认知小蜂窝网络中基于能效的下行资源分配算法

在认知小蜂窝网络框架下,对基于OFDMA技术的下行联合频谱资源块和功率分配问题进行了研究。小蜂窝基站在分布式结构下采用开放式接入方式共享空闲频谱资源以最大化其能量效率,基站间的竞争关系使系统资源的动态分配过程可建模为非合作博弈模型。由于最大化具有多个限制条件的分数形势的能量效用函数属于非凸最优问题,可通过将其转化为等价的减数形势,并从串行和并行迭代的角度进行求解。在给定资源块分配策略后,原有博弈模型可被重新建模为便于独立求解发射功率的等价子博弈模型。仿真结果表明,所提算法在干扰受限的通信环境下能收敛到纳什均衡,并有效提高了系统资源利用率和能量效率。

Energy efficiency-based downlink resource allocation in cognitive small cell networks

Joint downlink spectrum resource block (SRB) and power allocation was studied based on OFDMA in cognitive small cell networks. In a distributed framework, small cell base stations (SCBS) could share free spectrum resources with open access to maximize their energy efficiency (EE) respectively. The competition among SCBS made the dynamic resources allocation problem be modeled as a non-cooperative game. It was non-convex optimal to optimize the EE in a fractional form under multiple interference constraints. The maximization problem was transmitted into an equivalent problem in subtractive form which could be solved from the sequential and parallel iteration points of view. After obtaining the SRB allocation strategies, the master game could be re-modeled as equivalent sub-games for solving the transmission power more easily. Simulation results show the proposed algorithm can converge to a Nash equilibrium and effectively improve system resources utilization and EE in interference-limited communication environments.