Joint Resource, Deployment, and Caching Optimization for AR Applications in Dynamic UAV NOMA Networks

The cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA) are investigated in this paper. The delivery of multi-media contents for the mixed augmented reality (AR) and normal multi-media application is assisted by multiple mobile UAV base stations, which cache popular contents for wireless backhaul link traffic offloading. To cope with the dynamic content requests and mobility of users in practical scenarios, the dynamic optimization problem for user association, caching placement of UAVs, real-time deployment of UAVs, and power allocation of NOMA is modeled as a stackelberg game to minimize the long-term content delivery delay. Specifically, the game is decomposed into a leader level problem and a number of follower level problems. A correction mechanism is added in deep reinforcement learning (DRL) to optimize the user association in leader level. A meta actor network is proposed in DRL to jointly optimize the UAVs caching placement, real-time UAVs deployment and power allocation of NOMA in follower level. Then, a dynamic caching placement and resource allocation algorithm based on multi-agent meta deep reinforcement learning is proposed to minimize the long-term content delivery delay. Finally, we demonstrate that the considerable gains are achieved by the proposed algorithm.

The cache-enabling unmanned aerial vehicle (UAV) cellular network is investigated in this article, where the massive access capability is enhanced by applying non-orthogonal multiple access (NOMA). More particularly, a mobile UAV base station, which caches the popular contents to release the pressure on wireless backhaul links, is deployed to assist the delivery of large volume multimedia contents for ground users. The dynamic UAV cellular network with the dynamic UAV locations and content requests in practical scenario is considered in this paper. A long-term caching placement and content delivery joint optimization problem for content delivery delay minimization is formulated as a Markov decision process (MDP) to cope with the dynamic environment. A deep reinforcement learning (DRL) based caching placement and content delivery algorithm is proposed to tackle the MDP with large action space. Finally, it is demonstrated by the numerical results that: 1) a low content delivery delay is achieved by the studied cache-enabling UAV NOMA networks; 2) a good performance is provided by the proposed algorithm.

Unmanned aerial vehicles (UAVs) are being integrated as an active element in 5G and beyond networks. Because of their flexibility and mobility, UAV base stations (UAV-BSs) can be deployed according to the ground user distributions and their quality-of-service (QoS) requirement. Although there has been quite some prior research on the UAV deployment, no work has studied this problem in a 3-D setting and taken into account the UAV-BS capacity limit and the QoS requirements of ground users. Therefore, in this article, we focus on the problem of deploying UAV-BSs to provide satisfactory wireless communication services, with the aim to maximize the total number of covered user equipment subject to user data-rate requirements and UAV-BSs' capacity limit. First, we model the relationship between the air-to-ground path loss (PL) and the location of UAV-BSs in both horizontal and vertical dimensions, which has not been considered in previous works. Unlike the conventional UAV deployment problem formulation, the 3-D deployment problem is decoupled into a 2-D horizontal placement and altitude determination connected by PL requirement and minimization. Then, we propose a novel genetic algorithm-based 2-D placement approach in which UAV-BSs are placed to have maximum coverage of the users with consideration of data rate distribution. Finally, numerical and simulation results show that the proposed approach has enabled a better coverage percentage comparing with other schemes.

This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA). The delivery of a large volume of multimedia contents for ground users is assisted by a mobile UAV base station, which caches some popular contents for wireless backhaul link traffic offloading. In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance. To cope with the dynamic UAV locations and content requests in practical scenarios, we formulate the long-term caching placement and resource allocation optimization problem for content delivery delay minimization as a Markov decision process (MDP). The UAV acts as an agent to take actions for caching placement and resource allocation, which includes the user scheduling of content requests and the power allocation of NOMA users. In order to tackle the MDP, we propose a Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with \emph{soft ${\varepsilon}$-greedy} strategy to search for the optimal match between actions and states. Since the action-state table size of Q-learning grows with the number of states in the dynamic networks, we propose a function approximation based algorithm with combination of stochastic gradient descent and deep neural networks, which is suitable for large-scale networks. Finally, the numerical results show that the proposed algorithms provide considerable performance compared to benchmark algorithms, and obtain a trade-off between network performance and calculation complexity.

In this paper, the problem of unmanned aerial vehicles (UAV) deployment is investigated for visible light communication (VLC)-enabled UAV networks. Here, UAVs can simul-taneously provide communications and illumination services to ground users. In this model, ambient illumination distribution of the service area must be considered since it can cause interference over the VLC link and affects the illumination requirements of users. This problem is formulated as an optimization problem, which jointly considers UAV deployment, user association, power efficiency, and predictions of the illumination distribution. To solve this problem, we first need to predict illumination distribution to proactively determine the UAV deployment and user association so as to minimize total transmission power of UAVs. To predict the illumination distribution of the entire service area, a federated learning framework based on the machine learning algorithm of convolutional auto-encoder (CAE) is proposed. Compared to the centralized machine learning algorithms that requires complete illumination data for centralized training, the proposed algorithm enables the UAVs to train their local CAE with partial illumination data and cooperatively build a global CAE model that can predict the entire illumination distribution. Using these predictions, the optimal UAV deployment and user association policy that minimizes the total transmission power of UAVs is determined. Simulation results demonstrate that the proposed approach reduces the transmission power of UAVs up to 14.8% and 25.1%, respectively, compared to the local CAE prediction models and the conventional optimal algorithm without illumination distribution predictions.

