Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA
This work addresses a specific optimization challenge in wireless communications, but it is incremental as it applies an existing DRL method to a known bottleneck in IRS-NOMA systems.
The paper tackles the problem of maximizing sum rate in an IRS-assisted downlink NOMA system by using DRL to optimize the IRS phase shift matrices, achieving high sum rates compared to OMA and showing that a tenfold increase in SIC imperfection reduces rates by over 10%.
In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario with the aim of maximizing the sum rate of users. The optimization problem at the IRS is quite complicated, and non-convex, since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. Simulation results reveal that IRS assisted NOMA based on our utilized DRL scheme achieves high sum rate compared to OMA based one, and as the transmit power increases, the capability of serving more users increases. Furthermore, results show that imperfect successive interference cancellation (SIC) has a deleterious impact on the data rate of users performing SIC. As the imperfection increases by ten times, the rate decreases by more than 10%.