Maurilio Matracia, Mustafa A. Kishk, Mohamed-Slim Alouini
©SHUTTERSTOCK.COM/LUCIAN COMAN
Even though achieving global connectivity represents one of the main goals of 5G and beyond wireless networks, exurban areas are still suffering frequent outages because of the lack of proper telecom infrastructures, which are often available only in urban areas. Indeed, cellular network design is usually capacity driven, and thus the densities of base stations (BSs) follow mostly population and especially revenue densities. Contextually, we focus on one of the most promising solutions to provide sufficient and reliable coverage in far-flung areas: aerial base stations (ABSs), which consist of unmanned aerial vehicles (UAVs) carrying cellular BS equipment. In this article, we extensively discuss the problem of bridging what is called the urban–rural digital divide (i.e., the connectivity gap between urban and rural areas) from various perspectives. First, we showcase various alternative solutions and compare conventional terrestrial networks with aerial networks from a techno-economic point of view. Then, we highlight the topological aspects of rural environments and explain how they can affect the actual design of cellular networks. In addition, we investigate both the coverage probability and the reliability of the communication links via simulations, proving that the integration of ABSs can be quite promising in a 6G perspective. Finally, we propose two original extensions of our case study as open problems.
According to the International Telecommunication Union, last year roughly 2.9 billion people were still either unconnected or underconnected, and only 27% of the overall population of least developing countries could enjoy the benefits of an Internet connection. On top of that, the consequences of these numbers have also gained importance because of the COVID-19 pandemic. Thus, in the context of target 9c of the United Nations’ Sustainable Development Goals (SDGs), wireless networks should aim to offer universal and affordable access to broadband connectivity.
In fact, providing rural and remote areas with information and communications technologies can be the key to promote communities’ cultural and economic growth: countless applications, including precision agriculture, weather monitoring, and online services (for example, in the fields of education, commerce, finance, government, entertainment, and health care), depend on the presence of reliable communications systems.
However, the high costs of power, backhaul, installation/maintenance, and security, combined with the low population density and average revenue per user characterizing exurban areas, make them unappealing to telecom providers. Additionally, far-flung areas often lack a reliable power source, which is vital for any communications system, and present challenging topographies. Finally, we recall that a large percentage of telecom infrastructures in small islands and low-income exurban areas are powered by diesel generators, which have a high environmental impact and hence cannot be considered as a viable large-scale solution to mitigate climate change, as required by the 13th SDG.
Among various potential solutions, ABSs based on drones or other types of aerial platforms, for example, airships or gliders, are probably the most promising because of their multiple advantages, such as low cost and power consumption, long coverage radius, and high mobility. Nonetheless, several technological challenges (especially in terms of autonomy) still need to be overcome to make ABSs commercially viable in most rural areas.
This article’s main contributions are the following:
The rest of this work is structured as follows. The next section proposes a concise survey on rural connectivity, focusing on alternative solutions and techno-economic aspects of both aerial and conventional terrestrial infrastructures, while the section “Topological Aspects of Rural Network Design” discusses the topological aspects that need to be taken into account when designing and evaluating rural cellular networks. Next, we propose a realistic case study in the section “Network Performances,” and we use the obtained simulation results (in terms of coverage probability and SINR meta distribution) to extract fruitful insights for future rural cellular network design. Finally, we propose our follow-up open problems in the section “Open Problems” before concluding the article in the section “Summary and Conclusion.”
Potential solutions for bridging the digital divide should be tailored to the specific area under consideration and its respective users while meeting all of the regulatory, social, and economic constraints. Contextually, an interesting framework that finely quantifies the digital imbalance has been proposed in [2]. Then, the network infrastructure should be sized by estimating multiple factors, such as the current population density and its expected evolution as well as the percentage of simultaneously active users and their needs in terms of data rate.
This section overviews the main alternative technologies and platforms (also displayed in Figure 1) for achieving global connectivity and compares conventional terrestrial infrastructures and aerial ones from a techno-economic point of view. In particular, in the next section on alternative solutions we briefly discuss four paradigms, namely satellites, wind-turbine-mounted base stations (WTBSs), television white space (TVWS), and aerial platforms. Then the following section focuses on the conventional terrestrial infrastructure and the aerial one, providing details about their components and their respective costs.
