Energy-Efficient D2D-Assisted Computation Offloading in NOMA-Enabled Cognitive Networks

Due to the limited computation resources and lifetime of user equipment, we study the energy minimization problem for computation offloading in cognitive radio networks (CRNs). This work proposes a device-to-device (D2D)-assisted computation offloading scheme for non-orthogonal multiple access (NOMA)-enabled CRNs. Specifically, the secondary user (SU) can provide computation resources for the primary user (PU) to access the spectrum owned by the PU. With the constraints of task deadline and maximum transmit power, offloading decision and power control of PU and SU are optimized to minimize the energy consumption of CRNs. The solution is obtained by deploying the block coordinate descent method and successive convex approximation. Simulation results show the improvement of the proposed scheme in terms of energy consumption and computing performance compared with other methods.

Automated summary

The paper proposes a D2D-assisted computation offloading scheme for NOMA-enabled CRNs, optimizing offloading decision and power control of PU and SU to minimize energy consumption while meeting task deadline and maximum transmit power constraints. The solution is obtained using block coordinate descent method and successive convex approximation, showing improvement in energy consumption and computing performance compared to other methods according to simulation results.