A Joint Model Extraction and Data Detection Framework for IRS-NOMA System

This paper addresses the problem of data detection for intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. Also, the phase noise, analog-to-digital converter (ADC) quantization error and imperfect channel estimation are taken into consideration. At present, IRS is designed to be deployed in a wide range of scenarios. However, the quantization error model and IRS phase error model developed for limited scenarios may not be well adapted to the complex and changing environment, which brings a new challenge for the application of IRS. Therefore, this paper investigates a new hybrid method for joint noise model extraction, device activity detection and data detection in the case of unknown noise models, to solve the aforementioned IRS problem. Firstly, the Gaussian mixture model (GMM) is used to describe the model of quantization noise and phase noise. Thus the problem of model extraction is transformed into the calculation of the key parameters of GMM. Secondly, an improved expectation maximization algorithm is used to calculate the quantization noise model parameters. Finally, utilizing the property of the structure of the problem, a message-passing based algorithm is investigated. Simulation results show the merits of the proposed hybrid algorithm.