Latest Advances in Spectrum Management for 6G Communications

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 unmanned aerial vehicle (UAV) base station plays a significant role in enhancing the terrestrial network, when the ground base station (GBS) is destroyed in emergent cases or its load exceeds the capacity of the terrestrial network. Presently, many papers focus on optimizing the UAV position deployment and user access, while ignoring the optimization about the spectrum resource management. To solve this problem, we formulate a joint optimization problem of the spectrum resource management and the position placement for UAVs with the constraint of the limited backhaul capacity. Later, the joint optimization problem is modeled as a hierarchical game decision architecture comprised of a UAV position placement game and a spectrum resource management game. Further, we analyze the equilibrium property of the two games and propose two best response- (BR) based optimization algorithms to reach the Nash Equilibriums (NEs) of the two games, respectively. Specifically, the proposed algorithm about the UAV deployment considers the variable granularity local exploration and global random exploration. Simulation results show that the proposed UAV deployment algorithm can improve the total throughput by 7% and 20% at least in comparison with the K-means deployment algorithm and the fixed granularity exploration algorithm, respectively.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

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.

In this letter, a novel application method of reconfigurable intelligent surfaces (RIS) is proposed, which installs a RIS on an unmanned aerial vehicle (UAV) rather than fixed to buildings. When the location information of the eavesdropper is unknown, the UAV’s rapid deployment and RIS’s ability to change the transmission channel are utilized to dynamically optimize the UAV’s flight trajectory and RIS’s phase shift. The trajectory of the UAV and phase shift of the RIS are optimized to avoid the risk of potential eavesdropping and improve the average downlink secrecy rate of the system. This letter utilizes Q-learning and Deep Q-network (DQN) algorithms to solve the problem. Experiments show that RIS can reduce the flight distance of the UAV on the premise of ensuring security. The scenarios where the two algorithms are applicable are analyzed, and the DQN algorithm is more suitable for the scenarios of large state and action spaces. Also, the experimental results imply that the RIS-assisted UAV improves the average downlink secrecy rate and system security.

We propose a novel formation control method considering the challenges of formation keeping, unmanned aerial vehicle (UAV) control, and position adjustment in UAV Base Station (UAV-BS) formation control scenarios. The method is based on a loose coupling structure inspired by the traditional virtual structure method. Firstly, a type of new formation is designed for UAV-BS group. When the UAV-BS group keep a stable network, the centers of all UAV-BS moving trajectory, e.g., circular, are treated as virtual points to form a rigid body. When the UAV-BS group transforms between different formations, the real positions of the UAV-BS group are used to form a rigid body. Secondly, we redesign a trajectory tracking method for UAV-BS to control the flight direction. A flight direction controller is designed for UAV-BS, based on nonlinear guidance logic. According to the empirical model of error feedback, an error corrector is designed to control the position error between the UAV-BS and the virtual point. Simulation results show that the proposed algorithm can satisfactorily complete the formation and control of UA-BS. Besides, the algorithm can reduce the difficulty of formation control of UAV-BS and the requirements of formation control for computing power and network communication.

Recently, a reconfigurable intelligent surface (RIS) that provides alternative wireless propagation paths (by reflecting an electromagnetic wave from a base station in desired directions) has attracted attention as a key facilitating technology for Beyond 5G and 6G wireless communications. This paper presents an improved path-loss model suitable for RIS-aided wireless communications. Unlike previously reported RIS path-loss models, the proposed model considers more practical electromagnetic phenomena (e.g., the incident and reflected gain patterns of the RIS unit cell, the effective received power at the RIS, the reflection phase error of each unit cell, and the specular reflection loss), by applying rigorous analysis. Furthermore, the accuracy of the proposed path-loss model is validated by extensive experimental measurements using our developed RIS prototype. The prototype consists of 576 phase-reconfigurable unit cells operating at 29 GHz, each of which features two PIN diodes. Each PIN diode is independently controlled to produce the desired phase distributions on the RIS. It is shown that the calculated received power in the proposed path-loss model matches the measurement results within a 1 dB margin across various practical test environments.

Communication with unmanned aerial vehicles (UAVs) in current terrestrial networks suffers from poor signal strength due to the down-tilt of the access points (APs) that are optimized to serve ground users ends (GUEs). To solve this, one could tilt the AP antenna upwards or allocate more power to serve the UAV. However, this negatively affects GUE downlink (DL) rates. In this paper, we propose to solve this challenge using a reconfigurable intelligent surface (RIS) to enhance the UAV communication while preserving the 3GPP-prescribed downwards antenna tilt and potentially improving the DL performance of the GUE. We show that under conjugate beamforming (CB) precoding and proper power split between GUEs and the UAV at the APs, an RIS with phase-shifts configured to reflect radio signals towards the UAV can significantly improve the UAV DL throughput while simultaneously benefiting the GUEs. The presented numerical results show that the RIS-aided system can serve a UAV with a required data rate while improving the GUEs DL performance relative to that in a CF-MIMO system without a UAV and an RIS. We support this conclusion through simulations under a varying numbers of RIS reflecting elements, UAV heights, and power split factor.

The unmanned aerial vehicles (UAVs) have been widely considered as one of the key applications for future wireless communication systems, where UAVs can be used as aerial base stations (BSs) for coverage extension, transmission improvement, emergency communication, and etc. Against this background, each UAV BS is expected to select the optimal codeword to form directional analog beams, and it is capable of achieving concurrent transmissions from multiple other UAV BSs simultaneously. However, in such a kind of UAV networks, due to the vast number of connected mobile users (MUEs), UAV BSs cannot timely and preciously select the codeword from the pre-defined codebook. Fortunately, machine learning (ML) is suitable for decreasing complexity in codeword selection, because ML could extract features from the data samples acquired in real environments. In this paper, we propose an ML approach to achieve an efficient and low complexity codeword selection for UAV networks. Specifically, we first derive the probabilities that multiple UAV BSs serve one MUE to obtain the average sum rate (ASR) in UAV networks. On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing the ASR in UAV networks. Third, we proposed an iterative sequential minimal optimization (SMO) training algorithm to train the data of all links between UAV BSs and MUEs, where the algorithm convergence is also discussed. Finally, we show the comparison between our proposed algorithm and the traditional methods by the simulation results. The simulation at last demonstrate our method is a more efficient solution for obtain a higher performance, where a much lower computational complexity can achieved than the traditional algorithm based on channel estimation.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

To address the ever-growing connectivity demand in communications, the adoption of ingenious solutions, such as utilization of unmanned aerial vehicles (UAVs) as mobile base stations, is imperative. In general, the location of a UAV base station (UAV-BS) is determined by optimization algorithms, which have high computationally complexities and are hard to run on UAVs due to energy consumption and time constraints. In this article, we overview the UAV-BS trajectory optimization problem for next generation wireless networks and show that a convolutional neural network (CNN) model can be trained to infer the location of a UAV-BS in real time. To this end, we create a framework to determine the UAV-BS locations considering the deployment of mobile users (MUs) to generate labels by using the data obtained from an optimization algorithm. Performance evaluations reveal that once the CNN model is trained with the given labels and locations of MUs, the proposed approach is, indeed, capable of approximating the results given by the adopted optimization algorithm with high fidelity, outperforming reinforcement learning-based approaches in resource-constrained settings. We also explore future research challenges and highlight key issues.

Ground-based communication facilities are at risk of being destroyed after natural disasters, such as earthquakes, floods, tsunamis, hurricanes, fires, or terrorist attacks. Unmanned aerial vehicles (UAV) can be used as air base stations to support user equipment (UE). UAV base station (UAV-BS) development plays a major part in rescue operations and post-disaster reconstruction. Improving network throughput in the UAV-BS signal coverage area and reducing deployment costs while maintaining effective communication are important issues that need to be addressed. In view of the problem, this paper demonstrates the problem of maximizing network throughput under the constraint of UAV-BS capacity by deploying UAV-BS. We proposed a UAV-artificial bee colony (U-ABC) algorithm to solve the problem of UAV-BS deployment. U-ABC algorithm can calculate the optimal flight position of each UAV-BS and maximum network throughput in the disaster area. In performance evaluation, we compared U-ABC algorithm with genetic algorithm, Greedy-ABC algorithm, PSO algorithm, DI-PSO algorithm and PSO-GWO algorithm. We analyzed the flight height altitude of UAV-BSs, the interference factor of UAV-BS, and the influence of the number of UE on network throughput. Results show that the proposed method improved the overall network throughput and achieved a high UE coverage rate under a given number of UAV-BSs.

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.