Resource Collaborative Optimization Methods And Key Techniques In Wireless Networks

With the rapid development of mobile Internet applications and the dramatic increase of the requirement of data service, wireless networks are confronted with great challenges. Heterogenous network collaboration can optimize the wireless network resource and improve network capacity and multi-terminals collaboration can improve the user's quality of experience. In addtition, ultra-dense heterogeneous network formed by implement a large number of small cell base stations outside the traditional macro cellular can greatly enhance the network capacity. However, with the increase of the number of small cell base station, the resource allocation and routing design of the backhaul are increasingly becoming key factors to affect the user's service rate. Moreover, energy consumption becomes an important reason that restricts the ultra-dense deployment of the wireless access nodes. Aimed at the above problems, the resource collaborative optimization methods and key techniques in wireless networks are studied. The main research contents and results are as follows:(1) For the multi-terminal environment in heterogeneous wireless networks, the wireless access networks selection and resource allocation problem are studied. A network selection method and resource allocation algorithm based on multiple attribute decision making(MADM) algorithm are proposed. First, considering the network consitions, terminal and service characteristics, the most appropriate candidate networks are selected. Then, based on utilitization function, a resource allocation algorithm is proposed for each selected network. Simulation results demonstrate that the proposed algorithm can effectively use multiple networks resource to satisfy the user's specific requirements for service and simultaneously improve the network resource utilization.(2) For hybrid backhaul environment in wireless ultra-dense network, the collaborative resource allocation of multiple wireless backhaul links is studied. Based on a centralized backhaul link selection scheme according to the global neighbor cells information table in macro base station, a resource allocation approach based on the cost function for the parallel backhaul links is proposed. The optimization problem is established with the objective of minimizing the power consumption and transmission delay of the backhaul links and the constraints of available resource of the small cell base station.The analytical solution of the rate of the backhaul links is obtained by solving the convex optimization problem under certain conditions. The simulation results show that the algorithm can maintain better performance in terms of spectrum efficiency when the number of backhaul link varies.(3) For wireless backhaul environment in wireless ultra-dense network, the problem of the wireless backhaul routing decision is studied. Based on the classification method for small cell base station with the number of the shortest hop, a routing scheme is proposed based on hop first and load sharing. Then the optimization problem is established with the objective of maximizing the network throughput and the constraints of available resource and the total transmit power of the small cell base station. The solution of the backhaul routing is obtained by the proposed proportional fair algorithm based on the distance and available resource of the next hop.The simulation results indicate that the proposed algorithm can significantly outperform others algorithms in terms of system throughput rate.(4) For energy harvesting wireless network environment, the access control algorithm in wireless network with energy efficiency optimization is studied. An access control algorithm based on fuzzy control theory for dynamic parameters adjustment is proposed. The maximum backoff times of the wireless access node is dynamically adjusted for contending the shared wireless channel according to the harvested and remainder energy in the last frame. The random backoff time and the backoff step of the wireless access node in the backoff process are adjusted dynamically according to the service data rate of the wireless access node and the history condition of the shared wireless channel that the node experienced. The simulation results indicate that the proposed algorithm enhances the system throughput and improves the energy efficiency of the wireless access node.