Due　to　the　limited　battery　energy　of　mobile　devices,　the　issue　of　energy-efficient　resource　allocation　has　drawn　significant　interest　in　the　mobile　cloud　computing　area.　Simultaneous　wireless　information　and　power　transfer　(SWIPT)　is　an　innovative　way　to　provide　electrical　energy　for　mobile　devices.　Extensive　research　on　the　resource　allocation　problem　is　conducted　in　SWIPT　systems.　However,　most　previous　works　mainly　focus　on　energy　harvesting　over　a　relatively　narrow　frequency　range.　Due　to　small　amounts　of　energy　harvested　by　the　users,　the　practical　implementations　are　usually　limited　to　low　power　devices.　In　this　paper,　an　energy-efficient　uplink　resource　allocation　problem　is　investigated　in　a　cloud-based　cellular　network　with　ambient　radio　frequency　(RF)　energy　harvesting.　In　order　to　obtain　sufficient　energy,　a　broadband　rectenna　is　equipped　at　the　user　device　to　harvest　ambient　RF　energy　over　six　frequency　bands　at　the　same　time.　From　the　viewpoint　of　service　arrival　in　the　ambient　transmitter,　a　new　energy　arrival　model　is　presented.　The　joint　problem　of　sub-carrier　and　power　allocation　is　formulated　as　a　mixed-integer　nonlinear　programming　problem.　The　objective　is　to　maximize　the　energy　efficiency　while　satisfying　the　energy　consumption　constraint　and　the　total　data　rate　requirement.　In　order　to　reduce　the　computational　complexity,　a　suboptimal　solution　to　the　optimization　problem　is　derived　by　employing　a　quantum-behaved　particle　swarm　optimization　(QPSO)　algorithm.　Simulation　results　show　that　more　energy　can　be　harvested　by　the　user　devices　compared　with　narrow　band　SWIFT　systems,　and　the　QPSO　method　achieves　higher　energy　efficiency　than　a　conventional　particle　swarm　optimization　approach.