基于改进离散布谷鸟搜索算法的毫米波大规模MIMO系统波束选择

在毫米波大规模MIMO系统中采用全数字编码需要大量的射频链路，从而导致能量损耗过高。针对这一问题提出一种基于离散布谷鸟搜索算法(Discrete cuckoo search, DCS)的波束选择方案，减少所需射频链路数而不会造成明显的性能损失。首先分析毫米波大规模MIMO系统的波束选择模型，引用DCS算法来求解模型；然后针对布谷鸟算法Levy飞行离散化结果中出现的非正常编码，采用启发式贪婪算法进行修复；将遗传算法中的复制引入DCS算法中，复制全局最优的鸟巢来替换其中被发现的鸟巢，加快算法收敛速度。仿真结果表明，所提基于改进DCS算法的波束选择方案相比几种已有的方案可以获得更优的和速率性能。

Beam Selection for Millimeter Wave Massive MIMO Systems via Improved Discrete Cuckoo Search Algorithm

The use of full digital coding in millimeter-wave massive MIMO systems requires a large number of radio frequency(RF) chains, resulting in excessive energy consumption. A beam selection scheme based on discrete cuckoo algorithm (DCS) is proposed to reduce the number of required RF chains without obvious performance loss. Firstly, the beam selection model of millimeter wave massive MIMO system is analyzed, and the DCS algorithm is used to solve the model. Owing to the the abnormal coding in the Levy flight discretization result,the heuristic greedy algorithm is proposed to repair it. To speed up the convergence of the algorithm, the replication in the genetic algorithm is introduced into the DCS algorithm, and the global optimal nest is copied to replace the discovered nests. The simulation result shows that the proposed beam selection scheme based on the improved DCS algorithm can obtain better rate performance than several existing schemes.