Interference Management And Resource Allocation In Cognitive Small Cell Networks

The emergence of smart phone not only drives the rapid development of communication industry,but also accelerates the innovation of communication technologies.The rise of small cell technology has spared the conventional macro network an enormous burden of coverage,and made a compensation for coverage blind zone in macro cell edge.As a spectrum management technology,cognitive radio is introduced to improve spectrum efficiency and solve the radio resource shortage problem.The combination of cognitive radio and small cell technology is considered as a promising solution for further enhancement of spectrum utilization,which attracts considerable attention nowadays.However,in cognitive small cell networks,random deployment of small cell base stations makes the interference environment complicated and variable.Thus,this thesis studies the interference problem and resource allocation problem in cognitive small cell networks.This thesis focuses on the fairness issue of resource allocation in cognitive small cell networks,and proposes a distributed fairness-based resource allocation(DFRA)algorithm.Firstly,to ensure fairness in numbers of subchannels allocated to each cognitive small cell base station,the DFRA algorithm introduces satisfaction degree of small cell base station to evaluate the level of satisfaction of the allocation result for each small cell base station.Secondly,the DFRA algorithm guarantees the fairness in average throughput of each small cell base station by applying the channel state difference based resource allocation scheme,and at the same time improves the overall throughput of cognitive small cell networks.Finally,the proposed DFRA algorithm works in a fully distributed manner without any central coordination,which reduces the system signaling overhead.Simulation results indicate that the proposed DFRA algorithm can achieve significant improvement in both aspects of resource allocation fairness and network throughput.Additionally,in allusion to the interference management and resource allocation problem in cognitive small cell networks,most existing algorithms are based on the perfect spectrum sensing results,including the DFRA algorithm proposed in chapter 3.However,it's difficult to perform perfect spectrum sensing in practice.Therefore,it is more realistic to analyze the interference scenario for the case of imperfect spectrum sensing.This thesis proposes a distributed imperfect-spectrum-sensing-based resource allocation(DIRA)algorithm,which jointly consider the influence of imperfect spectrum sensing and channel uncertainty.The proposed DIRA algorithm aims to maximize the total throughput of cognitive small cell networks while guaranteeing the minimum quality of service of cognitive small cell users and ensuring the communication of macro cell users.Simulation results demonstrate the effectiveness of the proposed algorithm,and illustrate that the DIRA algorithm can not only mitigate the interference from cognitive small cell base station to macro cell users,but also enhance the throughput of small cell networks.