In this article, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASRIS) to enhance the quality of 6G cellular network services. The network integrates commensal symbiotic radio (CSR) subsystems to facilitate communication between passive Internet of Things (IoT) users and active users, referred to as symbiotic backscatter devices (SBDs) and symbiotic user equipments (SUEs), respectively. Since the SBDs are passive, transmitting information to the SUEs poses significant challenges. To overcome this challenge, we harness the capabilities of massive multiple input multiple output (MIMO) antennas within the base station (BS) to relay the information transmitted by SBDs with greater power. This scheme uses the non-orthogonal multiple access (NOMA) technique for multiple access among all users, and potential interferences are eliminated using successive interference cancellation (SIC). The primary objective is to maximize the sum throughput between SBDs and SUEs. To achieve this, we formulate an optimization problem involving variables such as active beamforming coefficients at the BS and ASRIS, phase adjustments of ASRIS, and scheduling parameters between CSR and cellular networks. To solve this optimization problem, we used three deep reinforcement learning (DRL) methods: proximal policy optimization (PPO), twin delayed deep deterministic policy gradient (TD3), and asynchronous advantage actor critic (A3C). These methods were simulated, and the results demonstrate that A3C, TD3, and PPO have the best convergence speeds and achieve the highest increases in network throughput, respectively. Finally, the proposed scheme was evaluated using passive simultaneously transmitting and reflecting RIS (STAR-RIS), which demonstrated poorer performance compared to ASRIS.

Symbiotic radio (SR) is a novel paradigm in cognitive ambient backscatter communication, which makes the connection of devices without the need to reallocation of the frequency spectrum and complex energy infrastructure and so, can turn a 6G network into a green communication. In the system model of this paper, the base stations (BSs) with the active antennas, symbiotic backscatter devices (SBDs) and symbiotic user equipment (SUE) are considered. The main purpose is to minimize the energy consumption (EC) in the SR network and increase energy efficiency (EE) by taking into account to fulfillment of the minimum required throughput for SBDs. To this end, we introduce a new scheduling system called Timing SR (T-SR) to optimal resource allocation between SBDs. In this system, SBDs harvest energy from ambient signals and then send their information without interfering with each other, even in simultaneous transmission, to SUE by using the carrier of its signal. The main optimization problem has non-convex objective functions and constraints. To solve it, we use the novel mathematical methods called conic quadratic representation (CQR) and sequential quadratic (SQ) techniques. Finally, simulation results demonstrate the superiority of the proposed method compared to other outlined schemes in reducing the EC.