Ground Users Research Articles

Secure Transmission for RIS-Assisted Downlink Hybrid FSO/RF SAGIN: Sum Secrecy Rate Maximization

This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted downlink hybrid free-space optics (FSO)/radio frequency (RF) space–air–ground integrated network (SAGIN) architecture, where the high altitude platform (HAP) converts the optical signal sent by the satellite into an electrical signal through optoelectronic conversion. The drone equipped with RIS dynamically adjusts the signal path to serve ground users, thereby addressing communication challenges caused by RF link blockages from clouds or buildings. To improve the security performance of SAGIN, this paper maximizes the sum secrecy rate (SSR) by optimizing the power allocation, RIS phase shift, and drone trajectory. Then, an alternating iterative framework is proposed for a joint solution using the simulated annealing algorithm, semi-definite programming, and the designed deep deterministic policy gradient (DDPG) algorithm. The simulation results show that the proposed scheme can significantly enhance security performance. Specifically, compared with the NOMA and SDMA schemes, the SSR of the proposed scheme is increased by 39.7% and 286.7%, respectively.

Resource Optimization of Airborne Base Stations Using Artificial Intelligence Methods

In remote areas and disaster-stricken regions, unmanned aerial vehicles (UAVs) can serve as base stations, providing wireless communication to ground users. Due to their high mobility, low cost, and rapid deployment and retrieval capabilities, UAVs can continuously adjust their position in three-dimensional (3D) space, improving wireless connectivity and enhancing data transmission rates. In this paper, we investigate the problem of ABS (Aerial Base Station) deployment in 3D space and power allocation with the aim of maximizing the data transmission rate in the system. To address this non-convex problem, we propose Q-learning, a reinforcement learning algorithm. By using the ABS as an agent, the algorithm enables the ABS to explore the state space and take actions based on an ϵ-greedy policy (optimal epsilon value) to determine its 3D position and power allocation. Simulation results demonstrate that the proposed algorithm outperforms individual position optimization and power allocation optimization.The purpose of this article is to study the efficiency of using modern artificial intelligence methods to optimize the use of resources of airborne base stations of public communication networks.The essence of the proposed solution is to use modern artificial intelligence methods, namely: the Q-learning method and the epsilon-greedy ϵ-greedy algorithm to ensure joint optimization of the placement of airborne base stations and power distribution to maximize the data transfer rate. The system has an implementation in the form of a simulation program. Simulation experiments have shown that the use of the Q-learning reinforcement learning method and the epsilon-greedy e-greedy algorithm for joint optimization provides a higher overall data transfer rate in the system compared to optimizing only the location or power distribution.The scientific novelty of the proposed solution is that joint optimization of the placement of an airborne base station and power distribution made it possible, in contrast to known results, to establish that the flight altitude of a UAV with a base station installed on it when optimizing only the location will be higher than the flight altitude of a UAV when jointly optimizing the location and power distribution.The practical significance is the possibility of developing a methodology for planning public communication networks using airborne base stations to obtain a higher overall data transfer rate on the corresponding network fragment.

Variations in Multi-Agent Actor–Critic Frameworks for Joint Optimizations in UAV Swarm Networks: Recent Evolution, Challenges, and Directions

Autonomous unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can efficiently perform surveillance, connectivity, computing, and energy transfer services for ground users (GUs). These missions require trajectory planning, UAV-GUs association, task offloading, next-hop selection, and resource allocation, including transmit power, bandwidth, timeslots, caching, and computing resources, to enhance network performance. Owing to the highly dynamic topology, limited resources, stringent quality of service requirements, and lack of global knowledge, optimizing network performance in UAVSNs is very intricate. To address this, an adaptive joint optimization framework is required to handle both discrete and continuous decision variables, ensuring optimal performance under various dynamic constraints. A multi-agent deep reinforcement learning-based adaptive actor–critic framework offers an effective solution by leveraging its ability to extract hidden features through agent interactions, generate hybrid actions under uncertainty, and adaptively learn with scalable generalization in dynamic conditions. This paper explores the recent evolutions of actor–critic frameworks to deal with joint optimization problems in UAVSNs by proposing a novel taxonomy based on the modifications in the internal actor–critic neural network structure. Additionally, key open research challenges are identified, and potential solutions are suggested as directions for future research in UAVSNs.

