Aerial Access Points Research Articles

Joint resource scheduling and trajectory design for multi-UAV-assisted uplink NOMA networks

The combination of unmanned aerial vehicle (UAV) communication and non-orthogonal multiple access (NOMA) is of great significance to support massive connections in the Internet of things (IoT). This paper studies a multi-UAV-assisted communication network, where the UAVs are employed as aerial access points to receive data from IoT nodes with NOMA adopted for uplink transmission. The aim is to maximize the average sum rate of the network, with the requirements of the individual upload of each node. To handle the node clustering problem, the spectral clustering and maximum weight matching algorithm are first integrated to divide the IoT nodes. Then, the time-varying NOMA user grouping scheme is implemented accordingly. Furthermore, by considering the trajectory decomposition and successive hover-and-fly structure, the complex UAV trajectory problem is transformed into a linear programming and the optimal time-continuous trajectory is achieved. Extensive simulations validate the effectiveness of the proposed algorithms for the UAV-assisted NOMA network.

Computation Offloading and Trajectory Control for UAV-Assisted Edge Computing Using Deep Reinforcement Learning

Task offloading has attracted widespread attention in accelerating applications and reducing energy consumption. However, in areas with surging traffic (nucleic acid testing, concerts, etc.), the limited resources of fixed-base stations cannot meet user requirements. Unmanned aerial vehicles (UAVs) can effectively serve as temporary-base stations or aerial access points for mobile devices (MDs). In the UAV-assisted MEC system, we intend to jointly optimize the trajectory and user association to maximize computational efficiency. This problem is a non-convex fractional problem; therefore, it is not feasible to use only a traditional method, such as Dinkelbach’s method, for solving a fractional problem. Therefore, to facilitate online decision making for this joint optimization problem, we introduce deep reinforcement learning (DRL) and propose a double-layer cycle algorithm for maximizing computation efficiency (DCMCE). Specifically, in the outer loop, we model the trajectory planning problem as a Markov decision process, and use deep reinforcement learning to output the best trajectory. In the inner loop, we use Dinkelbach’s method to simplify the fraction problem, and propose a priority function to optimize user association to maximize computational efficiency. Simulation results show that DCMCE achieves higher computational efficiency than the baseline scheme.

Cost-Efficient Design of an Energy-Neutral UAV-Based Mobile Network

This work proposes a framework to design a cost-efficient unmanned aerial vehicle (UAV)-based energy-neutral (EN) system deployed to harvest data from a set of internet-of-things (IoT) nodes. The energy-neutrality refers to the zero-sum balance between energy harvested, stored, and consumed during operation, which is a game-changer when a connection to the electricity grid is not available/feasible. This involves employing an off-grid charging station (CS) comprising of photovoltaic (PV) panels and batteries that provide enough energy to recharge the UAV-based aerial access points (AAPs). The investment cost is determined by the number of AAPs, PV panels, and ground battery units. Its minimization cannot be achieved using conventional optimization tools due to the non-tractable form of the CS load. Therefore, a novel wave-based method is proposed to represent the load profile as a proportional function of the required number of AAPs, so as to directly relate the CS design to the trajectory optimization. Compared to baseline scenarios, the proposed trajectory design can halve the time and energy consumption; the investment cost varies with the time and season of service; the off-grid CS is particularly advantageous in rural areas, while in urban areas its cost is comparable to that of a grid-connected alternative.

Enabling efficient caching in high mobility UAV communications network under limited backhaul

Due to flexible deployment, unmanned aerial vehicle (UAV) mounted aerial access points are capable of expanding the coverage capabilities of existing terrestrial base stations (TBSs). Different from TBSs, however, UAV access points (UAPs) are of high mobility in horizontal and vertical dimensions, which may deteriorate the coverage performance. Worsestill, the mobility of UAPs would as well increase the pressure of wireless backhaul. In this light, we investigate the performance of the cache-enabled UAV communications network (CUCN) in terms of network spatial throughput (ST) by analyzing the line of sight (LoS) connections and non-line of sight (NLoS) connections. It is found that the network ST is exponentially decreased with the square of UAP altitude. Furthermore, contrary to intuition, a large cache size may deteriorate the network ST when UAPs are over-deployed. The reason is that a large cache size increases the hit probability, which may increase the activation of UAPs and consequently result in complicated interference. Aiming to maximize the network ST, we optimize the cache strategy under limited backhaul. Remarkably, the results show that network ST could be substantially improved by the optimized cache strategy and the performance degeneration brought by UAP high mobility could be even eliminated especially when the UAP altitude is high.

