Dynamic RAT Selection and Transceiver Optimization for Mobile-Edge Computing Over Multi-RAT Heterogeneous Networks

Mobile-edge computing (MEC) integrated with multiple radio access technologies (multi-RATs) is a promising technique for satisfying the growing low-latency computation demand of intelligent Internet of Things (IoTs) applications. Under the wireless MapReduce framework for executing nomographic functions, this article investigates the joint RAT selection and transceiver design for over-the-air (OTA) aggregation of intermediate values (IVAs) in multi-RAT MEC systems, while taking into account the energy budget constraint for the local computing and IVA transmission per wireless device (WD), so as to adapt to the instantaneous communication opportunities in multiple RATs and the dynamic computational task loads. To provide a complete Pareto optimal solution, we minimize the weighted sum of the computational mean squared error (MSE) of the aggregated IVA at the RAT receivers, the IVA transmission cost of the WDs, and the associated transmission time delay. The joint RAT selection and transceiver design problem for OTA aggregation of the IVAs is a nonconvex mixed-integer problem, which is NP hard. We develop a low-complexity algorithm to solve the challenge by continuous relaxation and alternating optimization. Specifically, the optimal receive beamforming vectors at the gNB/access points (APs) are shown to be the minimum MSE (MMSE) filters. Exploiting the hidden convexity of the remaining subproblem, we obtain an efficient iterative algorithm by alternating between the RAT selection and the transmit coefficient variables for OTA aggregation of IVAs. Extensive numerical results verify the effectiveness of our proposed design as compared to other existing schemes.