Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge

By exploiting the superiority of nonorthogonal multiple access (NOMA), NOMA-aided mobile-edge computing (MEC) can provide scalable and low-latency services for Internet Things. However, given prevalent stochasticity wireless networks sophisticated signal processing NOMA, it is critical but challenging to design an efficient task offloading algorithm MEC, especially under a large number devices. This article presents online that jointly optimizes decisions resource allocation maximize long-term system utility (i.e., measure throughput fairness). Since optimization variables are temporary coupled, we first apply Lyapunov technique decouple stochastic into series per-slot deterministic subproblems, which does not require any prior knowledge network dynamics. Second, propose transform nonconvex subproblem optimizing NOMA power equivalently convex form by introducing set auxiliary variables, whereby time-complexity reduced from exponential complexity $\mathcal {O} (M^{3/2})$ . The proposed proved be asymptotically optimal, even partial device states at base station. Simulation results validate in terms utility, stability improvement, overhead reduction.