Through virtualization and edge computing, 5G promises improved performance, including high data rates, low latency, and increased reliability. Additionally, it is anticipated that 5G systems will be able to handle the increased traffic and demand brought on by the Internet of Things and machine-to-machine communications. Investing in 5G networks comes with risk, despite the obvious advantages it will offer. The 5G implementation needs to be clarified because it has many interconnected variables and unknowns (Nguyen et al., 2021). This section identifies the regulatory, infrastructure, and socio-economic elements that will influence the acceptance and implementation of 5G systems.
Decision-makers can benefit from understanding the features of 5G deployment as they create strategic plans and suggest configuration strategies for various locations. In order to do this, researchers Oughton et al. (2018) created a decision-support model that can measure how well digital infrastructure solutions for mobile digital communications function. By integrating 5G spectrum bands on the current Dutch macrocell network, they could predict the traffic threshold provided per user based on a supply-driven and demand-driven investment analysis based on a case study of the Netherlands. Oughton & Frias (2016) extrapolated 4G characteristics for 2020–2030 to examine the cost, coverage, and deployment implications of 5G networks across Britain. They performed a scenario-based analysis of 5G infrastructure strategies concerning the growth of mobile traffic using Britain as a case study example. Specifically, they examined the marginal impact of population growth on the overall demand for 5G in a UK growth corridor.
According to research by Bhushan et al. (2014), reusing current 4G base stations has an important effect on lowering expenditures when deploying a denser 5G macro base station. Xiao et al. (2019) evaluated the perceived value of an individual spectrum based on the potential influence the spectrum may have in reducing rollout costs. They discovered that adding a spectrum with comparable propagation properties to a coverage-constrained network is useless. Aside from this study, previous research on 5G deployment was primarily theoretical or non-spatiotemporal, and networks were typically built using random processes for evaluation. Depending on the interference and radio channel circumstances, stochastic geometric models may also be employed for system-level performance evaluations.
Additionally, planning for wireless cellular networks can use artificial intelligence and machine learning (Shafin et al., 2020). In order to supply an optimization framework for the cost-effective design of 5G base station networks, researchers created a meta-heuristic method (Aondoakaa, 2018). However, this framework did not consider changes in spatiotemporal development and was primarily used to support decisions about the best base station topology in a 5G mobile network.
In addition to infrastructural technological considerations, external variables, including legislation, the social economy, and the market, significantly impact the pace at which 5G networks are deployed (Campbell et al., 2017). For instance, according to a study by Sun (2019), the Committee on Foreign Investment in the US has tightened its safeguards for 5G technology due to the trade dispute between the US and China. Therefore, these regulations may slow down the US’s global rollout of 5G. Huawei has been prohibited from bidding for more UK 5G infrastructure contracts after the US government voiced concerns about the equipment’s safety. According to research by Oughton & Frias (2018), preventing a major provider of 5G infrastructure from aiding in constructing a nation’s network will raise the UK’s 5G investment costs over the following years. The cost of 5G implementation includes both equipment and spectrum resources. The first auction for the 2.3 GHz and 3.4 GHz 5G spectrum bands was finished in the UK in 2018. Accordingly, the four main mobile network providers EE, Vodafone, O2, and Three, spent close to £1 billion. Mobile network providers taking part in the rollout of 5G will thus face significant investment risks. Mobile network providers are frequently reluctant to engage in low-income regions owing to CapEx and OpEx as well as the lack of energy from the grid, which makes these regions unable to generate significant profits (Medin & Louie, 2019), which is influenced by economies of scale. As a result, this profit-driven rollout strategy may lead to most 5G deployments occurring in metropolitan regions (Kurt et al., 2021). Cheng et al. (2022) evaluated a variety of socio-techno-economic parameters that influenced the adoption and deployment of 5G networks and discovered that performance (0.36), business (0.2), acceptability (0.18), flexibility (0.17), and technology (0.09) had the highest weights among the categories. Bajpai et al. (2018)in their study state that the development of mobile networks has been pushed toward the ecosystem as a result of the shift in 5G deployment from service-driven to market-driven. Mobile internet data traffic has also started to skyrocket as more and more individuals depend on their mobile devices for business or enjoyment. The major cause is the expansion of data-hungry mobile applications like YouTube. Customers will be further encouraged to use 5G technologies in the market by introducing further comparable applications in the future (Campbell, 2017). In addition, 5G is the cornerstone access technology for the Internet of Things.
Additionally, according to Mowla et al. (2017), the information and communication technology (ICT) industry now uses the worldwide power output to the tune of 4.7%. The information and communication technology industry is expected to increase power consumption by 2030 greatly. Since 5G is anticipated to be a crucial technology aiding in solving the problem of growing internet data demand, the energy usage issue will progressively get attention as the 5G rollout moves into commercial mode. Several research projects have aimed to lower wireless cellular networks’ energy usage. According to literature by Chochliouros et al. (2021), to evaluate the power consumption of various base station types under varying traffic loads and quantify the energy efficiency of a wireless cellular network, researchers established an assessment methodology. Shurdi et al. (2021) elaborate that base stations are the primary energy consumers. A base station’s usual peak power is around 6 and 9 kW, respectively. With millimetre wave and 5G new technologies in the existing frequency range, these values will rise to 14 kW and 19 kW, respectively.
