| Title | AI Empowered RIS-Assisted NOMA Networks: Deep Learning or Reinforcement Learning? |
| Authors | Zhong, Ruikang Liu, Yuanwei Mu, Xidong Chen, Yue Song, Lingyang |
| Affiliation | Queen Mary Univ London, Sch Elect Engn & Comp Sci EECS, London E1 4NS, England Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing 100876, Peoples R China Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China Peking Univ, Dept Elect, Beijing 100871, Peoples R China |
| Keywords | RECONFIGURABLE INTELLIGENT SURFACES PASSIVE BEAMFORMING DESIGN REFLECTING SURFACE MULTIPLE-ACCESS PERFORMANCE SYSTEMS |
| Issue Date | Jan-2022 |
| Publisher | IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS |
| Abstract | A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication system over fading channels is investigated, where both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are employed. In particular, the time overhead for configuring the RIS reflective elements at the beginning of each fading channel is considered. The optimization goal is maximizing the effective throughput of the entire transmission period by jointly optimizing the phase shift of the RIS and the power allocation of the AP for each channel block. In an effort to solve the formulated problem and fill the research vacancy of the performance comparison between different machine learning tools in wireless networks, a deep learning (DL) approach and a reinforcement learning (RL) approach are proposed and their representative superiority and inferiority are investigated. The DL approach can locate the optimal phase shifts with the deep neural network fitting as well as the corresponding power allocation for each user. From the perspective of long-term reward, the phase shift control with configuration overhead can be regarded as a Markov decision process and the RL algorithm is proficient in solving such problems with the assistance of the Bellman equation. The numerical results indicate that: 1) From the perspective of the wireless network, NOMA can achieve a throughput gain of about 42% compared with OMA; 2) The well-trained RL and DL agents are able to achieve the same performance in Rician channel, while RL is superior in the Rayleigh channel; 3) The DL approach has lower complexity and faster convergence, while the RL approach has preferable strategy flexibility. |
| URI | http://hdl.handle.net/20.500.11897/632777 |
| ISSN | 0733-8716 |
| DOI | 10.1109/JSAC.2021.3126068 |
| Indexed | SCI(E) |
| Appears in Collections: | 信息科学技术学院 |
