Sum Throughput Maximization in Multi-BD Symbiotic Radio NOMA Network Assisted by Active-STAR-RIS

TL;DR

This paper proposes a novel multi-phase NOMA network enhanced by active-STAR-RIS and deep reinforcement learning to maximize sum throughput, addressing coverage and signal amplification challenges in symbiotic radio systems.

Contribution

It introduces the use of active-STAR-RIS in a symbiotic radio NOMA network and employs DRL algorithms for optimizing network parameters, a novel approach for throughput maximization.

Findings

DRL algorithms outperform traditional methods in throughput optimization.

Active-STAR-RIS significantly improves coverage and signal strength.

The proposed scheme achieves higher sum throughput compared to baseline models.

Abstract

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.