In this paper, we study the application of unmanned aerial vehicle (UAV) base stations (BSs) in order to improve the cellular network capacity. We consider flying BSs where BS equipments are mounted on UAVs, making it possible to move BSs freely in space. We study the optimization of UAVs' trajectory in a network with mobile users to improve the system throughput. We consider practical two-hop communications, i.e., the access link between a user and the UAV BS, and the backhaul link between the UAV BS and a macrocell BS plugged into the core network. We propose a reinforcement learning based algorithm to control the UAVs' mobility. Additionally, the proposed algorithm is subject to physical constraints of UAV mobility. Simulation results show that considering both the backhaul and access links in the UAV mobility optimization is highly effective in improving the system performance than only focusing on the access link.

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory, with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained to cancel the interference from the BS to the UAV-served user. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

In this paper, an unmanned aerial vehicle (UAV)-enabledspace-air-ground integrated relay system, in which UAV is equipped with phased-array antennas to receive satellite’s signal while utilizing non-orthogonal multiple access (NOMA) schemes to transmit data to land-based users, is proposed. For UAV signal collection, a detection-vector optimization problem can be addressed by introducing a serial of transformations into a second-order cone programming problem. Furthermore, the UAV-ground NOMA communication is summarized into a max-min problem in terms of UAV’s energy efficiency (EE), which is a fractional mixed-integer non-convex problem and has been widely recognized as hard work. Thus, a two-step solution is proposed. Firstly, we transfer the max-min fractional problem into a subtractive one. After that, the transformed problem is further divided into three sub-problems, which are used for jointly performing UAV trajectory optimization, NOMA scheduling optimization, and NOMA power allocation. In addition, the non-convexity of the above-mentioned problem is well solved by using the proposed iterative algorithm, achieving a rapid convergence. Numerical results illustrate the superiority of the proposed EE-NOMA scheme, which achieves three times higher energy-efficient than the conventional spectrum efficiency scheme, and NOMA is shown to globally outperform the orthogonal multiple access in UAV EE circumstance.

Unmanned aerial vehicle (UAV) base stations play a significant role in many scenarios like disaster relief and terrestrial traffic offloading. To put UAV base stations into operation, deployment is a key issue to be addressed. Existing works do not take into account different user Quality-of-Service (QoS) requirements in the deployment of multiple unmanned aerial vehicles (UAVs). Therefore, in this paper, we propose a 3D deployment scheme which minimizes the number of UAVs to cover all users with different QoS requirements. First, we derive the relationship between the altitude and coverage of a UAV at different user QoS requirements. Then, we formulate the deployment problem on the basis of the relationship. Next, we propose a 3D UAV deployment algorithm which considers both altitude and horizontal location. Finally, numerical results show that the proposed algorithm can deploy less UAVs to achieve full coverage of all users.

The flexible deployment of Unmanned Aerial Vehicle (UAV) swarms holds significant potential for low-altitude economy, but their communication security is severely threatened by malicious jamming. Generally, existing anti-jamming methods often overlook multi-user interference in swarm scenarios and fail to exploit the full potential of Intelligent Reflecting Surface (IRS) architectures. To solve the above challenges, we propose for the first time an anti-jamming framework for UAV swarm communications assisted by a Hybrid-IRS-assisted UAV (H-UAV). We jointly optimize the H-UAV’s trajectory, the hybrid IRS’s beamforming and active/passive element allocation of IRSs, and Non-Orthogonal Multiple Access (NOMA) communication strategy under imperfect jammer Channel State Information (CSI), to maximize average system transmission rate while minimizing communication energy consumption. To handle the formulated highly-coupled non-convex problem, we decompose it into three sub-problems. Specifically, we employ Successive Convex Approximation (SCA) to optimize the H-UAV’s trajectories. The IRS beamforming and element allocation are then transformed into a semi-definite programming problem by a designed penalty-based approach. Finally, the NOMA decoding order and power allocation are optimized via a dynamic ordering scheme and an SCA-based algorithm. Compared to existing representative schemes, the proposed framework can achieve higher average transmission rates and lower energy consumption.

