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Severe uplink UAV-to-BS and downlink BS-to-UAV interferences due to the LoS-dominated UAV-BS channels at high UAV altitude.
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Enabling high-rate, low-latency and ultra-reliable wireless communications between unmanned aerial vehicles (UAVs) and their associated ground pilots/users is of paramount importance to realize their large-scale usage in the future. To achieve this goal, cellular-connected UAV, whereby UAVs for various applications are integrated into the cellular...
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... the following new considerations need to be taken into account. Severe Aerial-Ground Interference: One major challenge to ensure the efficient coexistence between ground and aerial UEs lies in the severe aerial-ground interference, which is illustrated in Fig. ...
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Citations
... This necessitates autonomous and effective UAV flight path planning and uninterrupted connectivity as they depend on batteries and communications for operation and control, respectively [4]. Effective flight path planning must account for the surrounding complex 3D environment and obstacles to determine the optimal flight path, whereas uninterrupted connectivity facilitates precise control of UAVs, allowing for real-time flight optimization [5]. However, maintaining an uninterrupted excellent connection conflicts with the ideal minimum flight path, as there is no guarantee of consistent signal strength on the optimal route. ...
Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.
... In the literature, there have been various prior works investigating the use of terrestrial cellular networks to support the communication and sensing of UAVs. On the one hand, cellular-connected UAV has been extensively studied [26], in which GBSs support the communication of UAVs as aerial users by using the spectrum resources originally allocated to terrestrial subscribers. In particular, existing works studied cellular-connected UAV from different perspectives such as interference mitigation [27], [28], energy-efficient communications [29], [30], and mobile edge computing [31]. ...
... The authors in [11] exploited the cellular-connected UAV to enable bi-static sensing with GBS. In these above works with cellular-connected UAVs [10], [11], [26]- [31], the UAV trajectory control is introduced as a new design degree of freedom (DoF) to enhance the desired signal strength and mitigate the undesired interference, thus improving the communication and sensing performances. However, these works only considered communications with cellular-connected UAVs [26], [28]- [30], [32], or only investigated mono-static or bi-static ISAC for cellular-connected UAVs with one single GBS. ...
... In these above works with cellular-connected UAVs [10], [11], [26]- [31], the UAV trajectory control is introduced as a new design degree of freedom (DoF) to enhance the desired signal strength and mitigate the undesired interference, thus improving the communication and sensing performances. However, these works only considered communications with cellular-connected UAVs [26], [28]- [30], [32], or only investigated mono-static or bi-static ISAC for cellular-connected UAVs with one single GBS. The research on using terrestrial networked ISAC systems to support LAE has not been investigated in the literature yet. ...
This paper exploits the networked integrated sensing and communications (ISAC) to support low-altitude economy (LAE), in which a set of networked ground base stations (GBSs) cooperatively transmit joint information and sensing signals to communicate with multiple authorized unmanned aerial vehicles (UAVs) and concurrently detect unauthorized objects over the interested region in the three-dimensional (3D) space. We assume that each GBS is equipped with uniform linear array (ULA) antennas, which are deployed either horizontally or vertically to the ground. We also consider two types of UAV receivers, which have and do not have the capability of canceling the interference caused by dedicated sensing signals, respectively. Under each setup, we jointly design the coordinated transmit beamforming at multiple GBSs together with the authorized UAVs' trajectory control and their GBS associations, for enhancing the authorized UAVs' communication performance while ensuring the sensing requirements. In particular, we aim to maximize the average sum rate of authorized UAVs over a given flight period, subject to the minimum illumination power constraints toward the interested 3D sensing region, the maximum transmit power constraints at individual GBSs, and the flight constraints of UAVs. These problems are highly non-convex and challenging to solve, due to the involvement of binary UAV-GBS association variables as well as the coupling of beamforming and trajectory variables. To solve these non-convex problems, we propose efficient algorithms by using the techniques of alternating optimization, successive convex approximation, and semi-definite relaxation. Numerical results show that the proposed joint coordinated transmit beamforming and UAV trajectory designs efficiently balance the sensing-communication performance tradeoffs and significantly outperform various benchmarks.
