-networks. To overcome these challenges, researchers have recently been trying to develop different advanced energy-efficient methods [25,40,41]. These can be summarized in the following categories: research works on the enhanced management of dynamic power [42,43]; research works on AI-driven optimization methods [44]; and research works on the integration of renewable energy sources [36,37]. All these efforts combined will contribute to the enhancement of the energy efficiency of 5G networks and thus facilitate our movement towards 6G sustainable technology.
Cai et al., 2024 [45], proposed an innovative design of distributed photovoltaic 5G base station micro-grid structures. Through an energy management strategy, they managed to enhance the energy efficiency of 5G base stations by integrating a maximum power point tracking (MPPT) algorithm for improved information passage and better energy flows. However, the cooling of PV cells is still required and should be integrated into thermal management systems in order to increase the overall efficiency of the cells and, at the same time, ensure control of the temperatures of the different electronic devices and thier components.
Cabrera et al., 2023 [46], shed light on energy consumption in 5G networks; this remains a multifaceted challenge due to the continuous growth in demand. Researchers like Yang et al., 2019 [47] and Jobby et al., 2023 [48] have been exploring innovative solutions in attempts to improve energy efficiency. Further research and development efforts are required to better assist in the evolution of 5G networks towards 6G technology.
Wang et al., 2022, developed a joint optimization algorithm to better control base station states and the association of users in wireless caching networks [49]. Amine et al., 2022 [50], developed a reinforcement learning technique that is applied for energy optimization in 5G heterogeneous networks employing multi-sleeping controls.

Energy Consumption: Future Directions and Challenges

In terms of futures directions, it is obvious that research works on the energy consumption of 5G networks are still rare and disperse. This points to the urgent requirement that we develop future research by combining efforts from interdisciplinary sciences. A good direction for this is to couple artificial intelligence with renewable energy sources in pursuit of the goal of reducing the energy consumption of 5G and 6G technologies for a sustainable future.

