NOSCM: A Novel Offloading Strategy for NOMA-Enabled Hierarchical Small Cell Mobile-Edge Computing

Mobile-edge computing (MEC) is considered as a promising technology in 5G, as it can solve the contradiction between the explosive growth of computation-intensive tasks and the limited computation power and battery life of local devices. However, in the 5G environment, most of the existing studies on task offloading in MEC have either failed to study the compatible multiple access technologies or have not considered the hierarchical relationship between small cell base station (SBS) and macro base station (MBS). Therefore, to explore the MEC offloading problem under the unique 5G architecture is of great significance at present. In light of this, we study the task offloading strategy in the nonorthogonal multiple access (NOMA)-enabled small cell MEC network. Specifically, we first describe a noval small cell MEC architecture in which MBS and SBS are both deployed with edge servers and there is a hierarchical relationship between the two. Based on this architecture, we have established the communication model and computation model, respectively. Then, we formulate the energy and delay weighted sum minimization problem, which aims at minimizing the total cost of task offloading under different requirements and takes into account the constraints of computation capabilities. To solve the problem, we develop a hybrid genetic hill climbing (HGHC) algorithm that can quickly find the optimal solution. Moreover, we perform a lot of simulation experiments to evaluate the performance of our algorithm under different parameters. The experimental results show that our algorithm can converge within about 20 iterations, which is superior to traditional heuristic algorithms.

Automated summary

This study focuses on the task offloading strategy in NOMA-enabled small cell MEC networks in the 5G environment. By utilizing a novel small cell MEC architecture, an energy and delay weighted sum minimization problem is formulated and a hybrid genetic hill climbing (HGHC) algorithm is proposed to solve it. The algorithm shows superior performance compared to traditional heuristic algorithms, converging within about 20 iterations.