This paper is concerned with autonomous aerial vehicle (AAV) video coding and transmission in scenarios such as aerial search and monitoring. Unlike existing methods of modeling AAV video source coding and channel transmission separately, we investigate the joint source-channel optimization (JSCO) issue for video coding and transmission. Particularly, we design eight-dimensional delay-power-rate-distortion models in terms of source coding and channel transmission and characterize the correlation between video coding and transmission, with which a JSCO problem is formulated. Its objective is to minimize end-to-end distortion and AAV power consumption by optimizing fine-grained parameters related to AAV video coding and transmission. This problem is confirmed to be a challenging sequential-decision and non-convex optimization problem. We therefore decompose it into a family of repeated optimization problems by Lyapunov optimization and design an approximate convex optimization scheme with provable performance guarantees to tackle these problems. Based on the theoretical transformation, we propose a Lyapunov repeated iteration (LyaRI) algorithm. Both objective and subjective experiments are conducted to comprehensively evaluate the performance of LyaRI. The results indicate that, compared with its counterparts, LyaRI achieves better video quality and stability performance, with a 47.74% reduction in the variance of the obtained encoding bit.

Near-space information networks (NSINs) composed of high-altitude platforms (HAPs) and high- and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quick, robust, and cost-efficient sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control techniques, NSINs have been envisioned as an essential component of the emerging sixth-generation of mobile communication systems. This article reveals some critical issues needing to be tackled in NSINs through conducting experiments and discusses the latest advances in NSINs in the research areas of channel modeling, networking, and transmission from a forward-looking, comparative, and technical evolutionary perspective. In this article, we highlight the characteristics of NSINs and present the promising use cases of NSINs. The impact of airborne platforms' unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advances in HAP channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. A comprehensive review of the networking techniques of NSINs in network deployment, handoff management, and network management aspects is provided. Besides, the promising techniques and communication protocols of the physical (PHY) layer, medium access control (MAC) layer, network layer, and transport layer of NSINs for achieving efficient transmission over NSINs are reviewed, and we have conducted experiments with practical NSINs to verify the performance of some techniques. Finally, we outline some open issues and promising directions for NSINs deserved for future study and discuss the corresponding challenges.

To address the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper proposes an SnS-aware CE framework based on adaptive subarray partitioning. We first investigate spherical wave propagation and various SnS characteristics and construct an SnS near-field channel model for XL-MIMO systems. Due to the limitations of uniform subarray patterns in capturing SnS, we analyze the adverse effects of the non-ideal array segmentation (over- and under-segmentation) on CE accuracy. To counter these issues, we develop a dynamic hybrid beamforming-assisted power-based subarray segmentation paradigm (DHBF-PSSP), which integrates power measurements with a dynamic hybrid beamforming structure to enable joint subarray partitioning and decoupling. A power-adaptive subarray segmentation (PASS) algorithm leverages the statistical properties of power profiles, while subarray decoupling is achieved via a subarray segmentation-based sampling method (SS-SM) under radio frequency (RF) chain constraints. For subarray CE, we propose a subarray segmentation-based assorted block sparse Bayesian learning algorithm under the multiple measurement vectors framework (SS-ABSBL-MMV). This algorithm exploits angular-domain block sparsity under a discrete Fourier transform (DFT) codebook and inter-subcarrier structured sparsity. Simulation results confirm that the proposed framework outperforms existing methods in CE performance.

This paper is concerned with rate control (RC) for autonomous aerial vehicle (AAV) video encodingto produce a stable video bitrate and high quality videos under specific constraints. To this aim, we theoretically investigate the relationships between encoding parameters and bitrate and distortion, and design an accurate online rate-distortion (R-D) model by employing linear regression and predictive artificial intelligence (AI) methods. Considering the sensitivity of AAV video encoding to latency and power consumption, we further establish the mathematical relationships between encoding parameters and encoding time and power consumption. By integrating the R-D and encoding time and power consumption models, we formulate a multi-timescale optimization problem and propose a novel algorithm to solve it. Specifically, we decompose it into a family of single-timescale problems via an alternating direction method of multipliers (ADMM). In addition, we design an iterative optimization scheme to solve the single-timescale problems with low computational complexity. Extensive experiments are conducted to validate the designed model and algorithm. Experimental results indicate that the designed model has small estimation errors, and the variance of Y-PSNR achieved by the proposed algorithm is not greater than 26.3% of its counterpart.

