认知小蜂窝网络中基于干扰温度限制的下行能效资源分配算法  

Energy-Efficient Downlink Resource Allocation Algorithm Under Temperature-Interference Constraints in Cognitive Small Cell Networks

作　　者：贾亚男[1] 岳殿武[1,2] 

机构地区：[1]大连海事大学信息科学技术学院,辽宁大连116026 [2]杭州电子科技大学浙江省数据存储传输及应用技术研究重点实验室,浙江杭州310000

出　　处：《电子学报》2017年第4期844-854,共11页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61371091;No.61301228);高等学校博士学科点专项科研基金(No.20132125110006);中央高校基本科研业务费专项基金-大连海事大学优秀科技创新团队培育计划(No.3132013334);浙江省数据存储传输及应用技术研究重点实验室(杭州电子科技大学)开放基金(No.201401)

摘　　要：为最大化认知小蜂窝基站的能量效率,本文基于博弈论模型分析了下行联合频谱资源块和功率分配行为.在干扰受限环境下,多个基站采用分布式结构共享空闲频谱资源.为避免累加干扰损害主用户的通信,算法中引入了功率和干扰温度限制.由于具有耦合限制的分数形式的能量效用函数是非凸最优的,通过将其转化为等价的减数形式进行迭代求解.给定频谱资源块分配策略后,主博弈模型可被重新建模为便于求解发射功率的等价子博弈模型,并通过代价的形势解除耦合限制.仿真结果表明,本文所提算法能够收敛到纳什均衡,并有效提高了系统资源利用率和能量效率.In order to maximize the energy efficiency（ EE） of a cognitive small cell base station, this paper analyzes downlink joint spectrum resource blocks（ SRBs） and power allocation based on game theory. In an interference-limited environment,base stations can share unused spectrum resources in a distributed architecture. The proposed algorithm introduces power and interference temperature constraints to avoid harmful accumulated interference on primary users. It is non-convex optimal to optimize the fractional form EE under multiple coupling constraints. The maximization problem is transmitted into an equivalent problem in subtractive form which can be solved from the iterative point of view. After obtaining the SRBs allocation strategies, the original game can be re-modeled as equivalent sub-games, then the transmission power can be solved more easily after decoupling power constraints based on pricing. Simulation results show that the proposed algorithm can converge to a Nash equilibrium and effectively improve system resources utilization and EE.

关 键 词：认知小蜂窝网络 能量效率 干扰温度 频谱分配 功率分配 

分 类 号：TN929.5[电子电信—通信与信息系统]