Energy-Saving Deployment Optimization and Resource Management for UAV-Assisted Wireless Sensor Networks With NOMA

Energy-saving techniques are vital for the battery-powered sensor devices (SDs), which affect their lifetime. In this paper, we propose an air-and-ground cooperative wireless sensor network (AGWSN), wherein several UAVs are deployed as aerial access points (AAPs) to assist the terrestrial access point (TAP) for data collecting. The positions of the AAPs can be modified to approach the cell-edge SDs, therefore reducing the energy of the SDs expended in uploading data. To fully exploit the potential of the AGWSN, we formulate a joint AAP position optimization, channel allocation, and power control problem to minimize the total power consumption of all SDs subject to their decoding threshold. To solve the formulated problem, we first analyze the optimal user pairing rule in each cell and based on the rule propose a maximum-weighted-independent-set inspired algorithm for the AAP position optimization. Then, we remodel the channel allocation problem as an interference minimization problem and devise a K-CUT based algorithm to solve it. We further propose a low-complex iterative algorithm to obtain the optimal transmission power for each SD. The performance of the proposed algorithms is evaluated via theoretical analysis and numerical simulation. Simulation results indicate that if the intracell and intercell interference are not well coordinated, the superiorities of the AGWSN cannot be developed, and its performance is even worse than the traditional terrestrial network (TTN). Cooperated with our algorithms, the AGWSN significantly outperforms the TTN in terms of total power consumption and probability of successful decoding.

A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking

Unmanned aerial vehicles (UAVs) have found widespread commercial, civilian, and military applications. Wireless communication has always been one of the core technologies for UAV. However, the communication capacity is becoming a bottleneck for UAV to support more challenging application scenarios. The heavily-occupied sub-6 GHz frequency band is not sufficient to meet the ultra high-data-traffic requirements. The utilization of the millimeter-wave (mmWave) frequency bands is a promising direction for UAV communications, where large antenna arrays can be packed in a small area on the UAV to perform three-dimensional (3D) beamforming. On the other hand, UAVs serving as aerial access points or relays can significantly enhance the coverage and quality of service of the terrestrial mmWave cellular networks. In this paper, we provide a comprehensive survey on mmWave beamforming enabled UAV communications and networking. The technical potential of and challenges for mmWave-UAV communications are presented first. Then, we provide an overview on relevant mmWave antenna structures and channel modeling. Subsequently, the technologies and solutions for UAV-connected mmWave cellular networks and mmWave-UAV ad hoc networks are reviewed, respectively. Finally, we present open issues and promising directions for future research in mmWave beamforming enabled UAV communications and networking.

Cost- and Energy-Efficient Aerial Communication Networks With Interleaved Hovering and Flying

This work proposes a methodology for the energy-and cost-efficient 3-D deployment of an unmanned aerial vehicle (UAV)-based aerial access point (AAP), that exchanges a given amount of independent data with a set of ground user equipment (UE). Considering a fly-hover-communicate transmission scheme, the most energy-efficient 3-D hovering points (HPs) of the AAP are determined by decoupling the problem in the horizontal and vertical dimensions. First, we derive analytically the optimal hovering altitude that jointly maximizes the downlink and uplink global energy efficiency (GEE) of the system. Next, we propose the multilevel circle packing (MCP) algorithm to determine the minimal number of HPs and their associated horizontal coordinates, such that the AAP covers all the UEs in the given geographical area. A cost analysis is carried out to observe the variation of both fixed and variable costs; these are then minimized by suitably selecting the AAP's battery parameters, like the depth of discharge (DOD), defined as the portion of battery capacity that is consumed during a discharge cycle, and the velocity of the UAV. Simulation results show that: the UAV energy consumption has a significant impact on the 3-D HPs of the AAP; the time spent during the substitution swap of an out of power AAP has a major influence on the operational cost; the cost of the system can be optimized by suitably selecting the onboard battery and the UAV flight parameters.

Energy-Efficient 3-D Deployment of Aerial Access Points in a UAV Communication System

In this letter, we propose an energy-efficient 3-dimensional placement of multiple aerial access points (AAPs), in the desired area, acting as flying base stations for uplink communication from a set of ground user equipment (UE). The globally optimal energy-efficient vertical position of AAPs is derived analytically by considering the inter-cell interference and AAP energy consumption. The horizontal position of AAPs which maximize the packing density of the AAP coverage area are determined using a novel regular polygon-based AAP placement algorithm. We also determine the maximum number of non-interfering AAPs that can be placed in the desired area. The effect of the AAP energy consumption on the optimal placement and the analytic findings are verified via numerical simulations.

