Research On Long-term Optimal Resource Allocation Strategy For Non-orthogonal Multiple Access Based Space Internet Of Things Networks

In the past few years,with the rapid development of the Internet of Things business and the limitations of ground communication settings,the Space Internet of Things(S-Io T)has become one of the important directions for the development of the.next generation Internet of Thing s with its low cost-effective and wide area coverage broadband access capabilities.Space Internet of Things can cover remote mountainous areas,oceans,deserts,forests and other areas where it is difficult to deploy ground facilities.With its large band width and high speed,it is widely applied to the intelligent transportation,high-definition(HD)video transmission,commercial aviation and maritime services.In addition,the explosive growth of Massive Machine Type Communications(m MTC)demand for Io T devices has caused non-orthogonal multiple access(NOMA)transmission mechanisms to attract the attention of relevant scholars and the industry.However,the current NOMA mechanism resource allocation scheme only considers the fixed power constraints.For satellites with the limited power,the long-term network performance is slightly inferior.On the other hand,the cache resources on satellite are limited and data overflow issues need to be considered.Therefore,this paper comprehensively considers the satellite's limited power and cache,and based on Lyapunov's theory,studies the joint resource allocation strategy of the satellite-ground multi-terminal downlink communication power domain NOMA transmission mechanism.The specific research content is as follows:An S-Io T scenario for downlink communication with multiple ground terminals under the coverage of a single sub-carrier beam of a multi-beam satellite is established,and the long-term joint resource optimization allocation problem is constructed by jointly considering the long-term power limitation and cache limitation through queuing theory,This long-term problem is decomposed into multiple single-slot online subproblems by using Lyapunov's theory.Under this optimization problem model,the perfo rmance of the NOMA scheme based on cache allocation,the existing NOMA scheme based on channel,and the Orthogonal Multiple Access(OMA)scheme are analyzed first.Because the proposed long-term optimization of resource allocation is a non-convex optimization problem,Karush-Kuhn-Tucker(KKT)conditions are used to narrow the range of feasible solutions and then search to obtain the optimal solution.However,the spatial complexity of the KKT method increases exponentially with the number of users.Therefore,Particle Swarm Optimization(PSO)algorithm is used to ontain a better solution.The simulation verifies that the proposed long-term optimized NOMA-PSO scheme is superior to the existing related NOMA schemes.At the same time,because the optimal sequen tial interference cancellation(SIC)decoding order at the receiving end depends on both the channel state and the cache state,it is difficult to solve it by classical mathematical methods.Under the goal of maximizing network utility,the above-mentioned NOMA-PSO scheme tends to allocate more power to users with better channel conditions,while some users with poor channels may not be allocated power all the time.Based on this,this paper introduces the minimum rate constraint of Quality of Service(Qo S),starting from the fairness index,and improving the scheme with the goal of enhancing satisfaction among users.The performance such as outage probability and user satisfaction of the improved scheme under different signal-to-noise ratios(SNR)is studied.The maximum number of users that can be supported simultaneously under different signal-to-noise ratios is summarized,which shows that when multi-user access,it can effectively improve network utility and reduce network delay.The innovation of this paper is to use Lyapunov's theory to jointly model the long-term optimization problem of power and cache,and then decompose the long-term problem into a series of single-slot online subproblems,and allocate power in real time according to the channel sta te and cache state,which improves the network utility and enhances network stability.By using a deep learning algorithm to approximate the optimal SIC decoding order,the system performance is further improved.Based on this,the high fairness index of 5 G mobile communication is combined to further improve the scheme.