Energy-Efficient Computation Offloading in Meta Computing: Joint Power and Resource Optimization with Statistical CSI

This article investigates the energy-efficient computation offloading problem in a meta computing environment, integrating multiple-input multiple-output (MIMO) technologies and statistical channel state information (CSI). Existing resource allocation approaches always depend on instantaneous CSI, which is impractical for highly dynamic meta computing scenarios due to rapid CSI fluctuations. To address this issue, we propose a novel computation offloading scheme that jointly optimizes transmit power and computation resource allocation for multi-antenna users by leveraging statistical CSI. Specifically, a closed-form expression for the ergodic capacity and a tight bound for the MIMO diversity transmission scheme are derived. These theoretical results decouple the complex joint optimization into two tractable subproblems, where the ergodic capacity expression is utilized for global computation resource assignment via a Kuhn-Munkres (KM) algorithm, while the bound significantly simplifies the transmit power optimization problem. Experimental results demonstrate that the proposed scheme achieves substantial energy consumption reductions compared to conventional methods, highlighting the effectiveness of utilizing statistical CSI in meta computing resource allocation.