Joint Uplink and Downlink Rate Splitting for Fog-Computing-Enabled Internet of Medical Things

The Internet of Medical Things (IoMT) and fog computing facilitate the shift from hospital-based medical examinations to real-time electronic healthcare. A novel transmit scheme for fog computing-enabled IoMT is proposed in this article to address real-time monitoring needs, utilizing uplink and downlink rate splitting (RS) techniques. Fog computing enables offloading partial computation tasks to the edge server while processing the remaining tasks locally to reduce computing time. Uplink and downlink RS techniques offer flexible co-channel interference management to minimize offloading and feedback durations. The primary objective is to minimize the overall time cost encompassing task offloading, data processing, and result feedback. For this purpose, decisions on task offloading, computing resource allocation, uplink beamforming, downlink beamforming, and common rate allocation are jointly designed. However, this approach leads to a nonconvex optimization problem. Several auxiliary variables are introduced to handle this, and accurate surrogates are constructed to smooth the logarithmic transmit rate. Additionally, closed-form expressions are derived for optimal computing resource allocation per user. Based on these formulations, computing resource allocation and energy consumption are transformed into a convex constraint set. Finally, an alternating optimization algorithm is developed to update auxiliary and intrinsic variables iteratively. Simulation results demonstrate the effectiveness of the proposed transmit scheme and algorithm, showing substantial improvements over several baseline methods.