UAV-assisted NOMA Downlink Communications Based on Content Caching

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.

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.

In this paper, an unmanned aerial vehicle (UAV) using hybrid orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) solutions aided by multiple input multiple output (MIMO) technology is proposed to provide wireless communication for ground users (GUs). The proposed OMA-NOMA-MIMO system aims to improve throughput and spectrum efficiency. Additionally, it also strives to maximize the sum rate while achieving good user fairness. UAVs are considered as base stations (BSs) that provide services to users in multiple real-life scenarios, e.g. during natural disasters. They also enable aerial surveillance and help establish BSs during mass events. A pairing algorithm is proposed for far-near NOMA users with an optimized power allocation (PA) mechanism to improve the performance of NOMA-UAV-BS. Channels between UAV-BS and GU are established as being of the line-of-sight (LoS) and non-line-of-sight (NLoS) varieties, taking into consideration the angle of departure (AoD) and the angle of arrival (AoA). The results obtained demonstrate that NOMA performs best in specific scenarios, while OMA overcomes NOMA in others. The outcomes of the project may be utilized to control the transmission performed by the UAV-BS by serving the GUs depending on the required quality of service.

To enhance the service quality of the unmanned aerial vehicle (UAV), the UAV-aided Internet of Things (IoT) systems could deploy a Deep Neural Network (DNN) for performance prediction for the users. Non-orthogonal multiple access (NOMA) is applied to such networks in order to improve spectrum efficiency, and results in improved quality of service at the ground users under the mobility of UAV. The outage and ergodic capacity requirements of the IoT users may not be satisfied due to some imperfect system parameters such as hardware noise. A DNN-based algorithm for performance prediction and the design of multiple antennas at the UAV under hardware noise is proposed. In this DNN-based UAV-NOMA, the central controller (server) collects system parameters periodically based on observing the state of IoT system and performs adjustments to the dynamic environment. The closed-form expressions for the outage probability and the ergodic capacity are derived to evaluate the performance of a group of IoT users. Our numerical results demonstrate that: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i) In contrast to the traditional UAV-NOMA system, the UAV cannot know the performance at each IoT user in order to adjust the parameters (i.e. power allocation factors) before transmitting the signals to the devices; while the proposed DNN-based IoT system is capable of predicting the performance; ii) The performance of the IoT users can be significantly improved by integrating more antennas at the UAV and limiting levels of hardware noise; iii) By designing NOMA, the UAV-NOMA-based IoT system can increase the throughput to the tune of 40% compared with the benchmark (the orthogonal multiple access (OMA)-based IoT)</i> .

Unmanned aerial vehicles (UAVs) are expected to be an important new class of users in the fifth generation (5G) and beyond 5G cellular networks. In particular, there are emerging UAV applications such as aerial photograph and data relaying that require high-speed communications between the UAVs and the ground base stations (GBSs). Due to the high UAV altitude, the strong line-of-sight (LoS) links generally dominate the channels between the UAVs and GBSs, which brings both opportunities and challenges in the design of future wireless networks supporting both terrestrial and aerial users. Although each UAV can associate with more GBSs for communication as compared to terrestrial users thanks to the LoS-dominant channels, it also causes/suffers more severe interference to/from the terrestrial communications in the uplink/downlink. This paper studies the uplink communication from a multi-antenna UAV to a set of GBSs within its signal coverage by considering a practical yet challenging scenario when the number of antennas at the UAV is smaller than that of co-channel GBSs. To achieve high-rate transmission yet avoid interfering with any of the existing terrestrial communications at the co-channel GBSs, we propose a novel multi-beam transmission strategy by exploiting the non-orthogonal multiple access (NOMA) technique. Specifically, the UAV sends each data stream to a selected subset of the GBSs, which can decode the UAV's signals and then cancel them before decoding the messages of their served terrestrial users, and in the meanwhile nulls its interference at the other GBSs via zero-forcing (ZF) beamforming. To draw essential insight, we first characterize in closed-form the degrees-of-freedom (DoF) under the proposed strategy. Then, we propose an efficient algorithm to maximize the UAV's transmit rate subject to the interference avoidance constraints for protecting the terrestrial users.

A survey on UAV-assisted wireless communications: Recent advances and future trends

In this paper, a nonorthogonal multiple access (NOMA) network is considered to assist ground users’ communications with two reconfigurable intelligent surfaces (RISs) and one unmanned aerial vehicle (UAV) technologies. In this scheme, UAV acts as a relay while the first RIS aids ground‐to‐air (G2A) communication and the second RIS helps the air‐to‐ground (A2G) communication. Under the outage probability (OP) constraint, the problem is formulated to maximize the data rate of the ground users by optimizing the transmission powers of base station (BS) and UAV, power allocation coefficients to the users and phase shift of the reflecting elements (REs) of RISs. An iterative algorithm is proposed using artificial bee colony (ABC) method for solving the problem and improving the network performance. Proposed algorithm is validated by Monte Carlo simulations, and the impact of system parameters is investigated on the network performance. Numerical results clarify that by two RISs utilization, the performances of the network are approximately improved 25% and 40% in terms of the network data rate and total OP, respectively, in comparison with the bench mark algorithms.

