Wireless Network Successive Interference Optimization Based On Multi-objective Power Control

Faced with the requirement to greatly improve the spectrum efficiency of 5G,a new multiplexing technology,non-orthogonal multiple access(Noma),is proposed.In a code division multiple access system(NOMA).In the code division multiple access(CDMA)technology used in the 3G era,multiple users share a channel resource,and the user's address code has a certain correlation,which leads to mutual interference between users during decoding.In orthogonal multiple access(OMA),a channel is divided into subchannels,for example,channel resources are divided according to frequency division or time,and each subchannel can only be allocated to one user.The NOMA technology combines the above two technologies,that is,the channel resource is divided into multiple sub-channel resources,however,each subchannel resource can be assigned to multiple users.Noma technology uses orthogonal frequency division multiplexing(OFDM)technology,but the subchannels are orthogonal to each other.,and there is no interference between the sub-channels,but the same sub-channel is still shared by multiple users.There will still be interference problems,and these interferences are multiple access interference(MAI).Multiple access interference not only significantly reduces the performance of wireless network systems,but also restrict the number of users that the system can accommodate.By applying the serial interference elimination algorithm(sic,sequential interference elimination)to the receiving end of the base station,multiple access interference can be effectively reduced.The SIC algorithm solves the received multi-channel mixed signal through an iterative method.The mixed signal contains the signal and noise of multiple transmitting nodes.In each iteration,the signal with the highest power is first eliminated from the packet mixed signal,Then the strongest signal is picked from the remaining mixed signals to remove it.Such loop detection and the signal is removed from it until the signal-to-noise ratio of the signal is less than the set threshold,the algorithm ends.For the limited spectral resources of wireless communication systems,this method can effectively improve the network throughput and system capacity of each node of the system.There is a prerequisite for the realization of the serial interference cancellation algorithm.The signal strength of each mobile terminal to the base station must meet an appropriate ratio range in which the transmission power of each mobile terminal needs to be controlled.(1)We propose a series interference elimination method based on the power control of the non-cooperative game theory,and optimize and adjust the transmission power of the mobile node using the method of the non-cooperative game theory.Through the linear weighting method in the multipurpose technology,a multipurpose power control model was established with the aim of improving the signal-to-noise ratio of the base station signal and reducing the power consumption of the system.Power control issues are translated into a non-cooperative gaming process between wireless nodes.On this basis,the utility function is designed,and the transmission power of each node is adjusted so that each node can obtain the maximum utility.When the Nash equilibrium point is reached,increasing the transmission power of each node alone cannot achieve greater utility.Simulation analysis showed that this algorithm can effectively improve the throughput of nodes in a network system and reduce the total energy consumption of the system.(2)We proposed a power control method based on a multipurpose genetic algorithm for sequential interference elimination.Assumptions to guarantee the realization of the Si C algorithm,the minimum transmit power formula of each node and the optimization model of multiple objectives are derived.Aiming at the multi-objective model,the general NSGA-II is improved.The population distribution maintenance strategy and the inequality constraint optimization strategy improved the global search capability of the algorithm and obtained a Pareto optimal executable solution set.The effects of population and maximum number of iterations on this algorithm were analyzed experimentally.Under the same conditions,we compare the effects of the improved NSGA-II algorithm on this problem,and compare the improved NSGA-II algorithm with the PESA-II algorithm and the MOPSO algorithm.The experimental results verified the superiority of this algorithm.