Rotatable Antenna Assisted Mobile Edge Computing
This work addresses beam misalignment issues in mobile edge computing networks for users offloading tasks, representing an incremental improvement through mechanical antenna rotation.
This paper tackles the problem of maximizing weighted sum computation rate in a mobile edge computing network by using rotatable antennas to mitigate beam misalignment, achieving significant performance gains over conventional fixed-antenna systems as demonstrated in simulations.
This paper investigates a rotatable antenna (RA) assisted mobile edge computing (MEC) network, where multiple users offload their computation tasks to an edge server equipped with an RA array under a time-division multiple access protocol. To maximize the weighted sum computation rate, we formulate a joint optimization problem over the RA rotation angles, time-slot allocation, transmit power, and local CPU frequencies. Due to the non-convex nature of the formulated problem, a scenario-adaptive hybrid optimization algorithm is proposed. Specifically, for the dynamic rotating scenario, where RAs can flexibly reorient within each time slot, we derive closed-form optimal antenna pointing vectors to enable a low-complexity sequential solution. In contrast, for the static rotating scenario where RAs maintain a unified orientation, we develop an alternating optimization framework, where the non-convex RA rotation constraints are handled using successive convex approximation iteratively with the resource allocation. Simulation results demonstrate that the proposed RA assisted MEC network significantly outperforms conventional fixed-antenna MEC networks. Owing to the additional spatial degrees of freedom introduced by mechanical rotation, the flexibility of RAs effectively mitigates the severe beam misalignment inherent in fixed-antenna systems, particularly under high antenna directivity.