Research On Uplink Resource Allocation Scheme Based On NOMA In Machine Type Communications

Machine type communications(MTC)is an important part of the Internet of Things and has a wide market prospect.On the one hand,it is used in ultra-reliable and low latency communications(URLLC)scenarios such as autopilot,Internet of vehicles,and smart medical.On the other hand,it is used in massive machine type communications(m MTC)scenarios for sensing and data collection,such as smart city,environmental monitoring,smart agriculture and forest fire prevention.However,characteristics of MTC network are very different from human type communication networks.MTC has the characteristics of massive devices,uplink transmission mainly,low power consumption,different rate and delay quality of service(Qo S).As the number of devices connected increases,energy efficiency also needs to be improved.In addition,a radio resource allocation optimization scheme which considers only a single resource is imperfect.An optimization scheme which considers comprehensively multiple resources such as subcarriers and power can more effectively improve not only system power but also energy efficiency performance.In order to reduce MTC devices transmit power and improve system energy efficiency,a radio resource allocation scheme based on uplink power-domain NOMA is studied in this thesis.The main contributions of this thesis are as follows:(1)In order to meet the high transmission rate requirements of URLLC device and low power transmission requirements of m MTC device,a joint subcarrier and power allocation scheme in NOMA uplink is proposed.Specifically,it is divided into two sub-problems.Firstly,a two-way matching scheme is used to allocate subcarrier to URLLC devices while guaranteeing their transmission rate Qo S.Secondly,a resource allocation model is established with the goal of minimizing the power of the m MTC device,and the simplified model is solved using the Adaptive Genetic Algorithm(AGA).On the one hand,it is determined according to the AGA scheme that the chromosome is a mixture of real and discrete integer variables,and a real value encoding method is designed.On the other hand,an adaptive penalty function is established by considering two penalty factors,namely the number of violations of the constraints in the current individual and the proportion of feasible solutions in the current population.Simulation results show that the proposed scheme not only guarantees the high transmission rate of URLLC,but also can effectively reduce the transmit power of m MTC devices.(2)In order to further improve energy efficiency of the MTC system,an uplink NOMA resource allocation scheme based on energy efficiency optimization is proposed on the premise of the statistical delay Qo S requirements of different devices.Firstly,combining the effective capacity theory,the system energy efficiency is redefined with the effective capacity as the numerator and the sum of the device transmission power and circuit loss power as the denominator.Then,the objective function of the optimization model is transformed from fractional optimization to integer optimization by Dinkelbach algorithm.Finally,the optimal energy efficiency problem is solved with the improved AGA in(1).Simulation results show that the energy efficiency of the proposed scheme is about 96.2% of the optimal scheme based on traversal algorithm(TA).