Energy-Minimization Resource Allocation for FD-NOMA Enabled Integrated Sensing, Communication, and Computation in PIoT

The integration of power Internet of Things (PIoT) with integrated sensing, communication, and computation (ISCC) has become crucial for achieving hierarchical co-regulation and sustainable development in power systems. However, traditional PIoT models designed for edge computing are facing complex challenges due to more intricate and coupled resource allocation in the ISCC design. In this work, we propose a full-duplex (FD) and non-orthogonal multiple access (NOMA) assisted ISCC framework (FD-NOMA-ISCC) in PIoT and investigate the main challenges of FD-NOMA-ISCC from the perspective of joint resource optimization. We jointly optimize the receive beamformer, transmit beamforming design, uplink power control, task offloading decision, and computing resource allocation to minimize the total energy consumption. This forms a complex mixed integer nonlinear programming (MINLP) problem due to the strong correlation between uplink and downlink, as well as the coupling between communication and computing resource allocation. To tackle this, we propose an alternating optimization algorithm based on linear constrained minimum variance (LCMV) that decouples the problem into two iteratively solved subproblems: 1) joint transmit beamforming and power control problem, and 2) joint computing resource allocation and offloading decision problem. Numerical results show that the proposed scheme has a significant advantage in reducing system energy consumption compared with the benchmark schemes.