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Unmanned aerial vehicle (UAV) relay networks are convinced to be a significant complement to terrestrial infrastructures to provide robust network capacity. However, most of the existing works either considered enhanced mobile broadband (eMBB) payload communication or ultra-reliable and low latency communications (URLLC) control information communication. In this paper, we investigate resource allocation for the eMBB payload and URLLC control information communication multiplexing in a multi-UAV relay network. We firstly propose a multi-UAV relay model comprehensively considering path loss, small-scale channel fading and different quality of service requirements of eMBB and URLLC communications. Then we formulate the multiplexing problem as a joint user association, bandwidth and transmit power optimization problem to improve total transmission data rate and reduce power consumption. The solution of this problem is challenging due to different capacity characteristics of eMBB and URLLC communications, the coupling of continuous variables and integer variables, and the non-convexity. To mitigate these challenges, we equivalently decompose the original optimization problem into a URLLC problem and an eMBB problem. For the URLLC problem, we derive closed-form expressions of the optimal bandwidth and transmit power. For the eMBB problem, we develop an iterative solution framework of alternatively optimizing user association, bandwidth and transmit power.

This letter is concerned with investigating a joint user association and unmanned aerial vehicle (UAV) location optimization problem for UAV-aided communications. Different from the majority of existing studies, this letter aims at deriving solutions to the joint optimization problem that is modeled as a mixed-integer non-convex optimization problem (MINCOP) with a goal of maximizing users’ total achievable data rates. A novel iterative optimization scheme is proposed to handle the MINCOP. Specifically, the MINCOP is first decomposed into an integer optimization subproblem and a non-convex optimization subproblem. An iterative algorithm is then developed to optimize these two separated subproblems alternatively. Besides, the non-convexity of the non-convex subproblem is handled via successive convex approximation. Based on approximate results, Karush-Kuhn-Tucker (KKT) conditions are leveraged to obtain the evolutionary-form solution to the joint optimization problem. The comparison with the state-of-the-art verifies that the developed algorithm is convergent and is able to achieve good performance.

This paper is concerned with the network selection for heterogeneous networks. Although many network selection approaches have been developed for heterogeneous networks, few of them explore the network selection problem for integrated cellular and drone-cell networks. Furthermore, most of the existing designs may be unsuitable for the integrated cellular and drone-cell networks owing to the high dynamic drone-cell-user association and the relatively limited drone-cell network capacity. In this paper, we investigate a network selection problem for the integrated cellular and drone-cell networks with a goal of maximizing a proportional fairness function of time average utilities across users under coarse correlated equilibrium constraints and minimum time average utility constraints. To mitigate this challenging problem, we first convert it into a non-linear integer programming (NLIP) problem based on our derived theoretical results. Next, we propose a repeated-stochastic-game-based efficient and fair network selection (RSG-EF) algorithm to alleviate the NLIP problem by leveraging a linear approximation mechanism. Simulation results show that the RSG-EF algorithm can achieve the highest total utility and a high level of fairness across users compared with three benchmark algorithms.

Unmanned aerial vehicle (UAV) networks are convinced as a significant part of 5G and emerging 6G wireless networks. UAV slicing is a promising proposal of converging different services onto a common UAV network without deploying individual network solution for each type of service. This paper is concerned with UAV slicing for providing energy-efficient and fair service coverage for enhanced mobile broad-band (eMBB) users (UEs). Aiming at physically configuring UAV slices, the UAV slicing problem is formulated as a time-dependent mixed-integer-non-convex programming problem with a goal of maximizing all UEs’ data rates while minimizing UAVs’ total transmit power. To mitigate this challenging problem, we first decompose the original problem into two time-dependent subproblems using a Lyapunov approach. We then derive the procedure of tackling the non-convexity and the mixed-integer property of the subproblems by exploring a successive convex approximate (SCA) method and an alternative optimization scheme, respectively. Based on the derived results, we develop an algorithm with provable performance guarantees to mitigate the two subproblems repeatedly.

