UAV–Cellular Spectrum Sharing

The use of drone small cells (DSCs) which are aerial wireless base stations that can be mounted on flying devices such as unmanned aerial vehicles (UAVs), is emerging as an effective technique for providing wireless services to ground users in a variety of scenarios. The efficient deployment of such DSCs while optimizing the covered area is one of the key design challenges. In this paper, considering the low altitude platform (LAP), the downlink coverage performance of DSCs is investigated. The optimal DSC altitude which leads to a maximum ground coverage and minimum required transmit power for a single DSC is derived. Furthermore, the problem of providing a maximum coverage for a certain geographical area using two DSCs is investigated in two scenarios; interference free and full interference between DSCs. The impact of the distance between DSCs on the coverage area is studied and the optimal distance between DSCs resulting in maximum coverage is derived. Numerical results verify our analytical results on the existence of optimal DSCs altitude/separation distance and provide insights on the optimal deployment of DSCs to supplement wireless network coverage.

In this paper, we develop a comprehensive statistical framework to characterize and model large-scale unmanned aerial vehicle-enabled post-disaster recovery cellular networks. In the case of natural or man-made disasters, the cellular network is vulnerable to destruction resulting in coverage voids or coverage holes. Drone-based small cellular networks (DSCNs) can be rapidly deployed to fill such coverage voids. Due to capacity and back-hauling limitations on drone small cells (DSCs), each coverage hole requires a multitude of DSCs to meet the shortfall coverage at a desired quality-of-service. Moreover, ground users also tend to cluster in hot-spots in a post-disaster scenario. Motivated by this fact, we consider the clustered deployment of DSCs around the site of a destroyed BS. Joint consideration partially operating BSs and deployed DSCs yields a unique topology for such public safety networks. Borrowing tools from stochastic geometry, we develop a statistical framework to quantify the down-link performance of a DSCN. Our proposed clustering mechanism extends the traditional Matern and Thomas cluster processes to a more general case, where cluster size is dependent upon the size of the coverage hole. We then employ the newly developed framework to find closed-form expressions (later verified by Monte-Carlo simulations) to quantify the coverage probability, area spectral efficiency, and the energy efficiency for the down-link mobile user. Finally, we explore several design parameters (for both of the adopted cluster processes) that address optimal deployment of the network (i.e., number of drones per cluster, drone altitudes, and transmit power ratio between the traditional surviving base stations and the drone base stations).

Drone small cells (DSCs) can provide on-demand air-to-ground wireless communications in various unexpected situations, such as traffic jam or natural disasters. However, a DSC needs to face the challenges such as severe co-channel interference, limited battery capacity, and fast topology changes. Aiming to improve energy efficiency of DSCs and quality of services of customers, this paper presents a learning-based multiple drone management (LDM) framework by controlling the transmission power and the 3-dimension location of DSCs based on location data, and reference signal received power of users. Since the labeled throughput data are typically not available in emergency situations, we develop unsupervised learning DSC management techniques: 1) affinity propagation interference management scheme to mitigate interference and energy consumption, and 2) K-means position adjustment to adjust the new 3-dimension positions of drones. Our numerical results show that the proposed LDM framework combining with affinity propagation clustering and k-means clustering can enhance the energy efficiency of DSCs by 25% and the signal-to-interference-plus-noise ratio of ground users by 56%, respectively.

Radio resource slicing is critical to customize service provisioning in fifth-generation (5G) uplink radio access networks (RANs). Using drone-small-cells (DSCs) as aerial support for terrestrial base stations can enhance the flexibility for resource provisioning in response to traffic distribution variations. In this paper, we study a multi-DSC-assisted radio resource slicing problem for 5G uplink RANs, with the objective of minimizing the total uplink resource consumption under differentiated quality-of-service (QoS) constraints for both human-type and machine-type communication services. We begin with an interference-aware graph model to formulate the joint DSC three-dimension (3D) placement and device-DSC association problem for uplink radio resource slicing and prove that the proposed problem is NP-hard. A complexity-adjustable problem approximation is presented via screening candidate DSC deployment positions, which incorporates flight height adaptation to balance the uplink communication coverage and resource utilization. A lightweight approximation using a fixed DSC flight altitude is also provided with reduced complexity. For mathematical traceability, the DSC placement and device-DSC associations in each approximation are transformed as a special weight clique problem. An upgraded clique algorithm is then developed to determine how to deploy DSCs for a given number of DSCs. Simulation results demonstrate the proposed scheme's effectiveness in terms of resource utilization, network coverage, and drone dispatching cost.

Unmanned aerial vehicles (UAVs) as an Aerial Base Station (ABS) are the enabler in the provisioning of emergency communication services. However, ABS unplanned deployment creates interference from the neighboring co-channel base station, which hinders meeting the required quality-of-service (QoS) requirements and the minimum rate of users. Hence, the ABS deployment and its power allocation require a machine learning-based solution xthat can plan in real-time to enhance the users’ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max-min sum-rate</i> . We propose the reinforcement learning-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> greedy algorithm to solve the max-min optimization problem. The simulation results validate the proposal by achieving around 2.3 bps/Hz high <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimum sum-rate</i> compared to the conventional water filling algorithm at the same ABS altitude.

