Research On Resource Allocation Algorithms For Energy Harvestingassisted D2D Communication Networks

With the rapid development of communication technologies and the large-scale popularization of mobile terminals,it provides everyone with the opportunity to receive a bulk of information.Meanwhile,the explosive growth of network data volume has put higher demands on the system capacity and quality of service.The large number of mobile terminals accessing the network has caused a shortage of spectrum resources.To satisfy the rapidly growing system capacity demand,base stations are severely overloaded.Besides,large-scale devices have brought tremendous energy consumption,and the severe energy consumption problem has become a key factor affecting wireless communication.The energy harvesting-based device-to-device(D2D)communication can effectively improve spectrum efficiency and energy efficiency,so it has broad application prospects in next-generation mobile communications.However,energy harvesting-based D2 D communication still faces many challenges in terms of transmission security and system robustness.Therefore,this thesis has studied the resource allocation algorithms for energy harvesting-assisted D2 D communications.The main work of this thesis is as follows:(1)Contraposing the low spectrum efficiency of traditional wireless networks,a robust resource allocation algorithm for non-orthogonal multiple access-based D2 D communication networks with energy harvesting is studied.Considering the maximum outage probability threshold of users,the successive interference cancellation decoding order,and the maximum transmit power of users and the BS,an outage probability constraint-based robust energy efficiency optimization model is formulated.The optimal energy efficiency is obtained by jointly optimizing the transmission power,the resource block assignment factor,and the power splitting factor.Firstly,the formulated problem is converted into a non-probabilistic problem by using Markov's inequalities and the properties of Chi-square distribution.Then,Dinkelbach's method is used to transform the fractional objective function into a subtraction form and the integer variables are relaxed using the variable relaxation method.Finally,Lagrange dual theory is used to derive the closed-form expressions of robust resource allocation algorithm.Simulation results show that the proposed algorithm has better energy efficiency and robustness compared with the non-robust algorithm and the non-energy harvesting algorithm.(2)Contraposing the low flexibility of traditional wireless networks,a robust energy efficiency optimization algorithm for energy harvesting-based D2 D communication underlaying unmanned aerial vehicle(UAV)-assisted networks is studied,a robust resource allocation optimization model with Gaussian channel uncertainties and bounded coordinate uncertainties is formulated,which involves the outage probability constraints of ground terminals,the flight altitude constraint of the UAV,the minimum harvested energy constraints of D2 D users,and the transmission time constraint.Firstly,the Taylor series expansion is used to convert the coordinate uncertainty into the channel uncertainty.Based on the worst-case approach and the properties of the Q-function,the optimization problem with parameter perturbation is transformed into a deterministic one.Then,the non-convex problem is transformed into a convex one by exploiting the variablerelaxation method and the variable-substitution method.Finally,Lagrange dual theory is used to solve the optimization problem.Simulation results show that the proposed algorithm has better robustness and energy efficiency than the throughput-based maximization algorithm and the energy efficiency-based maximization algorithm.(3)To improve the security of the network,a secrecy rate and energy consumption tradeoff problem with ? fairness in D2 D communication networks with multiple eavesdroppers is formulated,which involves the interference threshold constraints of cellular users and the transmit power of D2 D transmitters.Firstly,the multi-objective optimization problem is converted into a single-objective optimization problem by using the weighted sum method.Then,the closed-form solution of the power splitting factor is obtained by analyzing the relationship between the objective function and the power splitting factor.Finally,the variable-relaxation method and the variable-substitution method are used to convert the non-convex optimization problem into a convex one which is solved by the convex optimization algorithm.Simulation results demonstrate that the proposed algorithm has better fairness than the secrecy rate-based maximization algorithm and the algorithm with max-min fairness.