Computational Bits Maximization Design of UAV‐Assisted MEC System With FD‐NOMA

ABSTRACT

In the context of emerging sixth‐generation (6G) services, communication networks must simultaneously support computation‐intensive and delay‐sensitive users. This paper investigates a full‐duplex non‐orthogonal multiple access (FD‐NOMA) enabled unmanned aerial vehicle (UAV)‐assisted mobile edge computing (MEC) system, where computing users offload tasks through the uplink (UL), while the UAV serves communication users through the downlink (DL) over the same time‐frequency resources. The resulting co‐channel interference, residual self‐interference, heterogeneous quality‐of‐service (QoS) requirements, and UAV mobility lead to a mixed‐integer nonconvex resource‐allocation problem. We maximize the average computation rate by jointly optimizing the UAV trajectory, SIC‐related channel coefficients, local computational bits, UAV computing resources, and UL/DL transmit powers under mobility, user‐side power, computation‐resource, and QoS constraints. A block coordinate descent (BCD) framework is developed, where successive convex approximation (SCA) is used for the continuous nonconvex blocks and a penalty‐assisted update is adopted for the SIC block. Simulation results demonstrate that the proposed FD‐NOMA scheme provides higher computation performance than the considered benchmark schemes, while its gain is affected by residual self‐interference.