多小区5G认知无线电网络能量有效资源优化

Energy-Efficient Resource Optimization for Cognitive Multi-cell Network in 5G

为提高5G通信系统中能量利用效率,本文提出一种资源配置算法来解决多小区5G认知无线电网络中资源配置问题。针对需要优化的载波分配变量和功率变量,该算法采用交替优化的方式分别对上述变量进行优化。对于载波分配,所提算法依据最大化信噪比原则来分配载波;对于功率分配,本文将其转变为另外一个等效问题,然后利用连续凸近似方法求解。由于传统正交频分复用调制(OFDM)具有严重的频谱泄露,其他几种具有较低频谱泄露特性的5G候选调制方式,例如滤波器组多载波调制(FBMC)、通用滤波多载波调制(UFMC)、广义频分复用调制(GFDM)等,也被分析比较。仿真结果表明本文所提算法相比干扰受限算法具有更高的能量效率,并且证明具有较低频谱泄露的调制方式能取得更高的能量效率。

In order to improve the energy efficiency of 5 G communication system,this paper proposed a novel resource allocation algorithm for the cognitive multi-cell network in 5 G. In the proposed algorithm,the alternate optimization framework is adopted to optimize the subcarrier assignment and power allocation,where the maximal signal-interference-plus-noise ratio criterion is applied to complete the subcarrier assignment and the successive convex approximation is utilized to solve the power allocation after transforming the problem into an equivalent one. Due to the significant spectral leakage of traditional orthogonal frequency division multiplexing( OFDM),some other modulation schemes as 5 G candidates are also analyzed,e.g.,filter-bank based multi-carrier( FBMC),universal filtered multi-carrier( UFMC),and generalized frequency division multiplexing( GFDM). The simulation results indicate that the proposed algorithm can achieve higher energy efficiency than the interference limited algorithm and the modulation scheme which has less spectral leakage can achieve higher energy efficiency.