Non-orthogonal multiple access (NOMA) is a promising radio access technique that enables massive connectivity and increased spectral efficiency. The deployment of aerial base stations (ABSs) as a relay is also an optimistic goal that fairly serves a large number of internet of things (IoT) devices. On one side, ABS-assisted communication leverages effective communication services for secondary IoT devices in smart cities. On the other hand, NOMA allows several IoT devices to concurrently acquire the same frequency-time resource. To this end, weighted sum-rate (WSR) is an essential goal because it allows numerous trade-offs between user fairness and sum-rate efficiency. Therefore, this work aims to investigate the WSR for an integrated aerial terrestrial network subject to cellular power and delay constraints in downlink NOMA. Herein, a theoretical insight-based low-complexity iterative solution is provided for optimal power and blocklength allocation to achieve maximum sum-rate. For this purpose, the mixed-integer non-linear problem is formulated and a low-complexity near-optimal solution is proposed. Numerical results show that the proposed scheme achieves a near-optimal solution and outperforms baseline techniques, i.e., the performance gain of 5.18% over the legacy OMA system for NOMA with two IoT devices per subcarrier.

A major issue in deploying aerial base stations is the strain it places on the already scarce spectrum and the associated required re-design of the radio resource allocations. To solve this problem, we propose a Licensed Shared Access- (LSA-) based aerial-terrestrial system. The proposed scheme is an integration of unmanned aerial vehicle base station (UAV-BS), as a licensee system, utilising the radio spectrum allocated to other wireless systems- the incumbent system, with the ground base-station operating at the traditional frequency band for cellular systems. However, a major challenge in using LSA for D-BSs is the spectrum unavailability while the incumbent system is active. In conventional terrestrial networks, this leads to service unavailability in a wide geographical area. To tackle this, we define a dynamic UAV-BS positioning scheme that takes into account the presence or otherwise of the incumbent on the spectrum (incumbent Busy and, Idle mode) while taking into consideration the spatial capacity demand in the licensee network. In the busy incumbent mode, the problem of the UAV-BS positioning is tantamount to a goal programming problem with the first level priority goal of fulfilling the incumbent’s interference threshold constraint. Simulation result show that in addition to, the proposed (LSA-) based aerial-terrestrial scheme, solving the challenge posed by scarcity of spectrum in aerial base station deployment, a significant improvement in the system efficiency is obtained. It is seen that the proposed dynamic UAV-BS positioning scheme achieved the desired capacity requirement of the licensee network, while ensuring the interference threshold is not exceeded, at a relatively smaller distance of 1000 m to the boundary of the incumbent’s system transmission coverage as opposed to a larger distance of about 19 Km.

With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre-install any infrastructure. Since the base station is attached to the drone, technically it is feasible for the base station to dynamic reposition itself in response to the changing locations of users for reducing the communication distance, decreasing the probability of signal blocking, and ultimately increasing the spectral efficiency. In this paper, we first propose distributed algorithms for autonomous control of drone movements, and then model and analyse the spectral efficiency performance of a drone small cell to shed new light on the fundamental benefits of dynamic repositioning. We show that, with dynamic repositioning, the spectral efficiency of drone small cells can be increased by nearly 100% for realistic drone speed, height, and user traffic model and without incurring any major increase in drone energy consumption.

Aerial base station (AeBS) is a promising technology for providing wireless coverage to ground user equipment. Traditional methods of optimizing AeBS networks often rely on pre-known distribution models of ground user equipment. However, in practical scenarios such as natural disasters or temporary large-scale public events, the distribution of user clusters is often unknown, posing challenges for the deployment and application of AeBS. To adapt to complex and unknown user environments, this paper studies a method of estimating information from local to global and proposes a multi-agent AeBSs autonomous deployment algorithm based on deep reinforcement learning (DRL). This method attempts to dynamically deploy AeBS to autonomously identify hotspots by sensing user equipment signals without network-side assistance, providing a more comprehensive and intelligent solution for AeBS deployment. Simulation results indicate that our method effectively guides the autonomous deployment of AeBS in emergency scenarios, addressing the challenge of the lack of network-side assistance.

Next generation mobile networks will rely ever more heavily on resource sharing.In this article we study the sharing of radio access network and spectrum among mobile operators.We assess the impact of sharing these two types of resources on the performance of spatially distributed mobile networks.We apply stochastic geometry to observe the combined effect of, for example, the level of spatial clustering among the deployed base stations, the shared network size, or the coordination in shared spectrum use on network coverage and expected user data rate.We uncover some complex effects of mobile network resource sharing, which involve non-linearly scaling gains and performance trade-offs related to the sharing scenario or the spatial clustering level.

Nowadays, the cellular network planning becomes a complex task due to unpredictable changes in mobile user behavior and high capacity requirements. Therefore, it is needed to deploy a high number of base stations in order to provide a requested quality of service. The advancement in technologies gives us an opportunity to use aerial base stations, which can change their positions in order to provide capacity only in space and time where and when it is needed. In this work, we present a novel self-organized approach based on gradient search and potential fields for aerial base station deployment in order to increase the cellular network capacity. In contrast to the current aerial base station deployment approaches, our solution satisfies all requirements of modern urban scenario -- it uses only local information, adapts to different environments, provides high degree of autonomy and scalability and operates in real time.

