The Resource Management Algorithm For Cognitive Heterogeneous Networks

With the rapid development of wireless communication technology and wireless business, the Heterogeneous Wireless Networks(HWNs) with a variety of access networks coexisting have become the trend of wireless communication network, and the demand of spectrum also grow rapidly. However, the policy of fixed allocation distributes the spectrum resources to the specific Wireless technology, leaving few resources for new systems and new technology to use, the situation that a shortage of spectrum resources is becoming seriously. In addition, the utilization of the allocated spectrum resources is poor, especially in the heterogeneous network, although the access networks provide similar business, the general spectrum allocation cannot guarantee the best quality of service due to the networks work individually. Therefore, it is necessary to explore the methods of spectrum resources for allocation in the heterogeneous networks.This paper focuses on the methods of spectrum resources for allocation in the heterogeneous networks, puts forward a structure of two levels for resources management, and optimizes the methods of resources management.The work of the paper is mainly reflected in the following aspects:(1) Firstly, the paper introduces the characteristics of the wireless spectrum briefly, and makes a conduction that it is important to manage the spectrum resources which are scarce and non-renewable. For the characteristics, this paper researches the management technology of sharing the spectrum resource for cognitive heterogeneous networks, constructs a structure of two levels for resources management with a long period for pre allocation of resources and a short period for scheduling combinations of resources, forming a mode of management with the coarse-grained management and fine-grained management coexist, which optimizes the management of spectrum resources, also improves the utilization of resources and the overall efficiency of the system.(2) In the long period of coarse-grained management, it predicts the traffic of current based on historical information herein the heterogeneous networks, and determines the amount of spectrum resources for the heterogeneous networks should be allocated. In the period of coarse-grained management, it analysis the nature of three forecasting methods in detail which are gray prediction model and Q–learning, determining the algorithm of prediction used in this paper, and allocates the spectrum resources to the heterogeneous networks in proportion base on the result of prediction. Finally, it analysis the performance of the algorithm of predicted allocation in the period of coarse-grained.(3) In the short period of fine-grained management, this paper puts forward an improved algorithm based on the bidding aiming the problem that spectrum resources does not satisfy the heterogeneous networks in the period of pre-allocated. This improved algorithm adds the value of interference and sets the reserve price in the original bidding algorithm, and allocates the spectrum resources to the networks who is lack of resources in the period of pre-allocated, not only improving the utilization of spectrum, but also enhancing the efficiency of the system. Finally, this paper analysis the performance of the improved auction algorithm, and verifies the efficient of this algorithm, and also analysis the performance about the structure of two levels for resources management, verifies the superiority and practicality.