Computation Energy Efficiency Optimization In Mobile Edge Computing Networks

The rapid developments of Internet-of-things(IoT)and 5G communication technologies and a wide range of emerging mobile applications have driven the increasing computing demands for mobile devices.As a promising technology,mobile edge computing(MEC)is an important key technology to improve the computation efficiency of mobile devices.However,most of the existing works have been devoted to reducing the energy consumputions,or increasing the number of computed bits,but ingnored the tradeoff between those two metric.In order to effectively improve the computation efficiency of MEC networks,the weighted sum computation effciency maximization problems are formulated in an MEC network with orthogonal frequency division multiple access(OFDMA).Besides,an optimal resource allocation strategy is proposed in the user cooperation-assisted mobile edge computing network to maximize the computation efficiency of the system.The detail contributions are as follows:1.The weighted computation efficiency maximization in OFDMA-based MEC networksIt is the first time that the framework for investigating the weighted computaion efficiency maximization problem is formulated in the OFDMA-based MEC network with considering the minimum required computation bits,offloading power,subchannel allocation.On this basis,the optimal resource allocation strategy is proposed both in binary offloading mode and partial offloading mode.Simulation results verify the trade-off between weighted user computation efficiency and computed bits.In addition,the simulation results also show that the proposed maximum weighted computation efficiency optimization scheme can improve fairness among users.2.The computation efficiency maximization in user cooperation-assited MEC networkIn a user cooperation-assisted MEC network,a framework for maximizing computation efficiency is established for the first time under the partial offloading mode.In considering user computation bit requirements,offloading power constraints,local computing frequency constraints,and offloading time allocation,the optimal resource allocation strategy is proposed.Based on the analysis and derivation,the corresponding optimal resource allocation strategy is proposed.Simulation results verify that user cooperation can further improve computation efficiency in MEC networks.It is also verified that there is a tradeoff between computation efficiency and computation bits.