Aiming　at　the　influence　of　user　equipment　(UE)　mobility　on　computational　results　feedback　of　mobile　edge　computing　(MEC)　server,　a　resource　allocation　strategy　based　on　virtual　machine　migration　is　proposed　to　maximize　the　system　energy　efficiency　(EE).　UEs　can　harvest　radio　frequency　energy　from　multiple　frequency　bands　as　they　move.　When　the　UE　moves　over　a　long　distance,　virtual　machine　migration　is　adopted　to　transfer　the　computational　tasks　offloaded　by　the　UE　from　the　initial　MEC　server　to　the　current　MEC　server.　After　the　current　MEC　server　completes　the　computational　task,　it　can　directly　feed　the　computational　results　back　to　the　UE,　which　can　significantly　reduce　the　delay.　After　the　current　MEC　server　completes　the　computational　task,　it　will　feed　the　computational　results　back　to　the　UE.　By　jointly　considering　offloading　computational　task　by　the　UE　and　computational　results　feedback　of　MEC　server,　the　problem　of　power　and　subcarrier　allocation　is　modeled　as　a　mixed　integer　nonlinear　programming　problem.　The　goal　is　to　maximize　the　system　EE　under　the　constraints　of　energy　consumption,　subcarrier　allocation,　and　transmitting　power.　The　suboptimal　solution　is　obtained　by　introducing　the　genetic　algorithm.　Simulation　results　show　that　the　proposed　method　has　higher　EE　than　partial　power　or　subcarrier　allocation　methods　based　on　the　genetic　algorithm.　©　2020　IEEE.