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

摘要

Inorder 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 aninterferencelimitedenvironment,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.%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.