Details of Research Outputs

Edge computing has been considered as a promising solution for enabling computation-intensive yet latency-sensitive applications at resource-constrained wireless devices (WDs). In this paper, exploiting the advanced small-cell dual connectivity (DC), we investigate a paradigm of dual computation offloading in which a WD can simultaneously offload partial workloads to a cloudlet-server co-located at the macro base station (MBS) and an edge-server (ES) co-located at a small-cell based station (SBS). To facilitate the multi-user dual computation offloading, we exploit a hybrid model of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA). Specifically, due to the SBSs' limited channel resources, we consider that the WDs form different NOMA-groups for offloading their respective workloads to different SBSs, which improves the spectrum efficiency. Meanwhile, all WDs use FDMA for offloading their workloads to the MBS, which avoids the WDs' co-channel interference. We formulate a joint optimization of the WDs' partial offloading decisions, their FDMA transmission to the MBS, different NOMA-groups' transmission to the SBSs, as well as the computing-rate allocation of the ESs and the cloudletserver, with the objective of minimizing the overall latency for completing all WDs' workloads. Despite the strict non-convexity of the joint optimization problem, we propose a layered yet cell-based distributed algorithm for obtaining the optimal dual offloading solution. Based on the optimal dual offloading solution, we further investigate how to properly group WDs into different NOMA-groups for offloading workloads to the corresponding SBSs, and propose a cross-entropy based learning algorithm for finding the optimal NOMA grouping scheme. Numerical results are finally provided to validate the effectiveness and efficiency of our proposed algorithms.