This letter is concerned with power control for a ultra-reliable and low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system incorporated with deep neural network (DNN) based channel estimation. Particularly, we formulate the power control problem for the UAV system as an optimization problem to accommodate the URLLC requirement of uplink control and non-payload signal delivery while ensuring the downlink high-speed payload transmission. This problem is challenging to be solved due to the requirement of analytically tractable channel models and the non-convex characteristic as well. To address the challenges, we propose a novel power control algorithm, which constructs analytically tractable channel models based on DNN estimation results and explores a semidefinite relaxation (SDR) scheme to tackle the non-convexity. Simulation results demonstrate the accuracy of the DNN estimation and verify the effectiveness of the proposed algorithm.

Future cellular networks are expected to offload flash crowd traffic in an area (e.g., a stadium with ongoing sporting events). Owing to the temporary characteristic of this traffic, investing in infrastructures may not be financially viable to provide service coverage for this traffic. Resorting to drone-cells (i.e., unmanned aerial vehicles acting as aerial base stations) to offer rapid but temporary service coverage is a promising solution. Although drone-cells provide quick coverage, efficient placement of drones is challenging. In this paper, we model heterogeneous traffic distribution and formulate a three-dimensional (3-D) drone-cell placement problem with a goal of mitigating congestion for future cellular networks under heterogeneous traffic. To make this problem easy to solve, we convert the 3-D drone-cell placement problem into a new 3-D drone-cell placement problem based on our derived theoretical results. We propose four algorithms to solve the new 3-D drone-cell placement problem. In these algorithms, we first develop four types of drone-cell altitude solvers to achieve a drone-cell altitude. With such an altitude, we further convert the new 3-D problem into a mixed-integer quadratic cone optimization problem in 2-D space, which is solvable by an optimization tool. Simulation results show that these algorithms can effectively help deploy a drone-cell to mitigate network congestion.

Owing to the explosive growth of requirements of rapid emergency communication response and accurate observation services, airborne communication networks (ACNs) have received much attention from both industry and academia. ACNs are subject to heterogeneous networks that are engineered to utilize satellites, high-altitude platforms (HAPs), and low-altitude platforms (LAPs) to build communication access platforms. Compared to terrestrial wireless networks, ACNs are characterized by frequently changed network topologies and more vulnerable communication connections. Furthermore, ACNs have the demand for the seamless integration of heterogeneous networks such that the network quality-of-service (QoS) can be improved. Thus, designing mechanisms and protocols for ACNs poses many challenges. To solve these challenges, extensive research has been conducted. The objective of this special issue is to disseminate the contributions in the field of ACNs. To present this special issue with the necessary background and offer an overall view of this field, three key areas of ACNs are covered. Specifically, this paper covers LAP-based communication networks, HAP-based communication networks, and integrated ACNs. For each area, this paper addresses the particular issues and reviews major mechanisms. This paper also points out future research directions and challenges.

This paper is concerned with the resource allocation in a multi-unmanned aerial vehicle (UAV)-aided network for providing enhanced mobile broadband (eMBB) services for user equipments. Different from most of the existing network resource allocation approaches, we investigate a joint non-orthogonal user association, subchannel allocation and power control problem. The objective of the problem is to maximize the network energy efficiency under the constraints on user equipments’ quality of service, UAVs’ network capacity and power consumption. We formulate the energy efficiency maximization problem as a challenging mixed-integer non-convex programming problem. To alleviate this problem, we first decompose the original problem into two subproblems, namely, an integer non-linear user association and subchannel allocation subproblem and a non-convex power control subproblem. We then design a two-stage approximation strategy to handle the non-linearity of the user association and subchannel allocation subproblem and exploit a successive convex approximation approach to tackle the non-convexity of the power control subproblem. Based on the derived results, we develop an iterative algorithm with provable convergence to mitigate the original problem. Simulation results show that our proposed framework can improve energy efficiency compared with several benchmark algorithms.

This paper is concerned with path planning for unmanned aerial vehicles (UAVs) flying through low altitude urban environment. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or do not incorporate multiple objectives. In this paper, we propose a multiobjective path planning (MOPP) framework to explore a suitable path for a UAV operating in a dynamic urban environment, where safety level is considered in the proposed framework to guarantee the safety of UAV in addition to travel time. To this aim, two types of safety index maps (SIMs) are developed first to capture static obstacles in the geography map and unexpected obstacles that are unavailable in the geography map. Then an MOPP method is proposed by jointly using offline and online search, where the offline search is based on the static SIM and helps shorten the travel time and avoid static obstacles, while the online search is based on the dynamic SIM of unexpected obstacles and helps bypass unexpected obstacles quickly. Extensive experimental results verify the effectiveness of the proposed framework under the dynamic urban environment.

