Deep-Learning-Based Resource Allocation for 6G NOMA-Assisted Backscatter Communications

The proliferation of Internet-of-Things applications has given rise to several challenges, including network congestion and high energy consumption. Among the promising technologies for beyond-5G networks, nonorthogonal multiple access (NOMA) and ambient backscatter communications (BackComs) stand out. These technologies enhance wireless access capacity and enable energy-efficient data sharing. In this study, we propose a novel energy-efficient resource allocation scheme for 6G NOMA-assisted BackCom networks. Our network model comprises a central reader (RD) and distributed backscatter devices (BDs) that harvest energy from incident signals to modulate useful data and reflect it toward the RD. To maximize energy efficiency (EE), we formulated a joint optimization problem of channel resource allocation and BDs’ reflection coefficients. However, solving this problem is challenging because of its nonconvexity and system dynamics. To address this issue, we developed a novel deep-learning-based algorithm that leverages the advantages of deep reinforcement learning. During training, we estimated the state components without relying on exact channel state information (CSI), which is computationally expensive. This estimation reduces the communication overhead raised in collecting CSI data. Extensive simulations were conducted to demonstrate the superiority of the proposed scheme. Simulation results show that the proposed scheme notably enhances EE compared to existing benchmarks. Specifically, improvements of approximately 30.3%, 41.7%, 6.0%, and 4.4% were observed when compared to the greedy approach, random approach, deep Q-Network, and successive convex approximation approach, respectively.