4. Thermal Management of Heat Transfer in 5G Networks Technology

With the emergence of 5G networks, thermal management has become a critical aspect of device design; this is especially the case when it comes to the ongoing increase in the density of large-scale integration circuits. This is due to the changes in device behavior and the elevated local temperatures [51,52,53,54,55], even with involving renewable energy sources such photovoltaic solar cells and panels. It is worth noting that the efficiency of photovoltaic cells usually decreases at higher operating temperatures [56,57].
Heat transfer in 5G networks occurs through convection, conduction, and radiation mechanisms. It takes place in many forms of equipment and devices such as antennas, chips, processors, and power amplifiers. Thermal management strategies are vital in overcoming the challenges posed by the overheating of these devices. It is important to ensure the correct functioning of the 5G network overall, as it is transitioning rapidly towards a 6G technology network.
Today, we thus need more advanced solutions, such as innovative cooling techniques and AI-based thermal optimization, to sustain the effective performance of future mobile telecommunication technologies.
The utilization of cryogenically cooled amplifiers is well established in the field of radio astronomy [58,59,60]; this started around 2008, ad has become economically viable in application in 3G technology system base stations (BSs). A detailed review of cryogenic receivers in commercial wireless applications can be found in [61]. Narahashi et al., 2008 [62], introduced a cryogenic receiver front-end operating in the 2 GHz band; it features HTSFs and a cryogenically cooled low-noise amplifier. The progress in wireless BSs utilizing cryogenic technologies are discussed in the literature [63,64,65], with receiver front-ends having applications in their extension into LTE networks [66].
Aslan et al., 2019 [67] addressed a fully passive cooling approach using double-sided printed circuit board (PCB) configuration for antenna arrays. In comparison to conventional structures, their research findings indicated that utilizing a thicker ground plane leads to a better thermal performance. Employing a passive fin heat sink for heat dissipation, Zhang et al., 2022 [68], conducted a numerical analysis to quantify the cooling efficiencies of a 5G active antenna unit (AAU). Their works illustrated that the cooling efficiency improves with an increase in the number of fins.
With their relatively low heat transfer rates that present a significant limitation, passive cooling techniques are commonly used in base stations [69] which require further optimization [70] for 5G and 6G systems. Currently, the majority of research concerning heat dissipation in 5G base stations is primarily focusing on passive cooling methods. Today, there is a clear gap in the literature in terms of research investigations that tend to quantify the temperature performances in 5G electronic devices. It is important to note that an effective thermal management system is crucial in ensuring the good operational efficiency of 5G equipment [71].
Phase-change materials (PCMs) are recognized for their ability to handle superior temperature control within a well-defined time period. Thus, their integration with heat sinks can be a promising approach for enhancing the thermal performance of electronic devices [72]. The advantage of PCMs over other materials is that, by controlling their volume fraction, in combination with heat sinks integration, their operating time to reach a set point temperature (SPT) can be controlled (see Kothari et al., 2020 [73]). As an example, Fok et al., 2010 [74] conducted an experimental analysis to quantify the cooling efficiency of portable electronic devices employing a PCM-based heat sink with fins. Gharbi et al., 2015 [75], conducted an experimental investigation on the thermal performance of several PCM-based heat sink cases, using pure PCM, silicone–PCM composites, and graphite–PCM composites. Their research findings illustrated how PCM can significantly reduce overall temperatures. Senthilkumar et al., 2024 [76], discussed the important role of various materials, such as hydrogels, metal–organic frameworks, and PCMs, in dissipating heat in 5G-enabled portable electronics in addition to their potential challenges and improvements.
Different thermal and electromagnetic (EM) simulations of several antenna (or chip) layouts in planar AESA’s were conducted and presented in the literature by Aslan et al., 2018 and Aslan et al., 2019 [67,77,78]. For example, simple patch antennas were used by the authors in their simulations (see Figure 4, Figure 5 and Figure 6). The simulation settings in and realization of the 3D model were described and the thermal and EM performances of a single element in a unit cell was studied. Both EM and thermal considerations are jointly incorporated in the optimization of array layouts, with a novel connection established between layout sparsity and thermal management. Aslan et al., 2018 and 2019 [67,77,78], showed that fully passive cooling can be achieved by attaching CPU fanless coolers to the chips (see Figure 4 and Figure 5). Their results showed that a much better cooling performance can be obtained by employing relatively thick ground planes with extended aperture sizes. This can be achieved without any effect for the electromagnetic properties.
Current cellular networks (5G) face greater penetration loss through buildings than 4G due to their higher frequency bands. Lu et al., 2023 [79], developed an innovative passive antenna system that is integrated into sandwich walls in energy-efficient buildings. Lu et al. (2013) [79] employed a three-dimensional finite element method (FEM) in steady-state thermal analysis. Their investigations primarily covered three types of walls: a sandwich wall, a signal-transmissive wall with stainless steel as connector (new design), and a signal-transmissive wall with copper (old design).
In their research, Lu et al. (2013) [79] conducted three-dimensional heat transfer simulations (see Figure 7 and Figure 8) to determine the thermal transmittance (U-values) of 5G antenna walls. Their results revealed that, using stainless steel as the connector material (new design), the U-value rose from 0.1496 (for the wall without antenna) to 0.156 W/m2K, leading to an additional heating loss per year of only 0.545 KWh/m2 in Helsinki. In contrast, with the old design that used copper as the connector material, the U-value increased dramatically to 0.3 W/m2K, exceeding the National Building Code of Finland’s limit of 0.17 W/m2K and causing additional heat loss of 12.8 KWh/m2 (23.5 times more than the new design). The new design significantly reduced the thermal bridging effects.