Access Points in the Air: Modeling and Optimization of Fixed-Wing UAV Network

Fixed-wing unmanned aerial vehicles (UAVs) are of great potential to serve as aerial access points (APs) owing to better aerodynamic performance and longer flight endurance. However, the inherent hovering feature of fixed-wing UAVs may result in discontinuity of connections and frequent handover of ground users (GUs). In this work, we model and evaluate the performance of a fixed-wing UAV network, where UAV APs provide coverage to GUs with millimeter wave backhaul. Firstly, it reveals that network spatial throughput (ST) is independent of the hover radius under real-time closest-UAV association, while linearly decreases with the hover radius if GUs are associated with the UAVs, whose hover center is the closest. Secondly, network ST is shown to be greatly degraded with the over-deployment of UAV APs due to the growing air-to-ground interference under excessive overlap of UAV cells. Finally, aiming to alleviate the interference, a projection area equivalence (PAE) rule is designed to tune the UAV beamwidth. Especially, network ST can be sustainably increased with growing UAV density and independent of UAV flight altitude if UAV beamwidth inversely grows with the square of UAV density under PAE.

Joint user association and interference mitigation for drone-assisted heterogeneous wireless networking

Using drone as aerial access point (AAP) to provide communication connectivity becomes an attractive application in future industrial wireless networking. However, the user association becomes more complex in the 3-tier drone-assisted heterogeneous networking (DAHN) due to the various types of APs. To address the issue, we formulated it as an association degree maximization problem, in which the load of APs and the user quality of service (QoS) rate requirement are considered. Since this is a non-linear mixed integer optimization problem, we relax the associated index variable and adopt the distributed heuristic method to find the near-optimal solution. Further, to mitigate the intra- and inter-layer interference, the coordinated multiple points (CoMP) supporting approach for edge user is used for enhancing the service performance. Simulations show that the proposed scheme provides better performances with a significant complexity reduction.

Connectivity and Blockage Effects in Millimeter-Wave Air-To-Everything Networks

Millimeter-wave (mmWave) offers high data rate and bandwidth for air-to-everything (A2X) communications including air-to-air, air-to-ground, and air-to-tower. MmWave communication in the A2X network is sensitive to buildings blockage effects. In this paper, we propose an analytical framework to define and characterise the connectivity for an aerial access point (AAP) by jointly using stochastic geometry and random shape theory. The buildings are modelled as a Boolean line-segment process with fixed height. The blocking area for an arbitrary building is derived and minimized by optimizing the altitude of AAP. A lower bound on the connectivity probability is derived as a function of the altitude of AAP and different parameters of users and buildings including their densities, sizes, and heights. Our study yields guidelines on practical mmWave A2X networks deployment.

Performance Analysis and Optimization of UAV Integrated Terrestrial Cellular Network

Unmanned aerial vehicles (UAVs) have been extensively applied as aerial access points to assist the terrestrial wireless network. Despite the inherent potential, nevertheless, it still remains to explore whether the gain of UAV access points (UAPs) could be fully harvested, which is critically dependent on the factors including the flight altitude and deployment density of UAPs. In this light, we investigate the performance of a downlink UAV integrated terrestrial cellular network (UTCN) and analytically study the influence of varying UAP altitude and density on the spatial throughput (ST) of UTCN. In particular, we obtain the UAP altitude upper bound, below which more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross-layer interference over the LOS paths becomes dominant, which results in significant degradation of network ST. More importantly, we reveal the limitation of the application of UAPs by showing that there exists a critical UAP density, beyond which network ST would encounter a rapid decrease. To fully exploit the potential of UAPs, we further tailor a probabilistic interference avoidance scheme and study the optimization of the UAP activated probability. Remarkably, network ST could be substantially improved using the optimized activated probability, i.e., network ST could increase with the growing UAP density and converge to a positive constant instead of zero in the dense UAP regime. Therefore, the results of this paper could provide insight on the deployment and optimization of UTCN.

Analyzing Effects of Directionality and Random Heights in Drone-Based mmWave Communication

Utilization of drones as aerial access points (AAPs) is a promising concept to enhance network coverage and area capacity promptly and on demand. The emerging millimeter-wave (mmWave) communication technology may in principle deliver higher data rates, thus, making the use of AAPs more effective. By extending the conventional (planar) stochastic geometry considerations, we construct a novel three-dimensional model for drone-based mmWave communication that captures the high directionality of transmissions as well as the random heights of the communicating entities. Choosing signal-to-interference ratio as our primary parameter of interest, we assess system performance with an emphasis on the impact of the “vertical” dimension in aerial mmWave connectivity. We also demonstrate that accurate performance assessment is only possible with simplified models for certain ranges of input parameters.