Research Allocation And Optimization In Aerial-Terrestrial Integrated Heterogeneous Networks For Terahertz Communications

With the rapid development of various real-time interactive applications,such as virtual reality applications,communication networks are moving towards Intelligent Internet of everything.But due to the shortage of resources in urban hot spots,the development of communication networks are moving towards the direction of Intelligent Internet of everything has been restricted.Terahertz(THz)communications technology,with the advantage of ultra-large bandwidth,provides a new solution to the problem of limited resources.However,due to the short communication distance and noncooperative communication between atmosphere and THz frequency band,the development of the overall network performance has encountered a bottleneck.As an emerging network architecture which can extend the coverage of terrestrial wireless communication network,the aerial-terrestrial integrated heterogeneous networks open up a new way to solve the problem of short terahertz communications distance.At the same time,the paradigm of integrated sensing and communications employ the integrated sensing and communication signals to realize the large bandwidth data transmission of the magnitude of Gb/s and simultaneously detect the surrounding environment,which provides a new idea for solving the non-cooperative communication problem between terahertz networks and atmosphere.Therefore,this thesis focused on the resource optimization problem under the aerial-terrestrial integrated heterogeneous networks of terahertz communications.To further improve the overall performance of the system,the main research works of this thesis can be summarized as follows:(1)Aiming at the sensitive problem of video streaming delay faced by virtual reality applications,an aerial-terrestrial integrated heterogeneous network architecture with two layers of multi-band compatible with THz band and Sub 6GHz band was proposed.For achieving the virtual reality streaming in the downlink,the optimization problem of minimizing the total video streaming delay of virtual reality equipments were established by jointly optimizing virtual reality equipments association,field of view adaption and transmit power allocation.For the mixed integer non-convex optimization problem,the original problem was decoupled into two sub-problems,and the two sub-problems were solved by hypergraph matching and Lagrangian method.Then an alternating optimization algorithm was designed to obtain the sub-optimal solution of the original problem.The simulation results have shown that compared with the traditional Sub 6GHz terrestrial cellular network and Sub 6GHz integrated heterogeneous network scheme,the proposed alternating optimization algorithm can reduce the total delay by 25.3% and 20%,respectively.Compared with the equal power allocation algorithm and the matching algorithm for random virtual reality equipments association and random field of view,the proposed alternating optimization algorithm can reduce the total delay by 23.3% and26.2%,respectively.(2)Aiming at the challenge of non-cooperative communication between atmosphere and THz frequency band,a network architecture for sensing-assisted UAV communication in THz band was proposed.Through the joint optimization of carrier frequency,transmit power and UAV trajectory in the THz band,the problem of minimizing the total delay of the system was created.Since this problem was a mixed integer non-convex optimization problem,the original problem was decoupled into two subproblems,and the original problem was solved alternately and iteratively through the Lagrangian method and the Proximal Policy Optimization algorithm.The simulation results have shown that compared with the traditional Sub 6GHz scheme,the proposed alternating and iterative optimization algorithm can reduce the total delay of the system by 24.4% and increase the spectrum effectiveness by 26.6%.Compared with the traditional iterative optimization scheme,the proposed alternating and iterative optimization algorithm can reduce the total delay of the system by 27.4% and increase the spectrum effectiveness by 34.2%.