... These networks must support high data transfer rates and low latency to facilitate real-time decision-making and synchronization between aerial and ground units. The choice of communication technology, whether it is radio frequency [14], cellular networks [15], or satellite links [16], is dictated by factors like range, bandwidth, environmental conditions, and the specific requirements of the operation. She et al. [17] developed a framework for ultra-reliable and low-latency communications in UAV systems, optimizing UAV altitude and antenna configuration to maximize communication range while meeting round-trip delay and packet loss probability requirements. ...
... Moreover, in cases where the altitude of the drone exceeds a certain threshold, the path loss can be likened to the propagation that occurs in unobstructed space. This relationship is demonstrated by Equations (5) and (6). Nevertheless, the received signal reference power (RSRP) experiences a significant decrease as the drone ascends to higher altitudes, specifically at 170 metres. ...
The operational range of conventional and license-free radio-controlled drones is limited due to line-of-sight restrictions (LoS). There exists a definitive method for operating a drone. Consequently, in order to fly the drone beyond the visual line of sight (BVLoS), it is necessary to replace the drone's original wireless communications equipment with a device that requires a licence and is connected to a cellular network. Long-Term Evolution (LTE), a terrestrial communication technique, enables a drone to establish a real-time connection with a ground station. This connection serves the goals of command and control (C&C) as well as payload delivery. Nevertheless, it is important to note that the electromagnetic environment undergoes changes as altitude increases, which can potentially complicate the process of interfacing with drones over terrestrial cellular networks. The objective of this article is to develop a prototype control system for low-altitude microdrones using LTE technology. Additionally, it seeks to assess the feasibility and effectiveness of cellular connectivity for drones operating at various altitudes. This evaluation will be conducted by examining factors like as latency, handover, and signal strength. At a certain altitude, the received signal experiences a decrease in power level by 20 dBm and a degradation in signal quality by 10 dB. The data throughput of the downlink had a fall of 70%, while the latency exhibited an increase of 94 ms. Despite meeting the basic criteria for drone cellular connection, the existing LTE network necessitates enhancements in order to expand aerial coverage, mitigate interference, and minimise network latency.
... Academic and industrial research communities have extensively explored the integration of UAVs with LTE and 5G networks as clients, highlighting both challenges and opportunities [7][8][9]. Therefore, there has been a strong uptake of cellular connectivity to connect the UAVs and support beyond line-of-sight (BLoS) operations. Furthermore, exploring UAVs as mobile Aerial Base Stations (ABS) presents an intriguing direction. ...
... This deployment method, while effective for various applications, does exhibit constraints related to the link between the ground controller and the UAV. These constraints include limited spectrum frequencies and relatively low data rates, as discussed in previous studies [8,52]. Both civil and military sectors have increasingly demanded the establishment of a robust, reliable, and wide-reaching wireless network to control the UAV, and here the cellular-connected UAV networks, sometimes known as ''Cellular Assisted UAVs'', come, as depicted in Fig. 4 [53,54]. ...
... effectiveness. Leveraging the extensive global coverage of cellular networks, this integration facilitates remote UAV control, live video streaming, and large-scale air traffic monitoring [1]. However, challenges exist in adapting existing cellular network designs primarily intended for ground users to meet the unique requirements of UAVs such as air-to-ground channel models and frequent handovers. ...
... These challenges include increased system complexity, the potential for Line-of-Sight (LoS) control, and higher HO rates due to UAV speed [45]. The primary differences between our previous work and this work has been highlighted in Table 2. ...
... Comparison with our previous work[45] Managing UAV HOs necessitates a distinct approach compared to conventional UE HO techniques. Techniques designed for terrestrial UEs may not be directly applicable to UAV networks due to the unique aspects of UAV operations. ...
