Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning

Unmanned Aerial Vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities. The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application. Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV that take place by means of frequency division duplex (FDD) via orthogonal or non-orthogonal multiple access (NOMA) schemes. The problem of jointly optimizing the bit allocation for uplink and downlink communication as well as for computing at the UAV, along with the cloudlet's trajectory under latency and UAV's energy budget constraints is formulated and addressed by leveraging successive convex approximation (SCA) strategies. Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet's trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet's trajectory.Comment: 14 pages, 5 figures, 2 tables, IEEE Transactions on Vehicular Technolog

Security enhancement for NOMA-UAV networks

Owing to its distinctive merits, non-orthogonal multiple access (NOMA) techniques have been utilized in unmanned aerial vehicle (UAV) enabled wireless base stations to provide effective coverage for terrestrial users. However, the security of NOMA-UAV systems remains a challenge due to the line-of-sight air-to-ground channels and higher transmission power of weaker users in NOMA. In this paper, we propose two schemes to guarantee the secure transmission in UAV-NOMA networks. When only one user requires secure transmission, we derive the hovering position for the UAV and the power allocation to meet rate threshold of the secure user while maximizing the sum rate of remaining users. This disrupts the eavesdropping towards the secure user effectively. When multiple users require secure transmission, we further take the advantage of beamforming via multiple antennas at the UAV to guarantee their secure transmission. Due to the non-convexity of this problem, we convert it into a convex one for an iterative solution by using the second order cone programming. Finally, simulation results are provided to show the effectiveness of the proposed scheme

Resource Allocation for Power Minimization in RIS-assisted Multi-UAV Networks with NOMA

Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks

Relaying in the Internet of Things (IoT): A Survey

The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA