Graph neural network-based scheduling for multi-UAV-enabled communications in D2D networks

In this paper,we jointly design the power control and position dispatch for Multi-Unmanned Aerial Vehicle(UAV)-enabled communication in Device-to-Device(D2D)networks.Our objective is to maximize the total transmission rate of Downlink Users(DUs).Meanwhile,the Quality of Service(QoS)of all D2D users must be satisfied.We comprehensively considered the interference among D2D communications and downlink transmissions.The original problem is strongly non-convex,which requires high computational complexity for traditional optimization methods.And to make matters worse,the results are not necessarily globally optimal.In this paper,we propose a novel Graph Neural Networks(GNN)based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner.Particularly,we first construct a GNN-based model for the proposed network,in which the transmission links and interference links are formulated as vertexes and edges,respectively.Then,by taking the channel state information and the coordinates of ground users as the inputs,as well as the location of UAVs and the transmission power of all transmitters as outputs,we obtain the mapping from inputs to outputs through training the parameters of GNN.Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples.Moreover,it also shows that the performance of proposed GNN-based method is better than that of traditional means.

Joint 3D Trajectory and Resource Optimization for A UAV Relay-Assisted Cognitive Radio Network

In this paper,we consider a new spectrum sharing scenario for a cognitive relay network,where a secondary unmanned aerial vehicle(UAV)relay receives information from the ground secondary base station(SBS)and transmits information to the ground secondary user(SU),coexisting with the primary users(PUs)at the same wireless frequency band.We investigate the optimization of the UAV relay’s three-dimensional(3D)trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs.The information throughput maximization problem is studied by jointly optimizing the UAV relay’s 3D trajectory and the transmit power of the SBS and the UAV,subject to the constraints on the velocity and elevation of the UAV relay,the maximum and average transmit power,and the information causality,as well as a set of interference temperature(IT)constraints.An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique,the successive convex approximation technique and the alternating optimization method.Finally,simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput。

Robust Artificial Noise-Aided Beamforming for A Secure MISO-NOMA Visible Light Communication System

Visible light communication(VLC)and non-orthogonal multiple access(NOMA)have been deemed two promising techniques in the next wireless communication networks.In this paper,secure communications in the presence of potential eavesdropper are investigated for a multiple-input single-output VLC system with NOMA.The artificial noise jamming and beamforming technologies are applied to improve secure performance.A robust resource allocation scheme is proposed to minimize the total transmit power taking into account the constraints on the quality of service requirement of the desired users and the maximum tolerable data rate of the eavesdropper,and the practical imperfect channel state information of both the desired users and the eavesdropper.The formulated non-convex optimization problem is tackled based onS-Procedure and semi-definite programming relaxation.Simulation results illustrate that our proposed resource allocation scheme can effectively guarantee communication security and achieve transmit power saving.Moreover,the height and number of LED can significantly affect system performance and the optimum LED height can be obtained for different LED numbers.

Resource Allocation and Trajectories Design for UAV-Assisted Jamming Cognitive UAV Networks

Unmanned aerial vehicle(UAV)communications are subject to the severe spectrum scarcity problem.Cognitive UAV networks are promising to tackle this issue while the confidential information is susceptible to be eavesdropped.A UAV jamming assisted scheme is proposed.A joint resource allocation and trajectories optimization problem is formulated in a UAV-assisted jamming cognitive UAV network subject to diverse power and trajectory constraints.An alternative optimization algorithm is proposed to solve the challenging non-convex joint optimization problem.Extensive simulation results demonstrate the superiority of our proposed scheme and many meaningful insights are obtained for the practical design of cognitive UAV networks.

Partially Distributed Channel and Power Management Based on Reinforcement Learning

This paper studies a dynamic multi-user wireless network,where users have no knowledge of the arrival rate and size of data block and suffer from a constraint on long-term average power consumption.Considering such a network,we address the problem of dynamically optimizing channel/power allocation,so as to minimize the long-term average data backlog.The design problem is shown to be a constrained Markov decision process.In order to solve the problem without knowledge on dynamics of the system,we introduce post-decision states and propose a resource allocation algorithm based on reinforcement learning.Since the channel/power allocation problem is coupled,the multiuser decision problem suffers from curses of dimensions(of state/action/outcome space).This makes centralized decision-making and optimization on channel/power allocation suffer from a long convergence time.As a countermeasure,a partially distributed resource allocation framework is proposed.The multiuser power allocation problem is decoupled into single-user decision problems,while channel allocation optimization is performed in a centralized manner.In order to further reduce computational complexity,we propose a low-complexity reinforcement learning method.Simulation results reveal that the proposed algorithm outperforms the state-of-the-art myopic optimizations in terms of energy efficiency and the backlog performance.

Aerial intelligent reflecting surface for secure wireless networks:Secrecy capacity and optimal trajectory strategy

This work investigates the potential of the aerial intelligent reflecting surface(AIRS)in secure communication,where an intelligent reflecting surface(IRS)carried by an unmanned aerial vehicle(UAV)is utilized to help the communication between the ground nodes.Specifically,we formulate the joint design of the AIRS’s deployment and the phase shift to maximize the secrecy rate.To solve the non-convex objective,we develop an alternating optimization(AO)approach,where the phase shift optimization is solved by the Riemannian manifold optimization(RMO)method,while the deployment optimization is handled by the successive convex approximation(SCA)technique.Furthermore,to reduce the computational complexity of the RMO method,an element-wise block coordinate descent(EBCD)based method is employed.Simulation results verify the effect of AIRS in improving the communication security,as well as the importance of designing the deployment and phase shift properly.