Figure 1 The main types of platforms for terrestrial, aerial, and space communications for exurban networks. LEO: low-Earth orbit.
In this section, we concisely overview other potential solutions to bridge the digital divide. However, for a broader overview of terrestrial, aerial, and space networks in far-flung areas, the reader can refer to works such as [3].
Satellites are considered as a disruptive technology for 5G and beyond networks, especially because of their potential to significantly boost coverage in far-flung areas. Companies such as SpaceX, Amazon, OneWeb, LeoSat, and Boeing are now competing in this new global market by deploying large satellite constellations that cover large portions of the Earth. However, as of today, commercial low-Earth orbit (LEO) satellites cannot provide access directly to cellular users since they require an intermediate dish, which is usually installed on the rooftop of the subscriber’s house (although there are recent collaborations among companies such as Apple, Globalstar, and Qualcomm, or SpaceX and T-Mobile, that plan to overcome this issue soon, at least for text messages). Moreover, a LEO satellite’s trajectory cannot be controlled, and such megaconstellations require advanced equipment for antenna tracking to support handover procedures [4]. Finally, their environmental impact can become considerable as increasingly more satellites are launched [5].
Exploiting large wind turbines (WTs) for providing exurban cellular connectivity can be an effective solution to combine power and communication infrastructures in a single tower that, compared to a conventional cell tower, has a taller and more robust structure. Whenever conveniently applicable (for example, if existing WTs, high wind energy potential, or government subsidies are available), the proposed solution would in turn bring advantages. (Such incentives could consist in tax reduction, land and spectral resources, financial support, etc.) Some possible advantages are cost-effectiveness (since the communications transceivers are incorporated into the power infrastructure), continuity of service (since the WT is often connected to a reliable power grid), and high performance (since the robustness of the WTs easily allows the installation of multiantenna systems, and considerable altitudes lead to wide coverage radii) [6]. Finally, some of the existing WTs are already connected to the core network for transmitting data on temperature, wind speed, and/or humidity to their control centers; therefore, the existing link could also support additional mobile users or sensors. Nonetheless, a considerable percentage of exurban areas is not very suitable for efficient wind energy harvesting.
The use of unused white space is an attractive solution since it can allow the achievement of a coverage radius of tens of kilometers even in the presence of obstructions, such as trees and thin buildings. Moreover, it benefits from both low cost and short deployment time. One of the main challenges is to coordinate the user devices so that they adjust their transmit power based on the channels available at every instant, so that they do not interfere with TV broadcasters. Databases developed by private companies (e.g., Nominet) can help in computing the required power limits. It is also important to note that the presence of many underused TV towers strongly supports the practical implementation of this solution. While the authors in [7] showed via simulations that TVWS can be effectively combined with high-altitude platforms (HAPs) by using 64- and 256-quadrature amplitude modulation schemes, an insightful techno-economic analysis about the feasibility of TVWS can be found in [8].
Both tethered and untethered aerial platforms can be quite effective in covering rural environments. Indeed, high-altitude untethered platforms, such as gliders or airships, can provide coverage over tens or even hundreds of kilometers squared (and therefore are appropriate for serving remote areas), while the advantage of tethered platforms lies in their extremely long endurance and the possibility of providing high-quality communications thanks to the wired backhaul link (thus they are preferred in suburban environments). As far as we are concerned, a good compromise between these two options is the Altaeros SuperTower, whose peculiarity is that it does not need any personnel for deployment. The main advantage of the ST-Flex SuperTower is its rapid deployment, while the ST-300 is able to carry as much as 300 kg of payload. However, there is also a huge interest in untethered drones, which we will deepen from a novel perspective in the next section.
Several works have focused on ABSs for rural connectivity. For example, our stochastic-geometry-based analysis in [1] allowed us to investigate how the coverage probability behaves as the user gets farther from the town center. In addition, [7] presented a novel architecture where HAPs deliver broadband services via the TVWS spectrum. Finally, the authors in [9] focused on all of the aspects related to the endurance of untethered drones when they are operating as ABSs in rural areas. In this article, we instead shed light on the techno-economic aspects of ABSs, their integration with the terrestrial infrastructure, and their potential when it comes to enhancing exurban networks’ coverage and reliability.