This paper investigates the efficient deployment and resource allocation of multiple unmanned aerial vehicles (UAVs) to be integrated with wireless terrestrial networks as aerial base stations (BSs) and serve ground user devices by utilizing non-orthogonal multiple access (NOMA) scheme. Aiming to optimize the energy efficiency (EE) of ground BSs (GBSs) and deployed UAVs, a constrained EE maximization problem is formulated. To solve the optimization problem, we decompose the problem into three subproblems. Firstly, GBS association and subchannel assignment subproblem is formulated and solved by applying matching theory. Then, UAV deployment and subchannel assignment subproblem is formulated. To solve the problem, we propose a clustering strategy based on K-means algorithm to divide users into approximately equal-size clusters. Then, a dynamic many-to-many matching algorithm is proposed to assign subchannels to the users. Finally, a non-convex power allocation subproblem is formulated and solved iteratively using a two-stage quadratic transform. The numerical results demonstrate the effectiveness of our proposed scheme.

In view of the scarce spectrum resources and inconvenient deployment in wireless communications, this work focuses on an unmanned aerial vehicle (UAV) enabled non-orthogonal multiple access (NOMA) system where a UAV deployed as a base station serves two users by NOMA. A joint power allocation and aerial jamming (PAAJ) scheme is proposed to achieve reliable and secure communications for the system in the presence of a malicious eavesdropper. To be specific, dynamic power allocation is adopted to ensure the reliability of the system and another friendly UAV jammer is introduced to improve the security of the system. The closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) are derived to evaluate the performance of the security-required user in the practical condition of probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) air-to-ground (A2G) channels. Moreover, the asymptotic EST is analyzed to obtain further insights. Numerical results show the validity of theoretical derivations and the superiority of the proposed PAAJ scheme over benchmark schemes. The EST of the UAV-enabled NOMA communication system outperforms the conventional terrestrial NOMA system at the low transmitting power from the UAV base station. There exists an optimal height of the UAV base station to maximize the EST, which provides a useful framework for designing UAV-enabled communication systems with heterogeneous service requirements.

In this paper, we investigate the integration of the Non-Orthogonal Multiple Access (NOMA) technology into the Unmanned Aerial Vehicle (UAV)-based wireless system. In particular, we study the joint NOMA power allocation, user pairing, and UAV deployment (placement) for this wireless system. To gain insight into the optimal structure of this problem, we derive the optimal power allocation and UAV placement to maximize the sum-rate of the two-user (one NOMA pair) network. We then address the general setting with multiple NOMA pairs where users must be paired into two-user groups using NOMA. For this setting, we optimize the user pairing, power allocation, and UAV placement to maximize the minimum sum rate for individual user pairs. Solving this optimization problem optimally requires exhaustive search over all possible pairing scenarios, which has very high complexity. To overcome this challenge, we propose a heuristic pairing algorithm based on the minimum sum-of-squared-distance criteria whose pairing result is then applied to perform optimal power allocation and UAV placement. Through numerical studies, we show the significance of UAV placement optimization and the fact that the proposed heuristic user pairing scheme achieves close-to-optimal performance.

Unmanned aerial vehicles (UAVs) are expected to be integrated into future wireless networks to offer services, especially in unreachable or congested areas. To improve the spectral efficiency, non-orthogonal multiple access (NOMA) scheme can be utilised within the UAV communication to allow more users to be covered and associated. The performance of the NOMA-UAVs network is governed by several factors including power allocation, user association and pairing methods. This paper presents an approach that uses multi-armed bandit (MAB) and two-sided matching frameworks to maximize the throughput of multi-UAV-assisted NOMA networks in a decentralized manner. The approach enables the UAVs to propose to the ground users (GUs) without explicit cooperation among the UAVs while the GUs can accept or reject the proposals. To this end, we propose a modified Thompson sampling algorithm that we named decaying epsilon Thompson sampling (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {D}\epsilon \text {TS}$ </tex-math></inline-formula>) MAB algorithm that is designed to improve the exploration-exploitation tradeoff in the MAB. The performance of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {D}\epsilon \text {TS}$ </tex-math></inline-formula> MAB algorithm is evaluated against other existing MAB techniques. Simulation results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {D}\epsilon \text {TS}$ </tex-math></inline-formula> algorithm attains faster convergence and improved performance in terms of smaller regret and increased achievable system throughput. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {D}\epsilon \text {TS}$ </tex-math></inline-formula> MAB algorithm particularly excels in regards of the convergence rate when the number of available action spaces increases.

An unmanned aerial vehicle (UAV) has been considered as a base station (BS) or a relay node in wireless communication systems due to its cost-effective and fast deployment. UAV BSs (UBSs) can provide a wireless connectivity to user equipments (UEs) within a given coverage, while UAV relay systems (URSs) can extend a service coverage of BS to provide a reliable communication link to an isolated UE (IU). The deployment of UAV(s) impacts greatly on the performance of UBS and URS. In this paper, a novel UAV deployment algorithm for multi-layer URS is proposed to maximize an average data rate of UEs while guaranteeing a seamless communication service to IU. The proposed algorithm utilizes the minimum number of UAVs and derives the minimum number of transmission time slots to improve an average data rate. In addition, a single-layer URS is considered as a special case of multi-layer URS, and its deployment algorithm is introduced. The performance comparison between multi-layer and single-layer URSs is presented in numerical results based on the position of IU.