Intelligent deployment of UAVs in 5G heterogeneous communication environment for improved coverage
Introduction
Unmanned aerial vehicles (UAVs) have made a mark in the area of networking with provisioning of continuous support to the network devices. This connectivity has improved the data rate which is the primary requirement of the 5G networks. With solutions to the CAPEX/OPEX issues, UAVs allow a vast range of applications in the heterogeneous networks. The next-generation heterogeneous networks aim at providing high data rate with improved coverage and capacity by deploying network facility in all the components. The use of multiple devices is the key aspect of the heterogeneous networking. These networks can be used to resolve the problems related to high stream data transfers (Shuai et al., 2011, Cheng et al., 2007). These networks aim at increasing the data rate, serving user demands for 100% availability and lesser delays in transmissions (Bor-Yaliniz and Yanikomeroglu, 2016). These networks although gel well with the fronthaul and the backhaul of the existing network formations, yet these are not capable of providing the full connectivity and coverage to all the users in the particular area (Li et al., 2014).
A traditional heterogeneous network comprises of the macro base station (MBS), small cells, femtocells and picocells for connectivity users (Bennis et al., 2013, Hossain et al., 2014). According to the architecture suggested under METIS, METIS2, and 5GPP, small cells, Radio Access Networks (RANs), Cloud-RANs form the crucial part of the 5G deployment (Osseiran et al., 2014). These components are the backbone of the high-speed transmission in these 5G networks. However, these small cells, femtocells, and picocells are to be deployed in larger number to serve the maximum users with high data rates. This deploying of more devices and access points can improve the capacity of the networks, but this also increases the network complexity and the cost to large extent (Curiac, 2016). It becomes relatively tough for the service providers to find the appropriate sites for the deployment of these devices and requires rigorous network planning which makes it complex to use them. An alternative approach is required which can not only improve the coordination but should be intelligent enough to take the decisions regarding the traffic regulations. Thus, UAVs can be a considered as an intelligent as well as reliable solutions to this problem of the next generation heterogeneous networks. UAVs can act as a pivot in the existing cellular infrastructure and can relay information between the stations and the cells efficiently; and can also provide direct support for connectivity between the user equipments (UEs), as shown in Fig. 1.
UAVs have already seen a lot of development and utility in the networks by acting as a centralized or autonomous device for prolonged connectivity between the users. However, the existing solutions aim only at the deployment of the UAVs as an alternative network node which can relay data and can handle extra users (Saleem et al., 2015). Positioning, mapping of UAVs to a particular area, load transfer delusions in the case of failures, cooperation and decision of deployment are still major issues which are to be resolved to provide improved coordination for increasing the capacity and the coverage of the existing cellular networks (Yaliniz et al., 2016, Galkin et al., 2016). Further, there exist some other approaches which use UAVs as ad hoc component and provide temporary connections between the other network nodes, but this does not stand with the continuous increasing demand of the users as well as does not provide a stable and a reliable solution for continuous support of high data rate demands. Handling more number of the users is one of the applications of these aerial vehicles. Deployment, mapping, and the requirement of UAVs to intelligently understand the network demand are not provided by the existing solutions. Thus, efficient approaches and models are required which can overcome these issues and can provide a stable and reliable ideology for capacity enhancement of the 5G heterogeneous networks.
In this paper, the coverage and capacity enhancement of the 5G heterogeneous networks are considered as the formal problem which is resolved using the UAVs. The proposed approach is initially derived for two different solutions. One of them focus on the formation of this as a decision problem for the MBS which has to decide where to place the UAVs, and the second focuses on the cooperative network formation which performs network bargaining between the UAVs to handle a particular demand area as well as for load balancing. The solutions to these problems are provided using the priority-wise dominance and the entropy approaches. The results obtained for the proposed approach shows significant improvement in terms of the network throughput, 5th percentile spectral efficiency, network coverage, signal to noise plus interference ratio, network delays, accuracy in the mapping of the demand areas with the UAVs, and delivery ratio. The proposed approach provides more stabilized network formations, a low-complex solution which provides a significant improvement in the gains for the above-mentioned parameters.