On the technical side, the main advantage of the conventional solution is that TBSs have sufficient autonomy and payload capability to host a large number of antennas, which allows them to satisfy high-capacity demands even for a large number of users, whereas ABSs (and especially untethered drones) are much more limited. By contrast, aerial nodes may easily fly at high altitudes, which allows them to take advantage of privileged line-of-sight (LoS) channel conditions over larger areas. Nonetheless, it should be noted that, by increasing the altitude, the aerial interference may become excessive, which requires advanced beamforming techniques (as we will assume later on in our case study) or proper cooperation schemes (e.g., based on trajectory and resource allocation optimizations, as in [10]).
Excluding the overall cost of backhaul (approximately US${\$}$15,000/km in the case of optical fiber [8]), the capital expenditure of a typical rural terrestrial infrastructure is roughly US${\$}$60,000, where equipment, site build, and installation costs respectively account for 56%, 35%, and 9%. On top of this, an administration cost of 20%–30% should be taken into account. In terms of annual operating costs, they account for approximately US${\$}$20,000, fairly distributed among maintenance, site rental, and electricity consumption costs (Section 3.2.3 of [11]). Thus, by tackling the problem of global connectivity with the idea of a conventional cellular infrastructure, the expected overall cost of achieving 4G connectivity would amount to US${\$}$388 billion, of which only a negligible part is associated to high-income countries, while more than 60% and 30% are required by emerging and low-income economies, respectively (Section V of [12]).
Since HAPs and tethered platforms are less popular than (untethered) drones, we hereby focus only on the latter. In any case, given the rapid growth of this market, the costs related to aerial infrastructures are expected to decrease considerably. Despite the scarcity of reliable average costs in the literature, we can roughly estimate the cost of purchasing a drone for communications as US${\$}$7,200, where US${\$}$5,000 is for the drone, US${\$}$700 is for its battery, US${\$}$1,000 is for its power station, and US${\$}$500 is for its flight controller. [A commercial example is the drone model DJI Matrice 300 RTK with battery (TB60), power station (BS60), and flight controller N3. This vehicle can fly for about 50 min and carry up to 3 kg of payload.]
Furthermore, deploying an aerial network implies the need of a charging infrastructure (for tethered platforms this could be seen as part of the aerial network itself, whereas for HAPs this is less important because of the longer autonomy), whereby issues related to the charging time and the consequent security problem of drones at charging stations, the cost of ownership, and installation of the stations should be considered. The main options are 1) conventional charging stations and 2) laser power beaming stations.
Unfortunately, as of today, a detailed economic analysis on charging infrastructures (either conventional charging stations or laser power beaming stations) in rural areas seems missing in the literature. Nonetheless, based on our analysis, we can fairly consider aerial infrastructures to be 5–10 times cheaper than the conventional ones.
Given the economic constraints and the wide range of opportunities, network planning should be done carefully. In this section, some important topological aspects are discussed to facilitate the design of rural and far-flung networks. However, it is evident that effective network planning requires collecting big data (e.g., by means of drones or by referring to similar environments). Also, highlands already hosting TV or WT towers could strongly attract investments since there would be more opportunities to provide ubiquitous coverage.
Estimating the size of the area to cover is evidently fundamental for determining the best solution to deploy and how to scale it. In the case of very small areas, it might not be convenient to cover them, unless by relying on the surrounding TBSs. In the case of very large areas, it might be complicated to deploy drones because it would require many charging stations (otherwise, an excessive part of the drones’ stored energy would be spent in moving from the charging station to the desired location), whereas LEO satellites would definitely be more effective. On the other hand, HAPs or towers, such as Facebook Connectivity’s SuperCell, are valuable platforms to cover medium-large areas. For intermediate sizes (i.e., just a few square kilometers), we believe that drones would generally be the most convenient solution.
Identifying the type of environment hosting the prospective exurban network is also quite important because it allows one to clarify some important aspects, such as ease of installation and maintenance, the presence of power sources, exposure to harsh weather conditions, government regulations, and inhabitants’ reactions. In particular, this last aspect strongly depends on the community’s perception about the effects of a new wireless technology on its members’ privacy and health. ABSs can be the perfect solution for mountainous and hard-to-reach areas, where building any terrestrial infrastructure is too expensive and complicated and part of the satellites’ trajectory may be obstructed by a mountain (although in most cases said obstruction is not as important in compromising the effectiveness of space networks).
