异构无线网络密集部署场景下高效网络接入及频谱分配
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摘要
如何在异构网络重叠覆盖场景下实现动态耦合频谱资源高效分配以满足用户流量需求是下一代无线通信网络的重要挑战。综合考虑网络域频谱属性差异化及用户域需求多样化问题,以用户获得总带宽最大化为目标,将频谱资源分配建模为非线性多约束条件0-1整数规划问题,并设计了两种求解方法。首先,设计了一种基于改进匈牙利算法的化简方法,该方法通过对约束条件进行化简,将复杂模型转化为标准形式0-1规划,并通过对匈牙利算法进行改进,有效求解了该复杂的频谱分配问题;其次,设计了一种改进的遗传算法,把主网络干扰约束及次用户需求融合进适应度评估中,以修正不符合要求的基因,并利用精英主义思想保留优秀个体,以进化迭代到优秀个体。最后通过实验对提出的方法与粒子群优化方法的性能进行对比分析,实验结果显示化简方法具有较大的效率优势,而改进遗传算法可得到更大的带宽。
How to achieve efficient allocation of dynamically coupled frequency spectrum resources to meet user traffic demands in heterogeneous network overlapping coverage scenarios is an important challenge for next-generation wireless communication networks. This paper comprehensively considers the difference of spectral attributes in the network domain and the diversification of user domain requirements. With the goal of maximizing the total bandwidth obtained by users,spectrum resource allocation is modeled as a nonlinear multi-constraint conditional 0-1 integer programming problem,and two solving methods are designed and implemented. Firstly, a simplified method based on the improved Hungarian algorithm is designed. By simplifying the constraints, the complex model is transformed into a standard form 0-1 programming, and the Hungarian algorithm is improved to effectively solve the complex spectrum allocation problem. Secondly,an improved genetic algorithm is designed, which uses elitism to preserve excellent individuals, fuses primary network interference constraints and sub-user requirements into fitness assessment to correct non-conforming genes for iteratively evolving to excellent individuals. Finally, the performance of the proposed methods and the particle swarm optimization method are compared by experiments. The experimental results show that the simplification method has a greater efficiency advantage, while the improved genetic algorithm can obtain a greater bandwidth.
How to achieve efficient allocation of dynamically coupled frequency spectrum resources to meet user traffic demands in heterogeneous network overlapping coverage scenarios is an important challenge for next-generation wireless communication networks. This paper comprehensively considers the difference of spectral attributes in the network domain and the diversification of user domain requirements. With the goal of maximizing the total bandwidth obtained by users,spectrum resource allocation is modeled as a nonlinear multi-constraint conditional 0-1 integer programming problem,and two solving methods are designed and implemented. Firstly, a simplified method based on the improved Hungarian algorithm is designed. By simplifying the constraints, the complex model is transformed into a standard form 0-1 programming, and the Hungarian algorithm is improved to effectively solve the complex spectrum allocation problem. Secondly,an improved genetic algorithm is designed, which uses elitism to preserve excellent individuals, fuses primary network interference constraints and sub-user requirements into fitness assessment to correct non-conforming genes for iteratively evolving to excellent individuals. Finally, the performance of the proposed methods and the particle swarm optimization method are compared by experiments. The experimental results show that the simplification method has a greater efficiency advantage, while the improved genetic algorithm can obtain a greater bandwidth.
引文
[1]IMT-2020(5G)推进组.5G愿景与需求白皮书[R].2014.
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[16]Owen G.Game theory[M].[S.l.]:Academic Press,1995.
[17]孙杰,郭伟,唐伟.认知无线多跳网中保证信干噪比的频谱分配算法[J].通信学报,2011,32(11):111-117.
[18]邝祝芳,陈志刚.认知无线Mesh网络中一种有效的多目标优化频谱分配算法[J].中南大学学报(自然科学版),2013,44(6):171-178.
[19]李鑫滨,刘磊,石爱武,等.基于一种改进人工蜂群算法的认知无线电频谱分配[J].应用科学学报,2013,31(5):448-453.
[20]Hasan N U,Ejaz W,Ejaz N,et al.Network selection and channel allocation for spectrum sharing in 5G heterogeneous networks[J].IEEE Access,2016,4:980-992.