The integration of Unmanned Aerial Vehicles (UAVs) into future 6G networks will open new possibilities for applications ranging from surveillance to communication infrastructure maintenance, precision agriculture, and surveying. However, ensuring uninterrupted connectivity for UAVs operating in remote or dynamic environments remains a significant challenge. This paper presents a novel approach to achieving seamless handover for UAVs when transitioning between terrestrial and satellite communication networks. The proposed method in this paper, leverages graph theory and develop a decision-making algorithm to optimise handover decisions, minimizing latency, improving performance, and reducing service disruption. It establishes a comprehensive graph model that represents the dynamic topology of available network nodes, including terrestrial base stations and low earth orbit (LEO) satellites, which adapts in real-time to changes in UAV position and network conditions. The approach incorporates a decision-making algorithm that considers several factors, such as received signal strength (RSS), signal-to-noise ratio (SNR), and elevation angle, to determine the optimal time and location for a handover between terrestrial base stations and satellite links. This ensures a seamless transition between communication links, minimizing service disruption. The performance of this method is evaluated through extensive simulations and comparison with existing solutions demonstrating significant improvements in RSS, SNR, throughput, latency, ping-pongs and enhanced overall UAV connectivity. The proposed graph method-based seamless handover solution represents a crucial advancement in enabling reliable and uninterrupted communication for UAVs operating in remote and challenging environments. By managing handovers between terrestrial and satellite networks, this research contributes to the realisation of the full potential of UAVs in emerging applications, thereby advancing the state-of-the-art in UAV technology.
... These cells are particularly beneficial in areas with high UAV activity. However, deploying such cells requires additional investment and may only become cost-effective when the number of UAVs connecting to the network increases [59]. ...
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.
... However, most of the current commercial UAVs use unlicensed spectrum for simple point-to-point communication, resulting in low communication rates, instability, insecurity, susceptibility to interference, and only flying within line of sight (LOS), all of which are obstacles to the future development of UAVs. To address this problem, both academia and industry have explored a new paradigm in recent years, the cellular connected UAV [5][6][7][8], which uses the UAV as an over-the-air user device for terrestrial base station (BS) services to access the cellular network, this approach is considered a prospective program. The advantage of cellular-connected UAV is that they can utilize established LTE and 5G networks to provide greater reliability, wider coverage, and greater data transfer capabilities, rather than relying on limited point-to-point connections [9]. ...
To address the challenges in the era of artificial intelligence, this paper investigates how computing offloading techniques can be utilized to improve the system performance in cellular connected unmanned aerial vehicle (UAV) networks with limited resources using non-orthogonal multiple access (NOMA) and mobile edge computing techniques. Energy consumed by the UAV in performing tasks and the time required to complete tasks are minimized by joint optimal task offloading decision, sub-channel allocation, and computing resource allocation while satisfying the user's quality of service requirements. We consider this problem as a nonlinear programming problem with integer and non-integer variables and solve it by iteratively updating the resource allocation and offloading decision. Specifically, given a fixed offloading decision, a many-to-one matching model is first applied for sub-channel allocation, then a detailed allocation of computing resource is performed by optimizing the total system overhead, and are solved by utilizing the matching algorithm and the Lagrange dual method, respectively. Then, according to the scenarios we have been given for resource allocation, we design a multi-objective task offloading algorithm to update the offloading decision. Simulation experiments demonstrate the feasibility of the scheme, which shows a significant reduction in both energy consumption and latency compared to the benchmark scheme.
... The proliferation of UAV nodes is causing a stir in the world of wireless network infrastructure, demanding a reshaping of traditional terrestrial networks to tackle a host of challenges [48]. One of the most pressing issues for UAV-assisted commu-The Role of 6G and Beyond on the Road to Net-Zero Carbon nications is the optimal three dimensions (3D) placement of these devices [49,50]. ...