Towards Optimizing the Downlink Transmit Power in UAV-Integrated IRS Wireless Systems

Air-to-ground interference poses a critical challenge in integrating unmanned aerial vehicles (UAVs) into cellular networks. In downlink scenarios, UAVs can withstand significant interference from co-channel base stations (BSs) due to the guaranteed line-of-sight (LoS) connection with ground users. Our research focuses on power optimization in BSs and applying green energy principles to pave the way for more sustainable and energy-efficient BSs within UAV-integrated wireless systems. To this end, this paper investigates a downlink UAV communication scenario in which the intelligent reflecting surface (IRS) is mounted on the UAV to practically nullify the interference originating from the co-channel BSs. We formulate the IRS beamforming matrix to reduce transmit power by optimizing passive beamforming for IRS elements, incorporating adjustments to phase shifts and amplitude coefficients while considering the positioning of the UAV. The proposed optimization problem is non-convex, and thus a successive convex approximation (SCA) method is adopted to convert all constraints to a quadratic approximation. Simulation results demonstrate that the proposed SCA algorithm provides an efficient transmit power minimization approach with low computational complexity for large IRS since it achieves close-to-optimal performance, and significantly outperforms conventional systems without IRS. In interference scenarios and with different numbers of IRS meta-atoms, the proposed algorithm achieves a power reduction of approximately 8 and 13 dBm, while maintaining the same required signal-to-interference-plus-noise ratio.

Performance Analysis of Parallel Free-Space Optical/Radio Frequency Transmissions in Satellite–Aerial–Ground Integrated Network with Power Allocation

Satellite–aerial–ground integrated networks (SAGINs) combined with hybrid free-space optical (FSO) and radio frequency (RF) transmissions have shown great potential in improving service throughput and reliability. The coverage mismatch and rate limitation of traditional hybrid FSO/RF design have restricted its development. In this paper, we investigate the performance of parallel FSO/RF transmissions in SAGIN, taking into account the effect of weather conditions and the quality of service (QoS) of ground users. A three-hop relay system is proposed, where the FSO and RF links jointly provide services to ground users in remote areas. Specifically, considering the limited transmit power of the relay node, we have studied the optimal power allocation between parallel FSO and RF links to further improve system energy efficiency. The performance of the proposed system is evaluated in terms of capacity outage probability, weighted average bit-error rate (BER), and energy efficiency. Moreover, asymptotic capacity outage probability is also derived to obtain more engineering insights. Finally, numerical results show that the energy efficiency of the proposed parallel scheme improves by 30.9% compared to only the FSO scheme at a total transmit power of 15 dBW.

Performance evaluation for Q-learning based anycast routing protocol in unmanned aerial vehicle networks with multiple base stations

Performance evaluation for Q-learning based anycast routing protocol in unmanned aerial vehicle networks with multiple base stations

Connectivity-Enhanced 3D Deployment Algorithm for Multiple UAVs in Space–Air–Ground Integrated Network

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network.

Multi-UAV trajectory planning for RIS-assisted SWIPT system under connectivity preservation

Multi-UAV trajectory planning for RIS-assisted SWIPT system under connectivity preservation

Resource Allocation in UAV-D2D Networks: A Scalable Heterogeneous Multi-Agent Deep Reinforcement Learning Approach

In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system decisions and serves as an effective and practical alternative. However, its application in large-scale dynamic environments is severely limited by the curse of dimensionality and communication overhead. To resolve this problem, we develop a scalable heterogeneous multi-agent mean-field actor-critic (SH-MAMFAC) framework. The framework treats ground users (GUs) and UAVs as distinct agents and designs cooperative rewards to convert the resource allocation problem into a fully cooperative game, enhancing global network performance. We also implement a mixed-action mapping strategy to handle discrete and continuous action spaces. A mean-field MADRL framework is introduced to minimize individual agent training loads while enhancing total cache hit probability (CHP). The simulation results show that our algorithm improves CHP and reduces transmission delay. A comparative analysis with existing mainstream deep reinforcement learning (DRL) algorithms shows that SH-MAMFAC significantly reduces training time and maintains high CHP as GU count grows. Additionally, by comparing with SH-MAMFAC variants that do not include trajectory optimization or power control, the proposed joint design scheme significantly reduces transmission delay.

