Research On Resource Allocation Techniques For Noma-based Heterogeneous Network

From the first generation of mobile communications to the current fifth-generation mobile communications(5G),the rapid development of wireless communication technology has greatly improved people's production methods and lifestyle.Compared to 4G systems,5G systems need to provide higher system capacity and more simultaneous service users.To achieve this goal,Non-Orthogonal Multiple Access(NOMA)is widely recognized as one of the candidate technologies.The Power Domain Non-Orthogonal Multiple Access(PD-NOMA)technology can implement the multiplexing of spectrum resources in the cell in the power domain,which can effectively improve the spectrum utilization and simultaneous service users.In addition,Heterogeneous Network(Het Net),as the main network structure in the ultra-dense networking,implements spatial multiplexing of spectrum resources.Through the joint deployment of micro-cells and dense femto-cells,the system capacity is improved,and the coverage performance of the network can be satisfied.NOMA-based Het Net technology can increase spectrum reuse rate and increase the number of service users.However,it also brings much more interference,which may reduce the system throughput,user data rate,and the fairness among the users.These problem can be solved by designing optimal wireless resource allocation algorithms.Thus,in this paper,we studies algorithms and techniques for improving system performance from the aspects of power control,user pairing and channel resource allocation for different communication environments and user requirements.The main innovations of this paper include the following three aspects.Firstly,To deal with the user fairness reduction of uplink users caused by inter-cell interference under the scenario of imperfect channel state information,a joint power allocation and user pairing algorithm is proposed.To prompt the fairness of users within one user pair and between different user pairs,we proposed a new optimization objective function based on Jensen's inequality.In order to improve the fairness in the user pair,a prediction based particle Swarm Optimization(PBPSO)algorithm is proposed.PBPSO first accelerates the convergence to the local best by predicting the optimization direction and the golden section method,and then improves the spatial search ability to find the global optimal solution.PBPSO effectively improve convergence speed and system performance,with guaranteed system throughput.On the basis of PBPSO,in order to im-prove the fairness between user pairs,Probability based Tabu Search(PTS)is proposed.First,the sub-optimal solution is obtained by relax the non-convex integer programming problem.Then the quantum search algorithm is used to convert the obtained solution into pairing probability.Finally,the optimal solution is found by Tabu search.PTS can improve the fairness between users,which has a strong solution space search ability and relieve the local optimal problem in integer programming.Through simulation verification,the resource allocation algorithm of joint power control and user pairing proposed in this paper greatly improves the fairness of uplink users.Secondly,to deal with the downlinks ystem throughput degradation caused by intercell interference,a graph theory based channel resource allocation algorithm is proposed.In the algorithm,in order to solve the problem of strong interference,a maximal clique based uniform graph coloring algorithm is proposed.It can increase the number of subchannels occupied by each user and promote the load balance of the channel.As a result,the system throughput is improved.In order to further solve the cumulative interference problem,a hypergraph clustering based resource allocation algorithm is proposed to allocate users to each channel,which reduces the total interference of the system.Moreover,in order to reduce the computational complexity,according to the sparseness of the weight matrix,the Lancozs algorithm is used to reduce the dimension of the matrix.The original optimization problem is transformed into the eigenvector problem of the reduced dimension matrix,which reduces the computational complexity.Through simulation verification,the proposed algorithm can improve downlink system maximum sum data rate in the closed access mode.Thirdly,to deal with the downlink system maximum sum data rate degradation caused by the SIC residual interference,in the scenario of the imperfect Interference Cancelation(SIC),a successive convex approximation(SCA)based downlink power allocation algorithm is proposed.The algorithm decomposes the power control into two sub-problems,which are power partition coefficient optimization and base station total transmit power optimization.The proposed algorithm can obtain the optimal decoding sequence under imperfect SIC and improve system maximum sum data rate.In order to solve the nonlinear fractional programming problem,the proportional fair objective function is transformed into several convex optimization problems by SCA to improve the system maximum sum data rate.Finally,to measure the effectiveness of the SCA algorithm,this paper proposes a power control algorithm based on Polyblock outer approximation,which can obtain op-timal power control results and provide optimization performance upper bounds for other algorithms.The simulation results show that the SCA-based algorithm can improve the downlink system maximum sum data rate in the scenario of the imperfect Interference Cancelation(SIC).The resource allocation algorithms proposed in this paper can effectively improve the fairness of uplink and maximum sum data rate of downlink of NOMA-based heterogeneous network.