Energy and Delay Minimization of Partial Computing Offloading for D2D-Assisted MEC Systems

As a promising 5G cutting-edge communication technology, mobile edge computing (MEC) can improve the quality of computing by offloading computation-intensive tasks to base stations (BSs) or adjacent users through device-to-device (D2D) links. When mobile users offload tasks through the D2D links, using this technology, both transmission energy consumption and transmission delay can be reduced. In this paper, we propose a D2D-assisted MEC system, which can process a large number of independent computing tasks to reduce energy consumption and delay. Different from conventional binary computing offloading, we use partial computing offloading in this paper, which can reduce delay and energy consumption. In particular, we first propose a Knapsack problem-based pre-allocation (PA) algorithm to reduce the amount of offloaded tasks with known transmission power. By using this algorithm, we can obtain the offloading decisions of the tasks. We also apply the variable substitution technique to recast the constructed non-convex problem to be convex, so that the optimal transmission power can be obtained. We finally propose a new alternate optimization algorithm to alternately optimize tasks offloading decisions and transmission power. Simulations show that the proposed algorithm can significantly reduce the delay and energy consumption compared with the traditional offloading schemes.