Power Control Strategy Research Based On Game Theory In Wireless Networks

The current wireless networks have changed the traditional networks communication mode,they provide the great convenience for sharing colorful resource.Technology companies invent advanced devices,e.g.intelligent mobile phone,pad,wireless sensor.These devices are generally battery powered,they bring enormous energy consumption because of devices' increment and low energy efficiency.It goes against sustainable development strategy that we encourage.The corresponding elements of wireless devices in the wireless network are the network nodes,and the network life cycle of the nodes depends on the energy consumption.Thus,how to design the wireless networks broadcast protocol and how to maximum energy efficiency have becoming a hot topic to be studied in the wireless networks.In this paper,we propose a strategy using power control based on game theory with the purpose of reducing wireless nodes' total energy consumption effectively and constructing a green network eventually.We first discuss relevant research background,recent research status and the remaining problem of energy-saving optimization in the wireless networks.We consider that it is a good way to energy-saving optimization and constructing a green network using game theory presented by economics.Then we introduce the knowledge and application about game theory,and propose the system model and energy consumption model conformed practically.In this paper,we apply the game theory to solve the minimum energy broadcast tree construction problem using power control by formulating the problem into a non-cooperative game,and discuss relevant energy-saving strategies of marginal contribution and shapley value based on game theory.Then,we make simulation experiments using related tools and compare the proposed algorithm to the other algorithms proposed in other papers,we get the power comparing figures as a result.We demonstrate that our game-based algorithm provides better performance than other known decentralized algorithms and the performance loss of our approach is small compared to a centralized solution.