Millimeter wave (mmWave) is promising for the fifth generation cellular systems. The sensitivity of mmWave signals to blockages, such as buildings in cities, however, makes the network performance hard to predict. Using concepts from stochastic geometry and random shape theory, this paper proposes an analytical framework to incorporate the blockage effects and evaluate the performance of mmWave cellular networks, in terms of coverage probability and achievable rate. Leveraging prior work on a blockage model, a stochastic characterization of the regions covered by line-of-sight (LOS) and non-LOS links is obtained, which allows different path loss laws to be applied to the LOS and non-LOS links, respectively. Based on the proposed framework, analytical expressions for the mmWave downlink coverage probability are derived, and then the network performance is examined. Numerical results show that millimeter wave (mmWave) networks can provide comparable coverage probability and much higher data rates than microwave networks.

Aerial base station (ABS) provides a flexible solution for hotspot scenarios in traditional cellular networks, where macro-cell base stations (MBSs) are challenged by overwhelming short-time traffic demands. However, due to stretched transmission distances from sky, the system performance of this ABS-scheme is sometimes questioned. In this paper, we study the system performance of ABS-assisted networks by tools from stochastic geometry. The two-tier network consisting of MBSs and ABSs is modeled as the superposition of a Poisson point process over the infinite plane and a binomial point process within an overlapped finite circular area. We consider a more general probabilistic line-of-sight and non-line-of-sight propagation model and derive coverage probability as well as area spectral efficiency. Based on the proposed analytical framework, we study the impacts of various parameters and compare the ABS-scheme with a benchmark scheme, in which network densification is realized through deploying additional ground base stations (GBSs). Simulation results unveil that: 1) the height and ABS number should be carefully designed to obtain the optimal performance and 2) when the number of assisting BSs is limited, the ABS-scheme can achieve even better performance than the GBS-scheme, which validates the feasibility of enhancing system performance through ABSs in hotspot scenarios.

Recently, the International Telecommunication Union allocated the 470?862 MHz band to the digital broadcasting (DB) service. Moreover, the 790?862 MHz sub-band will be allocated to the next-generation mobile system, known as the International Mobile Telecommunication ? Advanced (IMT-A), and to the DB on a co-primary basis in the year 2015. Currently, two candidate technologies are available to represent the IMT-A system; the Mobile WiMAX and Long Term Evolution ? Advanced (LTE-A). One of the main criteria of the IMT-A candidate is to not cause additional interference to the primary service (i.e., DB). In this paper, we address the spectrum sharing issue between the IMT-A candidates and the DB service. More precisely, we investigate the interference effect between the DB service and the mobile network, which could be either LTE-A or WiMAX. Our study proposes a spectrum sharing model to take into account the impact of interference and evaluates the spectrum sharing requirements such as frequency separation and separation distance. This model considers three spectrum sharing scenarios: co-channel, zero guard band, and adjacent channel. A statistical analysis is performed, by considering the interferer spectrum emission mask and victim receiver blocking techniques. The interference-to-noise ratio is used as an essential spectrum sharing criterion between the systems. The model considers the random distribution of the users, antenna heights, and the bandwidth effect as well as the deployment environment in order to achieve spectrum sharing. The results show that LTE-A is preferable to WiMAX in terms of having less interference impact on DB; this can eventually allow the operation of both services without performance degradation and thus will lead to efficient utilization of the radio spectrum.

Mobile cellular communication has already entered the mass market, and mobile Internet services will soon become a reality. The frequent use of mobile radio technologies wherever people are has a direct impact on the deployment of base stations or radio access points, including antennas. Put simply, to serve an increasing number of users requires an increasing number of base stations. Thus, operators must carefully plan the deployment and configurations of radio base stations in order to support voice and data traffic at a level of quality expected by customers. Planning tools are used to help radio engineers in their difficult tasks of balancing requirements or radio coverage and quality with economic and other practical aspects. These planning tools make extensive use of terrain databases and of visualisation tools that lead some tool vendors to offer functionalities very similar to a geographical information system (GIS) or even to base their product on a GIS. Futhermore, because radio communication between base stations and users is crucial, all computations in a planning tool are based on the use of radio-propagation predictions. Until recently, empirical propagation prediction seemed sufficient. However, more efficient planning and the planning of nonvoice services or of a mixture of voice and nonvoice services require more accurate propagation-prediction models. These propagation models are usually based on the computation of the physical interaction of ratio waves and the environment. Thus more detailed databases are required, especially in urban environments where most users are located. Although the authors are not GIS specialists, they aim to expose some of the relationship between radio-propagation models used for mobile radio network planning and databases for terrain and buildings. For example, an accuracy of about 1 m on the coordinates of building corners is found to be acceptable in terms of accuracy in radio coverage. As another example, simulation results show that the use of conventional propagation models and rough geographical databases for the planning of future cellular systems like UMTS might cause serious difficulties.