The use of UAVs undoubtedly helps the modern day networking. But, there exist some limitations to these aerial vehicles such as the maximum load of UAV, length, and weight of the antenna, fuel/power consumption of UAV. All these issues are to be resolved for the efficient utilization of UAVs in the upcoming 5G networks (Mozaffari et al., 2015, Mozaffari et al.,). The load has been taken care of in this paper, but other aspects related to the physical properties such as the speed, length and payload are not evaluated and it is assumed that the UAVs are capable of supporting the network components. Although the results may vary when these issues are considered, yet the proposed approach provides an efficient ideology for the use of UAVs in the 5G networks. Apart from these, mobile handovers are assumed to be happening using the existing technologies. However, since UAVs are highly dynamic and high-speed mobile stations, these require efficient handover mechanisms for smooth transfer of services (Sharma et al., 2016a). In this paper, these handovers are operated using the existing media independent mechanisms. The actual analysis of the handovers for UAV-assisted networks will be presented in the future reports.
Rest of the paper is structured as follows: Section 2 provides insight of the existing literature for the use of UAVs in the next generation networks, Section 3 provides network model over which the proposed approach is formulated. Section 4 gives the details of the proposed approach with a complete overview of the problem. Section 5 evaluated the proposed approach and compares it with the existing state-of-art solutions. Finally, Section 6 concludes the paper with possibilities of future research.
Section snippets
Related work
The UAV oriented networking has evolved over the last decade. However, use of these aerial vehicles in the heterogeneous networks has opened new paradigms in the area of networking. With the upcoming 5GPP, UAVs will play an important role as a continuous network support device which can not only increase the capacity but will also improve the coverage of the existing networks. Coverage and capacity can also be improved without the use of the UAVs. But this will increase the operational cost of
Network model
The network comprises a set N of UAVs operating in either single or multiple layers such that each UAV has a radio range R. Each UAV is capable of handling a set K of users making continuous requests from a particular demand area. The number of requests Sr comes with an arrival rate of λ and mean packet size of each service request is . Considering the omnidirectional antenna, the latitude of the UAVs is fixed at h. This altitude value is kept at an optimal value to allow better connectivity
Proposed approach
The proposed approach aims at increasing the capacity and the coverage of the next-generation heterogeneous wireless networks by using UAVs as the coordinating node between the devices. The proposed approach aims at enhancing the mapping of the UAVs to a particular demand area with minimum delay and increased coordination. The network model presented in the previous section forms the basis of the UAV deployment and is further evaluated over two different problem sets. The problem targeted in
Performance evaluation
The proposed approach aims at improving the coordination as well as the capacity and coverage of the heterogeneous wireless networks by providing an efficient approach for UAV to area mapping. The proposed approach uses entropy based decision policies to locate the UAVs in the area of a macro base station to support the extra users over the network operational time. The proposed approach is analyzed using the numerical simulations over an area of 10000×10000 sq.m. maneuvered by a maximum of 12
Conclusion
Coverage and capacity enhancement of the next-generation heterogeneous wireless networks are the biggest issues as more and more devices are making requests at the same time. Unmanned aerial vehicles (UAVs) can provide a pivotal support to regulate the data in these networks to improve the coordination as well as the coverage. UAVs can be used to relay information as well as can be used as an aerial base station to facilitate the users for data sharing with the macro base station. In the worst
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2022, Physical CommunicationCitation Excerpt :A summary to the discussed optimization problems is presented in Tables 3 and 4 at the end of the section. In the same way of objective categorization as that was in Section 2, different objectives of different papers in the literature include maximizing the number of the covered users [75–80], or users’ coverage probability [81], maximizing the minimum individual users’ rate [82,83] or system per-user capacity and coverage [84]. Other papers aimed to improve the whole network sum-rates [75,85–98], spectral efficiency [99], the minimum SIR [100], or the total receive power for users [101].