2021/07/01 by Nguyen Quang Hieu, Hieu, Nguyen Quang, Dinh Thai Hoang +5 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Technologies #Artificial Intelligence (cs.AI) #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT) #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.2107.00238
openalex publication_date 2021/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This letter introduces a novel framework to optimize the power allocation for\nusers in a Rate Splitting Multiple Access (RSMA) network. In the network,\nmessages intended for users are split into different parts that are a single\ncommon part and respective private parts. This mechanism enables RSMA to\nflexibly manage interference and thus enhance energy and spectral efficiency.\nAlthough possessing outstanding advantages, optimizing power allocation in RSMA\nis very challenging under the uncertainty of the communication channel and the\ntransmitter has limited knowledge of the channel information. To solve the\nproblem, we first develop a Markov Decision Process framework to model the\ndynamic of the communication channel. The deep reinforcement algorithm is then\nproposed to find the optimal power allocation policy for the transmitter\nwithout requiring any prior information of the channel. The simulation results\nshow that the proposed scheme can outperform baseline schemes in terms of\naverage sum-rate under different power and QoS requirements.\n