The entity of the load has strong implications for the capacity of the network. Generally speaking, it is recommended to deploy the BSs consistently with the users’ density distribution, that is, with more BSs close to the users’ clusters and fewer in sparsely populated zones. Therefore, ABSs can strongly benefit from their mobility and relocation flexibility whenever the load distribution evolves over time, but their capacity is quite limited because of weight constraints.
Nonetheless, beamforming techniques as well as favorable channel conditions can definitely help in serving users even from relatively far BSs. Finally, note that in the same environment there could be multiple types of users with different demand characteristics; for example, rural inhabitants, farmers, and Internet of Things devices should be considered as three different types of users.
Rural environments usually have a basic cellular infrastructure. As previously mentioned, TV towers and WTs can be endowed with cellular functionalities and integrated into the network architecture. However, sometimes the infrastructure in rural areas is already sufficient (also in terms of backhaul links, power grids, or energy sources) but just needs to be restored or improved, for example, by converting 3G BSs into 4G ones. Nonetheless, using satellites always requires building their own ground stations, which in turn implies additional costs.
In this section, we propose insightful simulation results about the joint access–backhaul coverage probability and SINR meta distribution. The coverage probability relates to the chance that a typical user experiences sufficient quality of service (QoS) at a given instant, where the QoS is hereby considered in terms of either SINR or simply signal-to-noise ratio (SNR). On the other hand, the SINR (or SNR) meta distribution quantifies the probability that a given area is covered for at least a specific fraction x (referred to as reliability) of the time.
To evaluate these performance metrics, we assume the system setup illustrated in Figure 2, where we have a comprehensive environment that includes urban, suburban, and rural areas; however, when applying the LoS model proposed in [15], we consider the parameters of a rural environment. The actual TBSs’ density follows a 2D Gaussian distribution centered at the town center and scaled by a factor ${\lambda}_{T}$. On the other side, ABSs are deployed uniformly starting from a distance re (which identifies a circular ABS exclusion zone ${A}_{e}$ centered around the town center).
Figure 2 The considered system setup: the typical user is located at distance ru from the origin and associates to the BS that provides the maximum average received power. All of the ABSs are placed outside a circular exclusion zone ${A}_{e}$.
Note that the results are obtained by assuming that the terrestrial antennas devoted to backhaul transmit with very narrow beams. In this way, we can neglect the backhaul interference experienced by any ABS. Anyway, this is already a more general case compared to the one we assumed in [1], where backhaul links were assumed ideal.
The main simulation parameters are summarized in Table 1, which suggests that non-LoS drones will have a negligible influence on the user experience because, apart from their longer average distance to the user, they are characterized by a higher path loss exponent and smaller mean loss coefficient.
Table 1 Main simulation parameters.
As we can see from Figure 3, the behavior of the joint coverage probability Pc as a function of the ABSs’ density ${\lambda}_{A}$ strongly depends on the distance between the user and the town center (ru). In particular, it becomes increasingly more constant as ru decreases since urban users evidently have low chances of being in a LoS condition with any UAVs. On the other hand, as we move our focus away from the city center, we start noticing the need for a higher concentration of ABSs because of the scarce availability of TBSs.
Figure 3 Coverage probability as a function of the ABSs’ density for various values of ru.
Surprisingly, deploying just 0.04 ABSs/km2 would be enough for an exurban user equipment (for example, located at distance ru = 12–18 km from the town center) to achieve better coverage than its urban counterpart. This happens because the presence of mostly aerial nodes limits the aggregate interference to only its aerial component (nonetheless, it is evident that the aerial interference would become excessive if ${\lambda}_{A}$ were strongly increased above the considered range).
Moreover, we can note that the change of behavior with respect to ru is not linear at all; for example, the QoS experienced close to the edge of the exclusion zone is much more constant when going toward the urban area rather than the rural one (in other words, the red curve looks much more similar to the blue one than the yellow one).
Finally, note that in our case the backhaul link never represents the bottleneck of the system, but this would not be true (at least for ${r}_{u}\approx{r}_{e}$) if the assumption of sharp backhaul beamforming (which implies negligible interferences) were disregarded.
