Joint Offloading Selection and Resource Allocation for Integrated Localization and Computing in Edge-intelligent Networks

Driven by a series of advanced intelligent applications, it is expected to provide low-latency and high-accuracy localization and computing services at the network edge. To this end, this paper aims to realize an efficient integration of localization and computing by leveraging the collaborative capability of distributed multi-node, i.e, multiple base station (BSs) and user equipments (UEs), for the transfer and fusion of localization information and computing data. To enhance the overall performance of integrated localization and computing under limited radio and computing resources, a mixed integer nonlinear programming (MINLP) problem with the objective of the weighted total energy consumption minimization while ensuring quality of service (QoS) requirements of localization and computing is formulated. By exploiting the structure of this computationally difficult MINLP problem and employing some approximation techiniques, we propose an alternating optimization (AO)-based joint offloading selection and resource allocation algorithm to obtain a feasible sub-optimal solution. Simulation results show that the proposed algorithm can effectively achieve a good performance both for localization and computing under limited resources, and has an obvious performance gain over the baseline ones, which confirms its feasibility and effectiveness in edge intelligence networks.