Energy Harvesting Based Energy Efficient Resource Scheduling And Optimization In Cellular Wireless Networks

The continuous development of wireless communications,the explosive growth of the data communication service and the deep optimization of the network have resulted in a dramatic increase in the energy consumption of the wireless networks.On the one hand,huge energy consumption brings high operating costs to network operators;on the other hand,large amounts of emissions make the greenhouse effect more and more serious.Therefore,it is of major practical significance to study how to achieve green communication and reduce the energy consumption of the communication system.Energy harvesting technology can collect and utilize renewable energy such as solar energy,wind energy and microwave radiation that widely exists around us,and is an effective measure to deal with high energy consumption and achieve green communication.This article addresses the new requirements for the development of cellular wireless networks based on energy harvesting.From the perspective of energy management and power allocation,this paper explores the dual near-far effect problems and the issue of energy supply and service non-adaptation based on energy harvesting.Through the use and improvement of existing energy harvesting models,a set of superior energy efficiency resource scheduling and optimization methods for cellular wireless networks based on energy harvesting is proposed to provide theoretical support for designing energy efficient cellular networks.The main research contents and innovations of the paper are summarized as follows:1.Research on resource scheduling mechanism for cellular wireless networks with stable energy sources.For the dual near-far effect problem in wireless power transfer,power control and resource allocation technologies are studied.Since the traditional wireless energy transfer scheme does not consider the impact of distance on network performance and lacks the utilization of high energy efficiency acquisition methods.Therefore,the paper first proposes an energy-efficient adaptive wireless energy transfer strategy for cellular networks with stable energy sources.The strategy can adaptively adjust the transmission power of the energy tower according to the distance between the mobile terminal and the power beacon,and effectively reduce the energy consumption of Grid under the premise of ensuring the throughput of the network does not decrease.Research shows that network energy efficiency is significantly optimized after adopting the proposed strategy.Based on this,the paper studies the distance-aware clustering strategy for the Internet of Things with unwavering energy sources.A distance-aware clustering strategy is proposed to balance the imbalance of energy acquisition and consumption of nodes in the Internet of Things.The research shows that:compared with not using it,the distance-aware clustering strategy can improve the system energy efficiency by more than 4 times,reduce the communication interruption by at least 50%,and at the same time,improve the network coverage efficiency.2.Research on resource scheduling mechanism for cellular wireless networks with unstable energy sources.In a cellular wireless network powered by renewable energy only,since the renewable energy source is space-time instability and non-uniformity,mobile terminal suffers from the non-adaptation of energy supply and service.Therefore,the paper studies the renewable energy cooperation and power control technology.A bargaining-based green energy allocation game is proposed.First,by defining the green energy parameters,selfishness,and green energy collaborative revenue function,a bargaining-based power allocation game model is set up to convert the optimal power allocation problem into a maximum social welfare problem.Then,the existence of the Nash bargaining solution for the renewable energy distribution game is demonstrated.Finally,through the two-way policy update algorithm,the optimal solution to the Nash bargaining game is obtained via an iterative iteration.The research shows that in the actual environment of renewable energy harvesting,compared with not adopting the strategy,after adopting the bargaining-based green energy allocation game strategy,the average rate of the network and the outage probability are significantly optimized.In addition,the strategy does not need static channel state information and energy acquisition functions,so it can be implemented in unpredictable environments.3.Research on energy-efficient resource allocation technology for hybrid energy-powered cellular wireless networks.On the basis of the previous research,in-depth exploration of energy efficient resource scheduling and energy management technologies for cellular networks based on energy harvesting is conducted.Firstly,the paper studies the energy scheduling mechanism of green energy cooperation in cellular networks with hybrid energy supply,and proposes a water filling optimized power allocation strategy.By adopting the water filling optimization,the transmission power of power beacon is optimized with the non-uniform energy supply;the consumption of the Grid is reduced by the cooperation of the renewable energy between power beacons.The result shows that compared with the traditional method of green energy cooperation with uniform power allocation,the proposed strategy can increase the rate of the network by 8%?20%and reduce the energy consumption of the Grid by about 20%.Based on this,an online strategy of adaptive traffic offloading and bandwidth allocation for green M2M communications is studied.By adaptive traffic offloading,the amount of traffic afforded by a mobile terminal is matched with the non-uniform renewable energy that has harvested.By adaptive bandwidth allocation,the bandwidth occupied by mobile terminals are matched with the traffic that is redistributed after the traffic offloading.Research shows that:Compared with the strategy of not using the adaptive traffic offloading and bandwidth allocation,after adopting the proposed strategy,the energy consumption of the grid is decreased by 55%and the average rate is doubled.