Figure 4 shows interesting results about the SINR meta distribution at the town center and at 18 km of distance from it. Consistently as with Figure 3, we can clearly notice from the solid lines that increasing the ABSs’ density strongly benefits rural users. However, it also causes a slight decrease of the QoS in the town center.
Figure 4 SINR meta distribution for typical urban and rural users, for various values of ${\lambda}_{A}$.
Moreover, by comparing the red curves (that is, ${\lambda}_{A} = {0}{.}{04}$ ABSs/km2), we can state that the town center is characterized by more fairness, meaning that a higher number of links would be able to achieve at least a minimal reliability of just 40%. This can be explained by noting that urban areas generally provide a shorter distance to the closest TBS; nonetheless, the strong aggregate interference almost always prevents us from achieving high reliability.
An interesting open problem regards the techno-economic analysis when considering conventional cell towers and optical fiber deployment. In particular, it would be interesting to make a fair comparison among various potential technologies for a given economic budget: by considering the technologies and costs mentioned in the section “Brief Survey on Rural Connectivity,” the costs related to the deployment and operation of ABSs and their charging infrastructure should be compared with the costs of conventional network architectures providing an equivalent QoS.
As cellular networks become increasingly more heterogeneous over time, we believe that rural connectivity planning should also take into account the presence of existing or prospective WTs within the area of interest. Therefore, another open problem would be to extend the study proposed in the section “Network Performances” by adding a tier of WTs to the TBS and ABS tiers.
Furthermore, the performance of the network should be optimized by taking into account all of the aforementioned techno-economic constraints. For example, it might be convenient to have just one WTBS per wind farm (see Figure 5) to maintain an acceptable level of interference while improving coverage and capacity. For each wind farm, the BS equipment should be mounted on the optimal WT based on topological aspects, such as the distance to the neighbor nodes and/or the user distribution.
Figure 5 The system setup for the proposed open problem. The difference from the one in Figure 2 consists in the presence of some WTs distributed as a Poisson cluster process, where the cluster represents a wind farm. For each wind farm, at most one WT is optimally selected to be mounted with the BS equipment.
Moreover, given the scarcity of TBSs in rural areas, the presence of strategically deployed WTBSs would make it easier to improve the backhaul capability of the network. In other words, WTBSs could allow network operators to serve more users and deploy more ABSs if needed.
In this work, we provided an overview of rural connectivity in the context of future cellular networks, with a special focus on UAV-assisted architectures. Moreover, we discussed techno-economic aspects of both conventional and aerial network infrastructures, showing that aerial networks can often be a very cost-efficient solution. We then provided fruitful simulation results to help telecom operators in designing future cellular network architectures and proved that even an extremely low density of ABSs is sufficient to bridge the digital divide in terms of coverage probability and SINR meta distribution. Finally, we envisioned realistic extensions of this work involving both the economic and technical aspects.
Based on our study, we can conclude by promoting the deployment of ABSs (even with low density) in rural areas, at least from a coverage and reliability perspective. However, further research and development is required to make this solution effective also in the case of high traffic demands.
Maurilio Matracia (maurilio.matracia@kaust.edu.sa) is currently a doctoral student in the Communication Theory Lab at King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. His main research interest is stochastic geometry, with a special focus on postdisaster and rural cellular networks. He is a Student Member of IEEE.
Mustafa A. Kishk (mustafa.kishk@gmail.com) is currently an assistant professor with the Electronic Engineering Department, National University of Ireland Maynooth, W23 F2H6 Maynooth, Ireland. He received his Ph.D. degree in electrical engineering from Virginia Polytechnic Institute and State University, USA, in 2018. His research interests include stochastic geometry, energy harvesting wireless networks, unmanned aerial vehicle-enabled communication systems, and satellite communications. He is a Member of IEEE.
Mohamed-Slim Alouini (slim.alouini@kaust.edu.sa) is currently a distinguished professor of electrical and computer engineering at King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. He was born in Tunis, Tunisia. He received his Ph.D. degree in electrical engineering from the California Institute of Technology, Pasadena, CA, USA, in 1998. His current research interests include the modeling, design, and performance analysis of wireless communication systems. He is a Fellow of IEEE.
[1] M. Matracia, M. A. Kishk, and M.-S. Alouini, “Coverage analysis for UAV-assisted cellular networks in rural areas,” IEEE Open J. Veh. Technol., vol. 2, pp. 194–206, Apr. 2021, doi: 10.1109/OJVT.2021.3076814.