An intelligent AI model for the heat transfer modeling of 5G Smart Poles was developed by Khosravi et al., 2021 [80]. The authors ensured heat balance across three different surfaces of a cylindrical utility box; they covered the side exposed to solar radiation, the side where the electrically heated plate is located within the cylinder, and the side that represents the shaded side of the cylinder. Figure 9 shows an example of the integration of the AI model of Khosravi et al., 2021 [80], into the thermal design process of base station in order to intelligently control the heat flow and the maximum temperature. The input parameters they utilized for developing their AI model (see Figure 9) were as follows: the station’s latitude, ambient temperature, internal air flow-rate, and time. The used these parameters to forecast the local heat flow in Watts and the maximum temperature of the plate within the utility box (see Figure 9). The findings of Khosravi et al., 2021 [80], 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.
Zhang et al., 2023 [81] 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.
Lewis, 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 °C/W to 2.93 °C/W. They explained that this change corresponds indeed to a reduction in the operating temperature from about 108 °C to 85 °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 Liu 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. Lieu 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%.
Luo et al., 2025 [85], developed an advanced topology design method for the optimal design of extruded fins in a 3D turbulent natural convection system to improve the cooling efficiency of chips within a 5G active antenna unit. They employed three-dimensional fluid flow modeling with standard k– ε turbulence model. Their findings showed that the hollowed center and the heterogeneous distribution of pillars are two critical optimized features in the multi-finned thermal design problem, contributing nearly 30% to the overall enhancement of heat transfer. Applied to two 20 Watts chips, they revealed how their design method, with a fin volume fraction of 5%, can reduce the maximum temperature by over 10 K (and increase the Nusselt number by at least 14.6%) when compared to conventional pin and plate fins at the same value of volume fraction.
Deng et al., 2025 [86], developed a new phase-change device (called the roll-bond flat heat pipe (RBFHP)) that is easy to fabricate and apply on large surfaces for cooling base stations. The authors studied multi-heat-source scenarios under natural-air-cooling conditions, investigating the thermal performances and power distribution modes. Their findings revealed how their new device provides a better thermal performance compared to traditional devices.
Meng et al., 2024 [87], developed a parallel hybrid cooling system of thermosyphon and vapor compression to cool down 5G base stations. Their research findings revealed that operational parameters, including the set points for return air temperature and the speeds of the evaporator and condenser fans, can significantly influence the utilization ratio of the natural cooling source in the hybrid cooling system, thereby impacting the system’s energy efficiency ratio (EER). They illustrated, for example, that when the outdoor temperature falls below 20 °C, the local variations in the evaporator fan speed notably affect the system’s operating mode. Thus, by selecting an optimal evaporator fan speed, one may reduce the compressor’s operating time to 0%. Moreover, at a temperature of 15 °C outdoors, a fine-tuning of the evaporator and condenser fan speeds can enhance, respectively, the EER by about 60% and 52%. If the outdoor temperature is above 25 °C, the system can operate in air-conditioning mode, leading to minimal influence of fan speeds on the EER. Meng et al., 2024 [87] explained that, by optimizing both the evaporator fan speed and the set point temperature, the EER can be increased up to 17.19.
Feng et al., 2024 [88], proposed a new heat sink solution based on a microchannel thermosyphon array with air cooling; this was an attempt to optimize the design of 5G heat-dissipation devices. Their experimental measurements focused on the temperature uniformity across various filling ratios, heating power levels, and wind speeds. Their research findings revealed that the optimal filling ratio for the microchannel thermosyphon array is about 20%. The latter had a heating power of 80 W. They showed that the thermosyphon exhibits a thermal resistance of about 0.28 °C/W. Moreover, Feng et al., 2024 [88], explained that, if one increases the input power, then one can facilitate the startup process and thus promote a transition into a stable fluid flow and heat transfer phase. They showed that the microchannel thermosyphon array has a power limit of 40 W under natural convection conditions; however, at a wind speed of 6 m/s, the power limit increases to 140 W, inducing a thermal resistance of less than 0.4 °C/W; here, the temperature does not exceed a value of 74.3 °C. These research findings offer valuable insights for the optimization of 5G heat-dissipation device designs.
Many authors have been trying over the years to develop enhanced liquid-based coolers of base transceiver stations [89]. For example, Figure 11 illustrates an enhanced liquid-cooled base transceiver station (BTS) developed by Huttunen et al., 2020 [90], compared to an old one with a traditional heat sink.
Some authors, like Markiewicz et al., 2019 [91], have examined the theoretical concept of enhancing the performance of communication systems through the application of cryogenic cooling to their RF front-ends. This technique has garnered limited attention in the context of wireless communications. The primary theoretical analysis results obtained by Markiewicz et al., 2019 [91], are very promising and suggest a significant increase in channel capacity, which could lead to improved spectral efficiency, extended range, or reduced power output from mobile stations. This technique has wide potential applications in base stations for machine-type communication (MTC) and Internet of Things (IoT) equipment.

