DQN-based decentralized multi-agent JSAP resource allocation for UAV swarm communication

It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U)link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another.The convolutional neural network,target network,and experience replay are adopted while training.The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle(UAV)communication is a promising technology that provides swift and flexible ondemand wireless connectivity for devices without infrastructure support.With recent developments in UAVs,spectrum and energy efficient green UAV communication has become crucial.To deal with this issue,Spectrum Sharing Policy(SSP)is introduced to support green UAV communication.Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications.In this paper,we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency.Different from most existing works,we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference.We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication.Firstly,we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process.Then,we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem.Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication.

Optimization for UAV-assisted simultaneous transmission and reception communications in the existence of malicious jammers

In this paper,we study an unmanned aerial vehicle(UAV)-assisted communication system,where the UAV is dispatched to implement simultaneous transmission and reception(STR)in the existence of multiple malicious jammers.Two schemes are investigated,namely frequency band-division-duplex(FDD)and time-fraction(TF).Based on the FDD scheme,the UAV can transmit information by using the portion of the bandwidth and receive information within the remaining portion of the bandwidth simultaneously.To perform the STR within the whole bandwidth,the TF-based scheme is considered by using a fraction of a time slot for the downlink,while the remaining fraction of the time slot is allocated for the uplink.We aim to maximize the worst-case throughput by optimizing the UAV three-dimensional(3D)trajectory and resource allocation for each scheme.The optimization problem is non-convex and thus computationally intractable.To handle the nonlinear problem,we use the block coordinate decomposition method to disaggregate the optimization problem into four subproblems and adopt the successive convex approximation technique to tackle non-convex problems.The simulation results demonstrate the performance of the TF-based scheme over the benchmark schemes.

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.