Unmanned Aerial Vehicles (UAVs) are an indispensable core component of low altitude economic networks. It is very critical for achieving efficient UAV image transmission of air-to-ground communication. Due to the changes in flight area and unstable channel conditions, the signal-to-noise and transmission rate change rapidly. To adapt to these changes, we propose a channel adaptive encoding and decoding method for UAV image transmission. The proposed method includes a lightweight feature extraction module, a channel-wise feature enhance module, a transmission rate adaptive module, and the corresponding decoding module. The lightweight feature extraction module can quickly extract local detailed features and long-range spatial dependencies via residual block and mobile mamba. The channel-wise feature enhance module can enhance channel wise useful features via the involution operation and the SNR adjustment block according to channel state SNRs. The transmission rate adaptive module can further adaptively adjust the size of transmission features according to the transmission rate via the rate adjustment block and the rate mask block. The extensive experimental results on the SIRI-WHU, WHU-RS19, AID, and UCMerced Land Use datasets demonstrate that our method obtains higher PSNR, MS-SSMI, LPIPS and ACC than state-of-the-art methods.

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in wireless communication, yet accurate channel loss prediction remains a significant challenge, limiting resource optimization performance. This paper proposes a novel AIGC (Artificial Intelligence Generated Content)-driven framework that revolutionizes UAV communication through three key innovations: data augmentation, channel prediction, and trajectory optimization. First, aWasserstein Generative Adversarial Network (WGAN) is employed to generate high-quality synthetic channel data, addressing the time-consuming nature of data collection. Second, this augmented dataset trains a knowledge-driven Channel Knowledge Map (CKM) that achieves superior prediction accuracy by incorporating domain expertise. Finally, the enhanced CKM guides a reinforcement learning algorithm to generate optimal UAV trajectories. Experimental results demonstrate that our AIGC-powered approach significantly decreases train loss by 30.23% and reduces flight time from 81s to 37s compared to traditional methods, marking a substantial advancement in UAV-enabled wireless communications.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

When uncrewed aerial vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by positioning errors, we propose a compensation framework based on channel knowledge map (CKM), a site-specific database that stores and manages channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a proximal policy optimization-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design.

Traditional self-interference cancellation (SIC) methods are common in full-duplex (FD) integrated sensing and communication (ISAC) systems. However, exploring new SIC schemes is important due to the limitations of traditional approaches. With the challenging limitations of traditional SIC approaches, this paper proposes a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled FD ISAC system, where STAR-RIS enhances simultaneous communication and target sensing and reduces self-interference (SI) to a level comparable to traditional SIC approaches. The optimization of maximizing the sensing signal-to-interference-plus-noise ratio (SINR) and the communication sum rate, both crucial for improving sensing accuracy and overall communication performance, presents significant challenges due to the non-convex nature of these problems. Therefore, we develop alternating optimization algorithms to iteratively tackle these problems. Specifically, we devise the semi-definite relaxation (SDR)-based algorithm for transmit beamformer design. For the reflecting and refracting coefficients design, we adopt the successive convex approximation (SCA) method and implement the SDR-based algorithm to tackle the quartic and quadratic constraints. Simulation results validate the effectiveness of the proposed algorithms and show that the proposed deployment can achieve better performance than that of the benchmark using the traditional SIC approach without STAR-RIS deployment.

In this paper, we apply the channel state in-formation(CSI) provided by an online Channel Knowledge Map(CKM), which is dynamically environment-aware, to assist the UAV in calculating the propagation loss with the ground users. We aim to jointly design the U AV trajectory, user association, and power allocation to achieve a minimum flight time under the constraints of communication demands in an emergency scenario. We formulate the problem as a Markov process, use a Proximal Policy Optimization(PPO) method to find waypoints, and conduct experiments on positioning errors to discuss the accuracy requirements for CKM. Besides, acknowledging the physical world smoothness of UAVs, we introduce a two-stage training method, named Two-Stage Training PPO (TST-PPO), to update parameters tuning for a Bezier curve, aiming for a reduced completion time. Our simulation results demonstrate that our algorithm outperforms the original PPO both in optimality and convergence, and could design a smooth traj ectory for the U A V to fly fluently.

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

In this article, we aim to minimize the total power of all the Internet of Things Devices (IoTDs) by jointly optimizing user association, computational capacity, transmit power, and the location of unmanned aerial vehicles (UAVs) in an UAV-assisted cloud radio access network (C-RAN). In order to solve this nonconvex problem, we propose an effective algorithm by solving four subproblems iteratively. For the user association and the computational capacity subproblems, the nonconvex constraints are relaxed and the optimal solutions are obtained. For the transmit power control and the location planning subproblems, the successive convex approximation (SCA) technique is used to transform the nonconvex constraints into convex ones. Moreover, to obtain the suboptimal solutions, slack variables are also introduced to deal with the feasibility-check problems. The simulation results demonstrate that the proposed algorithm can greatly reduce the total power consumption of IoTDs.

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity.

Wireless-powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this article, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station, and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

In this paper, we study spectrum sharing of drone small cells (DSCs) network modeled by the 3-D Poisson point process. This paper also investigates an underlay spectrum sharing between the 3-D DSCs network and traditional cellular networks modeled by 2-D Poisson point processes. We take advantage of the tractability of the Poisson point process to derive the explicit expressions for the DSCs coverage probability and achievable throughput. To maximize the DSCs network throughput while satisfying the cellular network efficiency constraint, we find the optimal density of DSCs aerial base stations. Furthermore, we explore the scaling behavior of the optimal DSCs density with respect to the DSCs outage probability constraint under different heights of DSCs. Our analytical and numerical results show that the maximum throughput of the DSCs user increases almost linearly with the increase of the DSCs outage constraint. In order to protect the cellular user, the throughput of the DSCs user stops increasing when it meets the cellular network efficiency loss constraint. To further protect the cellular network in the spectrum underlay, we investigate the effect of primary exclusive regions (PERs) in a 3-D space. Unlike the circular PER in traditional cellular spectrum sharing in the 2-D space, the shape of the 3-D PER is found as a half sphere or a half sphere segment, depending on the radius of PER and the DSCs height limit. We show that the radius of PER should be restricted for small DSCs constraints and limited DSCs height.

In this paper, we study drone small cells (DSCs) network modeled by 3D Poisson point process. We further study an underlay spectrum sharing between the 3D DSCs network and traditional cellular networks modeled by 2D Poisson Point Processes. We derive the explicit expressions for the downlink DSCs coverage probability and achievable throughput.We obtain the optimal density of DSCs aerial base stations that maximizes the DSCs network throughput while satisfying the cellular network efficiency constraint. Our analytical and numerical results show that the maximized throughput of the DSCs user increases almost linearly with the increase of the DSCs outage constraint. However, to protect the cellular user, the potential throughput of the DSCs user should stop increasing when it encounters the cellular network efficiency loss constraint.