Thermal Management: Future Directions and Challenges

The increasing demands in power generation and heat release from 5G base station equipment and electronic devices require further research and development efforts. This is to propose new optimal designs of enhanced thermal management and more efficient heat transfer in circuit boards, components cabinets, and amplifier devices. Future solutions can be summarized by the following guidelines in computational science and design process:
  • Enhanced designs of future generations of antennas and electronic chips and components for reduced energy consumption.
  • Enhanced designs of heat sinks and coolers that employ innovative new technologies, such as non-traditional particle–fluid-based techniques [92,93,94], unconventional heat sinks design [95], innovative thermo–fluid topology designs [96,97,98,99], and shape optimization methods [100,101,102].

5. Conclusions

In conclusion, the present literature review on the thermal management and energy efficiency of 5G antenna network base stations has identified significant gaps, where future research efforts are required. The major identified research gaps are particularly in the fields of the optimization of hybrid cooling systems and in the integration of renewable energy and AI models within 5G and 6G thermal management. This is in addition to the important future role of developing advanced cryogenic cooling systems, enhanced phase-change materials, and the integration of complex thermal–fluid flows and systems (e.g., nanofluids and particulate flows). To overcome the challenges of higher speed rates, inducing higher energy consumption and the overheating of electronic devices, advanced research is required for identifying nd developing improved materials, enhanced thermal management components, and systems including innovative energy-efficient designs. Innovative heat-dissipation solutions are necessary in preventing overheating and ensuring the reliable operation of future antennas and equipment. Energy consumption reduction should be developed in combination with a reduction in operational costs, all while retaining respect for the environment. Future developments should focus on developing both passive and active thermal management methods, accompanied by enhanced antenna designs that will combine the optimal performances, efficiencies, resilience, and sustainability. These should be pursued in balance with the continuous expansion of space markets, which are deploying more and increasingly smaller satellites in outer space.

Author Contributions

Conceptualization, T.D.; methodology, T.D.; investigation, T.D.; original draft, T.D.; review and editing, O.M.; methodology, O.M.; investigation, O.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data and information are available upon request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Fifth-generation infrastructures, showing the dense deployment of base stations and interconnected equipment and devices. Adopted from [15].
Figure 1. Fifth-generation infrastructures, showing the dense deployment of base stations and interconnected equipment and devices. Adopted from [15].
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Figure 2. An example of a power state machine for a Strong−ARM SA−1100 processor.
Figure 2. An example of a power state machine for a Strong−ARM SA−1100 processor.
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Figure 3. AI energy-saving-based techniques for 5G base stations [32].
Figure 3. AI energy-saving-based techniques for 5G base stations [32].
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Figure 4. EM design parameters in a unit cell: (a) top view and (b) bottom view [67].
Figure 4. EM design parameters in a unit cell: (a) top view and (b) bottom view [67].
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Figure 5. Thermal design parameters in a unit cell: (a) top view and (b) bottom view [67].
Figure 5. Thermal design parameters in a unit cell: (a) top view and (b) bottom view [67].
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Figure 6. Design of a unit cell: (a) back side with a chip and (b) front side with a patch [67].
Figure 6. Design of a unit cell: (a) back side with a chip and (b) front side with a patch [67].
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Figure 7. Sandwich wall: ultra-wide-band back-to-back in wall spiral antenna system with two identical spiral antennas and a semi-rigid dual coaxial cable. 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].
Figure 7. Sandwich wall: ultra-wide-band back-to-back in wall spiral antenna system with two identical spiral antennas and a semi-rigid dual coaxial cable. 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].
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Figure 8. Enhanced local heat flow lines thanks to numerical modeling and design. (a) Nine-signal-transmissive wall with stainless steel (new design); (b) nine-signal-transmissive wall with copper (old design) [79].
Figure 8. Enhanced local heat flow lines thanks to numerical modeling and design. (a) Nine-signal-transmissive wall with stainless steel (new design); (b) nine-signal-transmissive wall with copper (old design) [79].
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Figure 9. An intelligent model for predicting the heat flow and maximum plate temperature [80].
Figure 9. An intelligent model for predicting the heat flow and maximum plate temperature [80].
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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 [81].
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 [81].
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Figure 11. (a) Air-cooled base transceiver station (BTS) unit with a heat sink; (b) the same BTS unit with a liquid cooling modification [90].
Figure 11. (a) Air-cooled base transceiver station (BTS) unit with a heat sink; (b) the same BTS unit with a liquid cooling modification [90].
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Dbouk, T.; Mourad, O. A Review on Thermal Management and Heat Dissipation Strategies for 5G and 6G Base Stations: Challenges and Solutions. Energies 2025, 18, 1355. https://doi.org/10.3390/en18061355

AMA Style

Dbouk T, Mourad O. A Review on Thermal Management and Heat Dissipation Strategies for 5G and 6G Base Stations: Challenges and Solutions. Energies. 2025; 18(6):1355. https://doi.org/10.3390/en18061355

Chicago/Turabian Style

Dbouk, Talib, and Oumar Mourad. 2025. "A Review on Thermal Management and Heat Dissipation Strategies for 5G and 6G Base Stations: Challenges and Solutions" Energies 18, no. 6: 1355. https://doi.org/10.3390/en18061355

APA Style

Dbouk, T., & Mourad, O. (2025). A Review on Thermal Management and Heat Dissipation Strategies for 5G and 6G Base Stations: Challenges and Solutions. Energies, 18(6), 1355. https://doi.org/10.3390/en18061355

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