This paper is concerned with providing radio access network (RAN) elements (supply) for flash crowd traffic demands. The concept of multi-tier cells [heterogeneous networks (HetNets)] has been introduced in 5G network proposals to alleviate the erratic supply–demand mismatch. However, since the locations of the RAN elements are determined mainly based on the long-term traffic behavior in 5G networks, even the HetNet architecture will have difficulty in coping up with the cell overload induced by flash crowd traffic. In this paper, we propose a proactive drone-cell deployment framework to alleviate overload conditions caused by flash crowd traffic in 5G networks. First, a hybrid distribution and three kinds of flash crowd traffic are developed in this framework. Second, we propose a prediction scheme and an operation control scheme to solve the deployment problem of drone cells according to the information collected from the sensor network. Third, the software-defined networking technology is employed to seamlessly integrate and disintegrate drone cells by reconfiguring the network. Our experimental results have shown that the proposed framework can effectively address the overload caused by flash crowd traffic.

Hot-electron bolometers (HEBs) made with MgB 2 have proven to have a large intermediate frequency and have shown potential for a low noise into the terahertz range. Although practical results from these mixers have been achieved fairly quickly, effort is still needed to realize an MgB 2 HEB with improved sensitivity to compete with state-of-the-art mixers. Here, we present the results of our mixer work to achieve MgB 2 HEBs based on hybrid physical chemical vapor deposition grown films, with improved sensitivity and higher temperature operation. A new fabrication process is developed which utilizes even thinner films (

Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure network slicing systems for bursty ultra-reliable and low latency communications (URLLC) service provision due to its stringent requirements on low packet blocking probability and low codeword error decoding probability. In this paper, we propose to orchestrate network resources for a network slicing system to guarantee a more reliable bursty URLLC service provision. We re-cut physical resource blocks (PRBs) and derive the minimum upper bound of bandwidth for URLLC transmission with a low packet blocking probability. We correlate coordinated multipoint (CoMP) beamforming with channel uses and derive the minimum upper bound of channel uses for URLLC transmission with a low codeword error decoding probability. Considering the agreement on converging diverse services onto shared infrastructures, we further investigate the network slicing for URLLC and enhanced mobile broadband (eMBB) service multiplexing. Particularly, we formulate the service multiplexing as an optimization problem to maximize the long-term total slice utility. The mitigation of this problem is challenging due to the requirements of future channel information and tackling a two timescale issue. To address the challenges, we develop a joint resource optimization algorithm based on a sample average approximate (SAA) technique and a distributed optimization method with provable performance guarantees.

This paper is concerned with the design of routing protocol capable of congestion mitigation for drone-cells communication networks where drone-cells remain stationary in the sky as relays. All of the (distance or hop-count based) existing routing protocols can perform well when the network is lightly loaded. Once the network is heavily loaded, a large number of packets might be backlogged in queues of network nodes since these protocols can not be aware of the network congestion condition. In this paper, we propose a queuing delay and transmission delay based routing protocol (QDTD) to relieve the network congestion caused by heavily loaded traffic. First, QDTD designs a novel ForWard-Back (FWB) queue architecture that significantly reduces the number of queues maintained at each network node. Second, both queuing delay and transmission delay are leveraged as a routing metric to enhance the performance of QDTD. Experimental results show that QDTD can effectively relieve the network congestion and reduce the overall network delay and achieve high throughput.

Flying UAV network has tremendous potential for civilian and military applications. This paper is concerned with the design of a routing protocol for fast flying UAV network where UAVs are flying fastly and randomly in the sky. Due to the high mobility degree of UAVs, there may not exist instantaneous end-to-end communication path; thus, it is particularly challenging to design an available routing protocol with low transmission delay. The contribution of this paper is that we propose a Fountain-code based Greedy Queue and Position Assisted routing protocol, called FGQPA. It designs a Power Allocation and Routing (PAR) policy to relief the effect of the queue backlog on the overall network delay and employs a “nearest span” scheme to direct packets to the destination with a small delay. Our experimental results show that the proposed FGQPA can achieve lower transmission delay than the state-of-the-art disruption tolerant network routing protocol.