In the emergency communication scenario, how to quickly collect ground users (GUs) information and carry out reliable transmission has become a difficult point. In this paper, we consider the unmanned aerial vehicle (UAV)-assisted emergency communication system based on uplink non-orthogonal multiple access (NOMA). For the considered system, an alternative optimization scheme for the space location of UAV and resource allocation is proposed to maximize the uplink throughput from the UAV to base station (BS). The original joint optimization problem turns out to be non-convex and is divided into two sub-problems. Firstly, we optimize the space location of UAV by using particle swarm optimization algorithm, which has significant advantages in solving the optimal space location problem. Secondly, by using the tools of the first-order Taylor expansion and successive convex approximation, we jointly optimize the bandwidth allocation coefficients and transmit powers of GUs to maximize the throughput from the UAV to BS. Finally, simulation results verify our proposed scheme is superior in terms of the throughput for UAV-assisted emergency communication system compared with the existing scheme and the baseline schemes.

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users' transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

Unmanned aerial vehicles (UAVs) are playing an important role in serving ground users as base stations (BSs) during temporary events, or after terrestrial BSs fail. UAV deployment is a fundamental and vital problem which directly affects the coverage capacity and user experience. Especially, while deploying a UAV backbone network, the cost and the quality of service (QoS) of the ground users as well as the connectivity among UAVs need to be considered jointly. In this paper, the deployment problem is modeled as minimizing the amount of deployed UAVs while guaranteeing the coverage capacity and connectivity among UAVs. Further, a novel UAV deployment scheme is proposed where a heuristic algorithm is adopted to delete locations from the initially defined candidate UAV locations iteratively, and UAVs are deployed at the remaining locations finally. The simulation results show that our UAV deployment scheme can achieve minimum deployed UAVs while ensuring QoS requirements and a robust network among UAVs.

The use of unmanned aerial vehicles (UAV) as flying base stations is rapidly growing in the field of wireless communications to leverage the capacity of congested cells. This study considers a two-cell system where one of the cells is saturated, i.e. can no longer serve its users, and is supported by a UAV. The UAV positioning problem is investigated specifically to benefit from the interference cancellation properties available through the introduction of power-domain non-orthogonal multiple access (NOMA) techniques in coordinated multipoint (CoMP) systems. Indeed, adequate placement of the UAV can enable triple mutual successive interference cancellation (TMSIC) between a triplet of users, including a cell-edge and a cell-center user in each cell, to maximize system throughput or a mixture of throughput and TMSIC probability. The random line-of-sight/non-line-of-sight realizations of air-to-ground links between users and UAV are taken into account in the problem modeling, showing a significant improvement in performance compared to the conventional mean path loss model. The performance evaluation highlights the existing trade-offs between system capacity, fairness, and computational complexity of the investigated approaches.

Non-orthogonal multiple access (NOMA) is indeed an important technique of 5G and beyond 5G (B5G) communication systems, as it can increase the spectrum efficiency and system capacity under the same bandwidth. Long-distance unmanned aerial vehicle (UAV) networks, comprising a base station and multiple UAVs, are commonly used for reconnaissance purposes due to the extended flight range of the UAVs, which can provide broad coverage and satisfy the requirements for real-time communication. To alleviate spectrum underutilization of single channel orthogonal multiple access, we investigate resource allocation in UAV networks by utilizing NOMA and then formulate an optimization problem that maximizes the network capacity. The formulated problem can be decomposed into two sub-problems, namely UAV grouping and power allocation optimization. Accordingly, we present a UAV grouping method with low complexity according to the difference of channel gain between UAVs and base station, in which the UAVs with larger difference will be in the same cluster such that they can transmit signals over the same channel. Then, a power allocation optimization algorithm based on Lagrange multiplier method is then used to optimize the transmit power of UAVs utilizing the same channel. This algorithm considers the quality of service requirements for each UAV, and assigns power levels that maximize the overall network capacity. Simulation results have shown that the presented algorithms improve network capacity by approximately 19% compared with that of orthogonal multiple access, which demonstrates the effectiveness of utilizing NOMA in long-distance UAV networks.

As widely recognized 6G promising techniques, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have recently attracted much research attention in both academia and industries. In this paper, we propose the schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over the radio-frequency (RF)-powered 6G mobile wireless networks, which consists of an UAV serving as an MEC-server to collect/receive data from multiple ground users (GUs) and several sets of IRSs to significantly enhance the simultaneous wireless data and energy transmissions. To overcome the difficulties of on-board energy limitation significantly affecting UAV&#x2019;s sustainability and performances, we propose the schemes to minimize UAV&#x2019;s total flying-time while enabling all GUs&#x2019; data to be collected/received. We formulate a processing-time minimization problem which jointly optimize IRSs&#x2019; phase shifts, UAV&#x2019;s control in trajectory, flying-time, and resource-allocation, and scheduling of GUs. Since our joint-optimization problem is non-convex, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which thus can be iteratively solved. Finally, we validate and evaluate our proposed schemes through the conducted numerical analyses, showing that the total UAV flying-time can be significantly saved by around 20% under our proposed IRS/UAV-based schemes simultaneously supporting both wireless data and energy transmissions.

In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.

In this paper, a power efficient deployment for visible light communication (VLC)-enabled unmanned aerial vehicles (UAVs) is studied. In the studied model, each UAV can provide communication service for ground users and illumination builds the VLC links between UAVs and users. Hence, each UAV’s signal transmission and illumination will affect other UAVs’ signal transmission and illumination. Therefore, to deploy VLC-enabled UAVs so as to efficiently service the ground users, the interference caused by the signal transmission and illumination of UAVs must be considered. This problem is formulated as an optimization problem whose goal is to optimize the deployment of UAVs so as to minimize the power consumption for signal transmission and illumination. An iterative algorithm is first proposed to transform the optimization problem into a series of interdependent subproblems, and the transformed problems are then solved by the Lagrangian dual method. In addition, convergence and complexity of the algorithm are also analyzed. Numerical results show that the proposed scheme can reduce at least 53.7% power consumption when compared to the baselines with UAVs at the center of each sub-area.