Intelligent Reflecting Surface Assisted Non-Orthogonal Multiple Access
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Intelligent reflecting surface (IRS) which consists of a large number of low-cost passive reflecting elements and can digitally manipulating electromagnetic waves, is a new and disruptive technology to achieve spectrum- and energy-efficient as well as cost-efficient wireless networks. In this paper, we consider an IRS-assisted non-orthogonal-multiple-access (NOMA) system in which a base station (BS) transmits superposed downlink signals to multiple users. In order to optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (SINR) (i.e., equivalently the rate) of all users, by jointly optimizing the power allocation at the BS and the phase shifts at the passive IRS. However, the formulated problem is non-convex and difficult to be solved optimally. By leveraging the block coordinated decent, successive convex optimization and semidefinite relaxation techniques, an efficient algorithm is further proposed to obtain a sub-optimal solution. The convergence is proved and the complexity is analyzed for the proposed algorithm. Also, a low-complexity solving scheme is proposed. Simulation results show that the IRS can enhance the rate performance for downlink NOMA systems significantly even for the scenario in which users have the same or comparable channel strength(es), and the practical IRS with a 3-bit phase quantizer is sufficient to ensure the rate degradation of less than 3.4
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