Research On Wireless Resource Optimization Algorithms Based On Network Energy Efficiency

The requirements of network capacity and coverage quality in future wirelesscommunication neworks will greatly improve the intensity of cellular networkingdeployment and the complexity of physical transmission, it will also increase thecomplexity of wireless resource management and the network energy consumption.Thus, the research of relevant problems combining the wireless network resourceoptimization and network energy consumption has practical significance. In terms ofvarious energy sources, networks can be divided into the traditional powered networkssupplied by power grid and battery, the energy harvesting networks supplied byrenewable energy, and the hybrid powered networks supplied by mixed energy, etc. Atpresent, researches on the network energy efficiency of wireless mobile communicationnetworks are mainly focused on the traditional powered networks and the energyharvesting networks. Therefore, the wireless resource optimization algorithms aremainly researched in our thesis based on energy efficiency in the traditional powerednetwork and the energy harvesting network. The detailed research works are as follows:1. The relay selection and power allocation algorithms with the two-waymulti-relay cooperative mode in the traditional powered network. A joint relay selectionalgorithm is presented by jointly considering both channel state information (CSI) andresidual energy. Furthermore, a multi-relay power allocation algorithm based on convexoptimization (MRPA-CO) is proposed, which uses the Lagrange multiplier method toallocate the power under the total power constraint at the terminal nodes and relaynodes respectively. Simulation results show that the proposed CSI&residual energybased relay selection and MRPA-CO algorithms can prolong network lifetime andimprove the network rate compared to the traditional relay selection and powerallocation algorithms.2. The timeslot and energy resource optimization algorithms in two-node andthree-node energy harvesting networks. Firstly, the time duration of each timeslot isdivided into two time intervals respectively used for energy harvesting and datatransmission according to the save-then-transmit protocol. Next, the energy cooperationbetween nodes through energy transfer is fulfiled in the first time interval on the basis ofcompleting energy harvesting in the energy harvesting phase, and the data transmission is accomplished during the second time interval for timeslot resource optimization andnetwork rate improvement. Simulation results show that the proposed algorithms canimprove the network rate through timeslot resource and energy resource optimizationsin energy harvesting networks.