Intelligent Resource Allocation in Backscatter-NOMA Networks: A Soft Actor Critic Framework

With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra-massive machine-type communications networks are expected to connect large-scale Internet of Things (IoT) devices. However, due to NOMA co-channel interference, power allocation to large-scale IoT devices becomes critical. With existing convex optimization approaches, it is challenging to find the optimal resource allocation in a dynamic environment. To alleviate this problem and increase the sum rate of uplink backscatter devices, this work develops an efficient model-free BAC-NOMA approach to assist the base station with complex resource scheduling tasks in a dynamic environment. We jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using the soft-actor critic algorithm. The proposed algorithm ensures the quality of service (QoS) requirements of downlink users while enhancing the sum rate of uplink backscatter devices. Numerical results reveal the superiority of the proposed algorithm over the conventional optimization (benchmark) approach in terms of the average sum rate of uplink backscatter devices. We show that a network with multiple downlink users obtained a higher reward for a large number of iterations than episodes with a lower number of iterations. With different numbers of backscatter devices, the proposed algorithm outperforms the benchmark scheme and BAC with orthogonal multiple access. Additionally, we demonstrate that our proposed algorithm enhances sum rate efficiency at different self-interference coefficients and noise levels. Finally, we evaluate the sum rate efficiency of the proposed algorithm with varying QoS requirements and cell radii.