Additionally, 5G base stations will be installed at a higher density, resulting in a significant increase in the energy consumption of 5G networks over the following years. In addition, the future power infrastructure may face difficulties due to the ever-increasing power consumption, so building a green 5G network is critical to increasing energy efficiency (Zhang et al., 2019). Wu et al. (2017) elaborate that the energy efficiency of 5G networks may be increased using various methods, including allocation of resources, network design and deployment, energy harvesting and transfer, and hardware solutions. In a recent review and outlook on energy efficiency techniques in ultra-dense wireless heterogeneous networks, researchers also introduced two additional categories of approaches: base station sleeping strategy and radio transmission process optimization (Chang et al., 2020). There has yet to be a thorough investigation of the energy consumption features of 5G networks at the national level since prior research on energy consumption has either been for local networks or a single base station.
References
Aondoakaa, D. T. (2018). Cost-efficient 5G heterogeneous base station deployment using meta-heuristics (Doctoral dissertation, Brunel University London).
Bajpai, N., Biberman, J., & Ye, Y. (2018). National ICT-Driven Development Policy: Comparing Approaches in India and China (No. 2). ICT India Working Paper.
Bhushan, N., Li, J., Malladi, D., Gilmore, R., Brenner, D., Damnjanovic, A., … & Geirhofer, S. (2014). Network densification: the dominant theme for wireless evolution into 5G. IEEE Communications Magazine, 52(2), 82–89.
Campbell, K., Diffley, J., Flanagan, B., Morelli, B., O’Neil, B., & Sideco, F. (2017). The 5G economy: How 5G technology will contribute to the global economy. IHS Economics and IHS Technology, 4(16), 1.
Chang, K. C., Chu, K. C., Wang, H. C., Lin, Y. C., & Pan, J. S. (2020). Energy saving technology of 5G base station based on Internet of Things collaborative control. IEEE Access, 8, 32935-32946.
Cheng, X., Hu, Y., & Varga, L. (2022). 5G network deployment and the associated energy consumption in the UK: A complex systems’ exploration. Technological Forecasting and Social Change, 180, 121672.
Chochliouros, I. P., Kourtis, M. A., Spiliopoulou, A. S., Lazaridis, P., Zaharis, Z., Zarakovitis, C., & Kourtis, A. (2021). Energy efficiency concerns and trends in future 5G network infrastructures. Energies, 14(17), 5392.
Medin, M., & Louie, G. (2019). The 5G ecosystem: Risks and opportunities for DoD. Defense Innovation Board Washington DC United States.
Mowla, M. M., Ahmad, I., Habibi, D., & Phung, Q. V. (2017). A green communication model for 5G systems. IEEE Transactions on Green Communications and Networking, 1(3), 264-280.
Nguyen, H. X., Trestian, R., To, D., & Tatipamula, M. (2021). Digital twin for 5G and beyond. IEEE Communications Magazine, 59(2), 10–15.
Oughton, E., Frias, Z., Russell, T., Sicker, D., & Cleevely, D. D. (2018). Towards 5G: Scenario-based assessment of the future supply and demand for mobile telecommunications infrastructure. Technological Forecasting and Social Change, pp. 133, 141–155.
Oughton, E. J., & Frias, Z. (2016). Exploring the cost, coverage and rollout implications of 5G in Britain. Cambridge: Centre for Risk Studies, Cambridge Judge Business School.
Oughton, E. J., & Frias, Z. (2018). The cost, coverage and rollout implications of 5G infrastructure in Britain. Telecommunications Policy, 42(8), 636–652.
Shafin, R., Liu, L., Chandrasekhar, V., Chen, H., Reed, J., & Zhang, J. C. (2020). Artificial intelligence-enabled cellular networks: A critical path to beyond-5G and 6G. IEEE Wireless Communications, 27(2), 212–217.
Shurdi, O., Ruci, L., Biberaj, A., & Mesi, G. (2021). 5G energy efficiency overview. European Scientific Journal, 17(3), 315-27.
Sun, H. (2019). US-China tech war: Impacts and prospects. China Quarterly of International Strategic Studies, 5(02), 197-212.
Wu, Q., Li, G. Y., Chen, W., Ng, D. W. K., & Schober, R. (2017). An overview of sustainable green 5G networks. IEEE wireless communications, 24(4), 72-80.
Xiao, Y., Krunz, M., & Shu, T. (2019). Multi-operator network sharing for massive IoT. IEEE Communications Magazine, 57(4), 96-101.
Zhang, S., Cai, X., Zhou, W., & Wang, Y. (2019). Green 5G enabling technologies: an overview. IET Communications, 13(2), 135–143.
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