[2] C. Zhang, S. Dang, B. Shihada, and M.-S. Alouini, “On telecommunication service imbalance and infrastructure resource deployment,” IEEE Wireless Commun. Lett., vol. 10, no. 10, pp. 2125–2129, Oct. 2021, doi: 10.1109/LWC.2021.3094866.
[3] E. Yaacoub and M.-S. Alouini, “A key 6G challenge and opportunity—Connecting the base of the pyramid: A survey on rural connectivity,” Proc. IEEE, vol. 108, no. 4, pp. 533–582, Apr. 2020, doi: 10.1109/JPROC.2020.2976703.
[4] G. Giambene, S. Kota, and P. Pillai, “Satellite-5G integration: A network perspective,” IEEE Netw., vol. 32, no. 5, pp. 25–31, Sep./Oct. 2018, doi: 10.1109/MNET.2018.1800037.
[5] B. Osoro and E. Oughton. “Universal broadband assessment of low earth orbit satellite constellations: Evaluating capacity, coverage, cost, and environmental emissions.” SSRN. [Online] . Available: https://ssrn.com/abstract=4178732
[6] M. Matracia, M. A. Kishk, and M.-S. Alouini, “Exploiting wind-turbine-mounted base stations to enhance rural connectivity,” IEEE Commun. Mag., vol. 59, no. 12, pp. 50–56, Dec. 2021, doi: 10.1109/MCOM.001.2100468.
[7] K. Katzis, L. Mfupe, and H. M. Hussien, “Opportunities and challenges of bridging the digital divide using 5G enabled high altitude platforms and TVWS spectrum,” in Proc. 8th Int. Conf. Commun. Netw. (ComNet), Toronto, Canada: IEEE, 2020, pp. 1–7, doi: 10.1109/ComNet47917.2020.9306090.
[8] M. Khalil, J. Qadir, O. Onireti, M. A. Imran, and S. Younis, “Feasibility, architecture and cost considerations of using TVWS for rural Internet access in 5G,” in Proc. 20th Conf. Innov. Clouds, Internet Netw. (ICIN), Paris, France: IEEE, 2017, pp. 23–30, doi: 10.1109/ICIN.2017.7899245.
[9] Y. Qin, M. A. Kishk, and M.-S. Alouini, “Drone charging stations deployment in rural areas for better wireless coverage: Challenges and solutions,” IEEE Internet Things Mag., vol. 5, no. 1, pp. 148–153, Mar. 2022, doi: 10.1109/IOTM.001.2100083.
[10] L. Xie, J. Xu, and Y. Zeng, “Common throughput maximization for UAV-enabled interference channel with wireless powered communications,” IEEE Trans. Commun., vol. 68, no. 5, pp. 3197–3212, May 2020, doi: 10.1109/TCOMM.2020.2971488.
[11] E. J. Oughton, N. Comini, V. Foster, and J. W. Hall, “Policy choices can help keep 4G and 5G universal broadband affordable,” Technol. Forecasting Soc. Change, vol. 176, Mar. 2022, Art. no. 121409, doi: 10.1016/j.techfore.2021.121409.
[12] E. J. Oughton. “Delivering affordable universal broadband: Exploring the global investment needs of broadband infrastructure to achieve target 9c of the sustainable development goals.” SSRN. [Online] . Available: https://ssrn.com/abstract=4178725
[13] A. Robotics. Introducing DroneHome Drone Battery Swap Station (2016). (Oct. 25, 2016). Accessed: Jul. 28, 2022. [Online Video] . Available: https://www.youtube.com/watch?v=qci1t4cOqvI
[14] M.-A. Lahmeri, M. A. Kishk, and M.-S. Alouini, “Charging techniques for UAV-assisted data collection: Is laser power beaming the answer?” IEEE Commun. Mag., vol. 60, no. 5, pp. 50–56, May 2022, doi: 10.1109/MCOM.001.2100871.
[15] A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP altitude for maximum coverage,” IEEE Wireless Commun. Lett., vol. 3, no. 6, pp. 569–572, Dec. 2014, doi: 10.1109/LWC.2014.2342736.
Digital Object Identifier 10.1109/MVT.2023.3301228