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.

The operation of unmanned aerial vehicles (UAV) imposes various challenges on radio spectrum management to achieve safe operation, efficient spectrum utilization, and coexistence with legacy wireless networks. Current spectrum schemes have limitations when applied to UAV networks due to the dynamic nature of UAV networks that require adaptive spectrum decisions and robust schemes that provide seamless and reliable services. Existing surveys mostly focus on UAV applications, channel models, and security challenges, with a lack of studies on spectrum management in the context of UAV networks. Further, current spectrum efforts focus on terrestrial networks that feature fixed infrastructure and less dynamicity as compared to UAV networks. This motivates the need to revisit existing approaches and identify suitable schemes that allow for the rapid integration of UAVs with existing wireless technologies. Motivated by this observation, this article presents a comprehensive survey on spectrum management for UAV operations. It identifies suitable management schemes that align with UAV features and requirements to enable safe and efficient usage of the radio spectrum. The article assumes coexistence with prevalent wireless technologies that occupy the spectrum. It first presents the ruling from policymakers and regulators and discusses operation bands and radio interfaces. It then introduces deployment scenarios (applications and architectures) as standalone or heterogeneous networks. This is followed by a systematic structure for the management tools that employ deterministic, opportunistic, and competitive schemes. In addition, network monitoring, patrolling, and enforcement schemes are identified. The survey also specifies key tools that can be leveraged for spectrum management solutions such as optimization and blockchain. Finally, it recognizes open research directions and challenges that need to be tackled to advance UAV communications.

In unmanned aerial vehicle (UAV)-assisted wireless networks, the potential of the high probability of line-of-sight links could be a disadvantage for other nearby wireless networks because of interference caused by a UAV. For this reason, using interference mitigation techniques (IMTs) for UAV-assisted wireless networks is necessary to minimize interference effects. For instance, the mobility of UAVs gives a chance to manage interference better to avoid being a source of interference or being susceptible to interference. After the first-generation mobile network was introduced in the early 1980s, mobile communication systems were developed through several stages of evolution over the following few decades to satisfy rising demand and more stringent specifications. Different IMTs are needed due to these generations’ differences. Unlike previous research, this research presents a systematic review of IMTs in the current and future UAV-assisted wireless networks and discusses their challenges. We also review the current research trends of UAV IMTs and their future insights. Our motivation for conducting this research is the need for a systematic review addressing these issues. In this systematic review, we use a more precise classification of the IMTs for UAV-assisted wireless networks. We classify them into four different techniques, namely, adaptive modulation and coding schemes, dynamic antenna pattern adjustment, dynamic transmit power control, and sub-channel scheduling. Moreover, we discuss the IMTs for UAVs using B5G wireless technologies such as intelligent reflecting surfaces, rate-splitting, super modular massive multiple-input multiple-output, Terahertz communications, holographic beamforming, blockchain-based spectrum sharing, and artificial intelligence and machine learning schemes. Furthermore, we identify the challenges of UAV IMTs and their open research directions such as wireless radio frequency spectrum scarcity, lack of networking scalability, beam distortion, resource-constrained management, and new sources of interference. We believe this research will help researchers choose the best technique to mitigate the interference effects in UAV-assisted wireless networks.

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks are not operational due to natural disasters. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. Prior studies on UAV-based wireless coverage typically consider an air-to-ground path loss model, which assumes that the users are outdoor and located on a 2-D plane. In this paper, we propose using UAVs to provide wireless coverage for indoor users inside a high-rise building. First, we present realistic outdoor–indoor path loss models and describe the tradeoff introduced by these models. Then, we study the problem of efficient placement of a single UAV, where the objective is to minimize the total transmit power required to cover the entire high-rise building. The formulated problem is generally difficult to solve. To that end, we consider three cases of practical interest and provide efficient solutions to the formulated problem under these cases. Then, we study the problem of minimizing the number of UAVs required to provide wireless coverage to the high-rise building and prove that this problem is NP-complete. Due to the intractability of the problem, we use clustering to minimize the number of UAVs required to cover the indoor users. In our proposed algorithm, we check if the maximum transmit power of a UAV is sufficient to cover each cluster. If not, the number of clusters is incremented by one, and the problem is solved again. In the uniform split method, we split the building into $k$ regular structures and utilize $k$ UAVs to provide wireless coverage for indoor users regardless of user distribution. We demonstrate through simulations that the method that splits the building into regular structures requires 80% more number of UAVs relative to our proposed algorithm.

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider downlink scenarios from an aerial base station to ground users. In this paper, we consider an uplink scenario under disaster situations (such as earthquakes or floods), when cellular networks are down. We formulate the placement problem of UAVs, where the objective is to determine the locations of a set of UAVs that maximize the time duration of uplink transmission until the first wireless device runs out of energy. We prove that this problem is NP-complete. Due to its intractability, we start by restricting the number of UAVs to be one. We show that under this special case the problem can be formulated as a convex optimization problem under a restriction on the coverage angle of the ground users. After that, we propose a gradient projection-based algorithm to find the optimal location of the UAV. Based on this, we then develop an efficient algorithm for the general case of multiple UAVs. The proposed algorithm starts by clustering the wireless devices into several clusters where each cluster being served by one UAV. After it finishes clustering the wireless devices, it applies the gradient projection-based algorithm in each cluster. We also formulate the problem of minimizing the number of UAVs required to serve the ground users such that the time duration of uplink transmission of each wireless device is greater than or equal to a threshold value. We prove that this problem is NP-complete and propose to use two efficient methods to determine the minimum number of UAVs required to serve the wireless devices. We validate the analysis by simulations and demonstrate the effectiveness of the proposed algorithms under different cases.

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.