Sum Throughput Maximization in Multi-BD Symbiotic Radio NOMA Network
  Assisted by Active-STAR-RIS
Rahman Saadat Yeganeh Mohammad Javad Omidi Farshad Zeinali Mohammad Robat Mili Mohammad Ghavami
In this paper, we employ active simultaneously transmitting and reflecting
reconfigurable intelligent surface (ASRIS) to aid in establishing and enhancing
communication within a commensal symbiotic radio (CSR) network. Unlike
traditional RIS, ASRIS not only ensures coverage in an omni directional manner
but also amplifies received signals, consequently elevating overall network
performance. in the first phase, base station (BS) with active massive MIMO
antennas, send ambient signal to SBDs. In the first phase, the BS transmits
ambient signals to the symbiotic backscatter devices (SBDs), and after
harvesting the energy and modulating their information onto the signal carrier,
the SBDs send Backscatter signals back to the BS. In this scheme, we employ the
Backscatter Relay system to facilitate the transmission of information from the
SBDs to the symbiotic User Equipments (SUEs) with the assistance of the BS. In
the second phase, the BS transmits information signals to the SUEs after
eliminating interference using the Successive Interference Cancellation (SIC)
method. ASRIS is employed to establish communication among SUEs lacking a line
of sight (LoS) and to amplify power signals for SUEs with a LoS connection to
the BS. It is worth noting that we use NOMA for multiple access in all network.
  The main goal of this paper is to maximize the sum throughput between all
users. To achieve this, we formulate an optimization problem with variables
including active beamforming coefficients at the BS and ASRIS, as well as the
phase adjustments of ASRIS and scheduling parameters between the first and
second phases. To model this optimization problem, we employ three deep
reinforcement learning (DRL) methods, namely PPO, TD3, and A3C. Finally, the
mentioned methods are simulated and compared with each other.