Wireless routeing scheme for UAV-assisted access networks based on the link path loss

ABSTRACT In this work, a group of unmanned aerial vehicles (UAVs) is aimed to function as relay nodes that provide a multi-hop wireless link between ground users. The communication route between the sender and receiver is established with awareness of the link path loss between the cooperating UAV relay nodes. Then, Dijkstra’s shortest path algorithm is used to find the optimal route that has the minimum total path loss among all other possible routes. MATLAB simulation results show a high-performance gain when using Dijkstra’s algorithm for establishing the communication route compared with the baseline approach in which the route is formed based on the shortest-distance path through the UAV relay nodes. It is worth mentioning that both shadow fading and nonfading radio environments were taken into account. Moreover, the performance is further improved when the standard deviation of shadow fading increases. The work also investigates the probability of selecting each of the UAV nodes in forming the multi-hop link considering the total simulated realizations. Here, it is shown that more variability is observed in the indexes of the traversed UAV nodes when routeing through a random fading environment compared to a nonfading environment.

Research on path planning in UAV-assisted emergency communication

The rapid development of UAV communication technology makes it have application potential in wireless systems. However, for the optimization problem of UAV base station providing communication for ground personnel in the disaster area under special natural disasters, traditional deep reinforcement learning is used. Algorithms cannot solve such problems well. This article proposes the AD3QN algorithm combined with the attention mechanism, which can communicate with disaster victims on the ground better and more quickly, providing better information collection for rescue missions and completing the task of communication optimization. In the mission simulation environment, in order to make the environment more realistic, we innovatively designed the ground user distribution model, air-to-ground communication model, and electromagnetic interference model. Finally, the effect of the proposed algorithm is evaluated by comparing different deep reinforcement learning algorithms. The results show that the algorithm we proposed can provide communication services faster and has higher practicability in terms of algorithm.

NPO-BAND: Number and placement optimization for backhaul-aware UAV network deployment

NPO-BAND: Number and placement optimization for backhaul-aware UAV network deployment

A Three-Dimensional Time-Varying Channel Model for THz UAV-Based Dual-Mobility Channels.

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV's agility and mobility, ground user's mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems.

Inter-beam handover schemes for LEO satellites in 5G satellite–terrestrial integrated networks

Inter-beam handover schemes for LEO satellites in 5G satellite–terrestrial integrated networks

A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services

IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area’s base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services.

A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites.

Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.

Utilizing UAVs in Wireless Networks: Advantages, Challenges, Objectives, and Solution Methods

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance the performance and functionality of mobile networks. UAVs can act as flying base stations, relays, or users to provide wireless services to ground users or devices. However, the optimal placement and trajectory design of UAVs in mobile networks is a challenging problem, as it involves multiple objectives, constraints, and uncertainties. In this paper, we provide a comprehensive survey of the state-of-the-art research on UAV placement and trajectory optimization in cellular networks. We first introduce the main objectives and challenges of UAV placement and trajectory optimization, such as maximizing coverage, throughput, energy efficiency, or reliability, while minimizing interference, delay, or cost. We also examine the primary models and assumptions employed for UAV placement and trajectory optimization, including channel models, mobility models, network architectures, and constraints. Additionally, we discuss the main methods and algorithms employed for UAV placement and trajectory optimization. These include optimization techniques, heuristic algorithms, machine learning approaches, and distributed solutions. Analytical results, numerical simulations, or experimental tests are further discussed as the main performance metrics and evaluation methods used for UAV placement and trajectory optimization. We also highlight the main applications and scenarios of UAV placement and trajectory optimization, such as cellular offloading, emergency communications, or aerial base stations. Finally, we identify some open problems and future research directions on UAV placement and trajectory optimization in cellular networks.

Capacity and delay performance analysis for large-scale UAV-enabled wireless networks

Capacity and delay performance analysis for large-scale UAV-enabled wireless networks

UAV Position Optimization for Servicing Ground Users Based on Deep Reinforcement Learning

UAV Position Optimization for Servicing Ground Users Based on Deep Reinforcement Learning

A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning

In the air-to-ground transmissions, the lifespan of the network is based on the “unmanned aerial vehicle's (UAV)” life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate’s minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the “Internet of Things (IoT)” systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.