Non-orthogonal Multiple Access is Used for Power Allocation to MaximizeThroughput in Cognitive Radio Network Systems

Abstract

AbstractIn order to improve the resource utilization rate of spectrum, this paper performs power allocation based on the combination of Cognitive Radio Network (CRN) and Non-Orthogonal Multiple Access (NOMA). The primary user (PU) and secondary user (SU) in the cell of the paper model exist at the same time. The SU uses the NOMA method to access the system. Under the premise of ensuring the quality of service (QoS) of the PU and the SU, and the SU The cumulative interference to the PU cannot exceed the “interference temperature threshold” threshold for the normal operation of the PU. This paper proposes a power allocation method under the premise of the maximum available transmit power of the secondary base station (ST) and the QoS requirement constraints, which can maximize the throughput of the SU in the system, and also consider the channel conditions, transmit power and normal communication of the SU The signal-to-interference-to-noise ratio threshold and other situations. Aiming at nonlinear problems, a power allocation algorithm is proposed using convex optimization. By solving the power allocation factor, the system throughput of SU is improved. Finally, the simulation results show that the algorithm can effectively improve the system throughput of multiple SU models, and the research results lay a theoretical foundation for the application of CRN and NOMA technology.KeywordsCognitive radio networksNon-orthogonal multiple accessPower allocationReinforcement learning