[21]Tragos E Z,Zeadally S,Fragkiadakis A G,et al.Spectrum assignment in cognitive radio networks:a comprehensive survey[J].IEEE Communications Surveys&Tutorials,2013,15(3):1108-1135.
[22]Tsiropoulos G I,Dobre O A,Ahmed M H,et al.Radio resource allocation techniques for efficient spectrum access in cognitive radio networks[J].IEEE Communications Surveys&Tutorials,2016,18(1):824-847.
[23]李廷鹏,钱彦岭,李岳.基于改进匈牙利算法的多技能人员调度方法[J].国防科技大学学报,2016,38(2):144-149.
[2]Technical and operational information for identifying spectrum for the terrestrial component of future development of IMT-2000 and IMT-advanced:ITU-R M.2079[R].
[3]Mc Henry M A,Mc Closkey D,Bates J.Spectrum occupancy measurements:location 6 of 6:shared spectrum building roof[R].Shared Spectrum Company,2005.
[4]Xue J,Feng Z,Chen K.Beijing spectrum survey for cognitive radio applications[C]//IEEE 78th Vehicular Technology Conference,Sep 2013:1-5.
[5]IMT-2020(5G)推进组.5G概念白皮书[R].2015.
[6]IMT-2020(5G)推进组.5G网络技术架构白皮书[R].2015.
[7]IMT-2020(5G)推进组.5G网络架构设计白皮书[R].2016.
[8]王钦辉,叶保留,田宇,等.认知无线电网络中频谱分配算法[J].电子学报,2012,40(1):147-154.
[9]Peng C,Zheng H,Zhao B Y.Utilization and fairness in spectrum assignment for opportunistic spectrum access[J].Mobile Networks and Applications,2006,11(4):555-576.
[10]朱冰莲,朱方方,段青言,等.采用多策略离散人工蜂群的改进频谱分配算法[J].西安交通大学学报,2016,50(2):20-25.
[11]Ileri O,Samardzija D,Sizer T,et al.Demand responsive pricing and competitive spectrum allocation via a spectrum server[C]//New Frontiers in Dynamic Spectrum Access Networks,Baltimore,2005:194-202.
[12]Gandhi S,Buragohain C,Cao L,et al.A general framework for wireless spectrum auctions[R].UCSB,2007.
[13]张士兵,张国栋,包志华.认知无线网络中基于代理的动态频谱交易算法[J].通信学报,2013,34(3):119-125.
[14]Cao L,Zheng H.Distributed spectrum allocation via local bargaining[C]//Sensor and Ad Hoc Communications and Networks,Santa Clara,2005:475-486.
[15]Etkin R,Parekh A,Tse D.Spectrum sharing for unlicensed bands[C]//New Frontiers in Dynamic Spectrum Access Networks,Baltimore,2005:517-528.
[16]Owen G.Game theory[M].[S.l.]:Academic Press,1995.
[17]孙杰,郭伟,唐伟.认知无线多跳网中保证信干噪比的频谱分配算法[J].通信学报,2011,32(11):111-117.
[18]邝祝芳,陈志刚.认知无线Mesh网络中一种有效的多目标优化频谱分配算法[J].中南大学学报(自然科学版),2013,44(6):171-178.
[19]李鑫滨,刘磊,石爱武,等.基于一种改进人工蜂群算法的认知无线电频谱分配[J].应用科学学报,2013,31(5):448-453.
[20]Hasan N U,Ejaz W,Ejaz N,et al.Network selection and channel allocation for spectrum sharing in 5G heterogeneous networks[J].IEEE Access,2016,4:980-992.
[21]Tragos E Z,Zeadally S,Fragkiadakis A G,et al.Spectrum assignment in cognitive radio networks:a comprehensive survey[J].IEEE Communications Surveys&Tutorials,2013,15(3):1108-1135.
[22]Tsiropoulos G I,Dobre O A,Ahmed M H,et al.Radio resource allocation techniques for efficient spectrum access in cognitive radio networks[J].IEEE Communications Surveys&Tutorials,2016,18(1):824-847.
[23]李廷鹏,钱彦岭,李岳.基于改进匈牙利算法的多技能人员调度方法[J].国防科技大学学报,2016,38(2):144-149.