The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users' minimum data rate. The proposed solution to the problem considers the users' movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of $\simeq ~1/60$ and $\simeq ~1/9$ compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of $\simeq ~1/7$ compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.

Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density.

In this paper, the efficient 3D placement of UAV as an aerial base station in providing wireless coverage for users in a small and large coverage area is investigated. In the case of providing wireless coverage for outdoor and indoor users in a small area, the Particle Swarm Optimization (PSO) and K-means with Ternary Search (KTS) algorithms are invoked to find an efficient 3D location of a single UAV with the objective of minimizing its required transmit power. It was observed that a single UAV at the 3D location found using the PSO algorithm requires less transmit power, by a factor of 1/5 compared to that when using the KTS algorithm. In the case of providing wireless coverage for users in three different shapes of a large coverage area, namely square, rectangle and circular regions, the problems of finding an efficient placement of multiple UAVs equipped with a directional antenna are formulated with the objective to maximize the coverage area and coverage density using the Circle Packing Theory (CPT). Then, the UAV efficient altitude placement is formulated with the objective of minimizing its required transmit power. It is observed that the large number of UAVs does not necessarily result in the maximum coverage density. Based on the simulation results, the deployment of 16, 19 and 26 UAVs is capable of providing the maximum coverage density of 78.5%, 82.5% and 80.3% for the case of a square region with the dimensions of 2 km × 2 km, a rectangle region with the dimensions of 6 km × 1.8 km and a circular region with the radius of 1.125 km, respectively. These observations are obtained when the UAVs are located at the optimum altitude, where the required transmit power for each UAV is reasonably small.

In this paper, the influence of rainfall on the deployment of UAV as an aerial base station in the Malaysia 5G network is studied. The outdoor-to-outdoor and outdoor-to-indoor path loss models are derived by considering the user’s antenna height, rain attenuation, and the wall penetration loss at high frequencies. The problem of finding the UAV 3D placement is formulated with the objective to minimize the total path loss between the UAV and all users. The problem is solved by invoking two algorithms, namely Particle Swarm Optimization (PSO) and Gradient Descent (GD) algorithms. The performance of the proposed algorithms is evaluated by considering two scenarios to determine the optimum location of the UAV, namely outdoor-to-outdoor and outdoor-to-indoor scenarios. The simulation results show that, for the outdoor-to-outdoor scenario, both algorithms resulted in similar UAV 3D placement unlike for the outdoor-to-indoor scenario. Additionally, in both scenarios, the proposed algorithm that invokes PSO requires less iterations to converge to the minimum transmit power compared to that of the algorithm that invokes GD. Moreover, it is also observed that the rain attenuation increases the total path loss for high operating frequencies, namely at 24.9 GHz and 28.1 GHz. Hence, this resulted in an increase of UAV required transmit power. At 28.1 GHz, the presence of rain at the rate of 250 mm/h resulted in an increase of UAV required transmit power by a factor of 4 and 15 for outdoor-to-outdoor and outdoor-to-indoor scenarios, respectively.

This paper presents an adaptive modulation in a single-input-single-output (SISO)-OFDM-based cooperative system that employs a Constant Amplitude Zero Autocorrelation (CAZAC) preamble-based SNR estimator. A novel CAZAC preamble-based SNR estimator that does not incur a throughput penalty is derived by exploiting a modified CAZAC preamble structure for the OFDM system. The proposed CAZAC SNR estimator is used to estimate the channel quality by finding the instantaneous SNR of the received cooperative data, which is sent to the transmitter via feedback channel. The modulation scheme is then changed according to the estimated SNR. The performance of the CAZAC preamble-based SNR estimator exhibits less than 0.02 dB and less than 0.2 dB bias when communicating over AWGN and SUI-5 channels, respectively. The proposed adaptive modulation scheme with the CAZAC SNR estimator significantly improved the channel throughput in comparison with the corresponding benchmarker scheme dispensing with adaptive modulation that invokes M -PSK and M -QAM. More explicitly, the proposed adaptive modulation system with the CAZAC SNR estimator that invokes M -PSK exhibits significant improvement by about 5 dB at BER=10 ^-3 in comparison to its corresponding benchmarker scheme dispensing with adaptive modulation when communicating over the SUI-5 channel. On the other hand, the relay-aided cooperative transmission exhibited an approximately 6 dB SNR gain in terms of channel throughput in comparison to the corresponding non-cooperative benchmarker scheme, and benefited from spatial diversity.