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are spherically or uniformly distributed. Different from the existing works, this paper investigates two particular motion patterns of radiosondes in a typhoon, which significantly challenges the theoretical analysis. According to their particular motion patterns, this paper first separately models the distributions of horizontal and vertical distances from a radiosonde to its receiver. Secondly, this paper derives the closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of a radiosonde's three-dimensional (3D) propagation distance to its receiver. Thirdly, this paper derives the analytical expression of the uplink CP for any radiosonde in the network. Finally, extensive numerical simulations are conducted to validate the theoretical analysis, and the influence of various network design parameters is comprehensively discussed. Simulation results show that when the signal-to-interference-noise ratio (SINR) threshold is below -35 dB, and the density of radiosondes remains under 0.01/km(3), the uplink CP approaches 26%, 39%, and 50% in three patterns.

Satellites equipped with computing capabilities play a crucial role as access platforms for 5G and NextG (beyond 5G) non-terrestrial networks (NTNs). They facilitate the continuous execution of resource-intensive edge-assisted deep learning (DL) tasks offloaded from user equipment (UEs) in remote areas. To manage this effectively, satellite access network (SAN) resources must be carefully “sliced”, taking into account both the limited energy availability and the inherent scarcity of SAN resources. Existing SAN slicing approaches tend to use semantic communications for UE data transmission but overlook the impact of varying channel qualities between UEs and satellites. A cyclic dependency between the inter-slice and intra-slice resource schedulers makes it challenging to incorporate channel awareness at both levels. Armed with this insight, in this paper, we propose channel-aware semantic SAN (CASemSAN), a semantic SAN slicing algorithm that considers the channel conditions for NextG AI-native NTNs. It not only compresses tasks' data according to their semantics but also exploits the channel conditions of a SAN to support more tasks while still minimizing overall energy consumption. After analyzing the characteristics of this optimization problem, we propose an online greedy CASemSAN slicing algorithm to approximate its optimal solution. Extensive experiments verify the effectiveness of CASemSAN in energy saving and its ability to support a substantial number of tasks, compared with other baselines.

Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and communication tasks quickly, robustly, safely, and cost-efficiently via effective situation awareness and cooperation among UAVs. To achieve the promising benefits, the crucial IUAV networking issue should be tackled. This article argues that I-UAV networking can be classified into two categories: quality-of-service (QoS) driven networking and quality-of-experience (QoE) driven networking. Each category of networking poses emerging challenges that have severe effects on the safe and efficient accomplishment of I-UAV missions. This article elaborately analyzes these challenges and expounds on the corresponding intelligent approaches to tackle the I-UAV networking issue. Besides, considering the uplifting effect of extending the scalability of I-UAV networks through cooperating with high altitude platforms (HAPs), this article gives an overview of the integrated I-UAV and HAP network and presents the corresponding networking challenges and intelligent approaches.

This article is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers' QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Finally, we design extensive simulations to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 66.75% more energy-efficient than benchmarks.

Unmanned Aerial Vehicles (UAVs)-assisted networks play a pivotal role in both terrestrial base stations (BSs) and non-terrestrial networks (NTNs) due to their extensive coverage and collaborative decision-making capabilities. However, the presence of diverse node types, rapidly evolving requirements, and dynamic channel conditions poses substantial challenges for ground users (GUs) access, particularly in an unknown environment within BS-UAV-NTN integrated networks. To tackle these challenges, this paper introduces a novel approach-a deep Q-learning network (DQN)-based algorithm for UAVs deployment and an adaptive and load balancing (ALB) scheme for GUs access. This paper formulates the GUs access problem in BS-UAV-NTN networks as a maximization problem, transforming it into a Markov Decision Process (MDP) problem for UAVs deployment in unknown environment.The proposed solution includes a DQN-based UAVs deployment algorithm and an access scheme that prioritizes BSs and UAVs. Simulation results convincingly show that this access scheme outperforms traditional Q-learning and random schemes in terms of rewards and the number of accessed GUs.

This paper investigates the three-dimensional (3D) downlink sparse channel estimation for tethered aerial platform-enabled multi-user communication systems operating in a frequency division duplexing mode with large-scale antenna arrays. To this end, we design a non-identical Bernoulli-Gaussian distribution-based channel model that reflects the potential common sparsity caused by distant scatterers in a low-altitude environment. A low-complexity channel estimation algorithm without the need for prior knowledge of channel sparsity is proposed. It first applies a greedy pursuit algorithm to roughly estimate the common support set (CSS). Given the initial CSS, multi-user channels are then iteratively estimated and refined using a novel neighborhood-based channel estimation refinement scheme, which includes a decentralized sparse channel estimator to recover sparse channels accurately under non-i.i.d channel sparsity priors with low computation burden. Simulation results indicate that the proposed algorithm outperforms its existing counterparts and approaches the performance limit with perfect knowledge of the CSS.

Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for theoretical analysis of video coding and optimization of video transmission quality, is under-studied. The designed R-D model is desired to be simple, accurate, and generic owing to the limited capability of the UAV. Observing the key role of transformed residuals in the R-D model, this paper elaborately discusses the modeling of the transformed residual distribution and proposes an estimation algorithm to estimate the statistical parameter of the distribution. Specifically, considering the stringent requirements for low complexity and high generalization capability, we first design a linear parameter estimator by mining and analyzing UAV video statistical characteristics. Further, we develop a bias update scheme to improve the accuracy of the estimator. Test results on multiple real video sequences taken by UAVs show that the proposed estimation algorithm is more accurate than benchmarks.

This paper is concerned with the issue of improving video subscribers’ quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers’ QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers’ QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 45.69% more energy-efficient than benchmarks.

Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure slicing systems for bursty URLLC service provision due to its stringent requirements on low packet blocking probability and low codeword decoding error probability. This chapter proposes to orchestrate network resources for a slicing system to guarantee more reliable bursty URLLC transmission. This chapter re-cuts physical resource blocks and derives the minimum upper bound of bandwidth for URLLC transmission with a low packet blocking probability. This chapter also correlates coordinated multipoint beamforming with channel uses and derives the minimum upper bound of channel uses for URLLC transmission with a low codeword decoding error probability. Considering the agreement on converging diverse services onto shared infrastructures, this chapter further investigates the network slicing for URLLC and eMBB service multiplexing. Particularly, it formulates the service multiplexing as an optimization problem, which is challenging to be mitigated due to requirements of future channel information and of tackling a two timescale issue. To address the challenges, it develops a resource optimization algorithm based on a sample average approximate technique and a distributed optimization method with provable performance guarantees.

随着无人机软硬件技术的发展，多无人机集群自组织形成的无人机自组网（Flying Ad-Hoc networks,FANETs）受到了越来越多的来自学术界和工业界的关注，其灵活的部署和快速的反应能力使其能高效地完成多种多样的任务。而无人机自组网路由协议是提高服务质量（Quality of service,QoS）最重要的方法之一，但无人机自组网的移动性和动态性给路由协议的设计带来了严峻的挑战。传统的移动路由协议不能很好地满足无人机自组网的路由需求，因此研究者们从基于拓扑、地理和分层的角度提出了各式各样的无人机自组网路由协议，旨在克服移动性和提高网络的服务质量，并指出未来无人机自组网的路由协议可以考虑机会路由、软件定义网络（Software defined network,SDN）决策和预测驱动决策等综合提高QoS。本文主要针对无人机自组网网络特征，从不同的路由方法出发，SDN对路由协议进行总结和归纳，并对未来的研究方向进行了展望。

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.

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.

A state-of-the-art commercial drones have typically battery limitations to support their operation. Effective battery management is one of the main enablers of practical drone system operations. This paper designs an energy-efficient monitoring drone-based scheduling algorithm to control the number of activation drones in a multi-drone system. This study focuses on reducing overlapping regions of the inter-drone monitoring area in terms of energy efficiency. We formulate this problem as finding a maximum weight independent set (MWIS) problem on a graph model and propose a real-time update of the message algorithm based on belief propagation. Our results showed that the proposed algorithm achieves significant improvements in energy consumption compared to the existing algorithm.

The invention provides a method for designing a joint routing protocol of an unmanned aerial vehicle communication network, and belongs to the technical field of designing the routing protocol of the unmanned aerial vehicle communication network. The method comprises the following steps: firstly constructing a lightweight unmanned aerial vehicle forward queue model that is low in maintenance cost and is easy to achieve; then based on the model, separately extracting an instant queue power concept and an instant transmission power concept, and designing a joint routing decision in a sum form; designing a new data packet header, adding unmanned aerial vehicle information to the data packet header, opening the monitoring mode of the unmanned aerial vehicle, and passively discovering a neighbor unmanned aerial vehicle; and finally selecting a proper unmanned aerial vehicle to serve as the next hop according to the maximum routing decision variable, and transmitting data packets to the next hop unmanned aerial vehicle. The joint routing protocol of the unmanned aerial vehicle communication network has a smaller node queue maintenance cost and a lower neighbor unmanned aerial vehicle discovery cost, and efficient communication access services can be provided for short time bursty traffic on the ground.

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.