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Efficient Multi-Cell Clustering for Coordinated Multi-Point Transmission with Blossom Tree Algorithm

机译:开花树算法的多点协作高效多小区聚类

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Coordinated multi-point(CoMP) transmission clustering schemes could provide significant gains of system performance, such as throughput and cell- edge user data rates. Due to limitations of the backhaul communication and signal processing capability of base stations(BSs), the intrinsic problem of CoMP is that the selection of which BSs shall cooperate as only a few of BSs can be grouped in a cluster. However, approximating the theoretical performance bound of this clustering problem in CoMP at present is seldom discussed due to its inherent combinatorial complexity. In this paper, a novel efficient multi-cell clustering scheme based on blossom tree algorithm is proposed for cellular networks, incorporating CoMP with two cells in each cluster. With blossom tree algorithm, the proposed scheme can find out the optimal clustering strategy and help the CoMP transmission reach its theoretical performance bound on data rate in real-time computing(milliseconds in MATLAB simulation for one clustering). The simulation results show that our proposed method outperforms the existing dynamic greedy method in terms of cell edge users' average achievable data rate. Besides, it can also maintain high performance when extended to larger clusters in that with 4-cell clustering, the proposed method can reach 23.8% higher data rates than dynamic greedy method.
机译:协作多点(CoMP)传输群集方案可以显着提高系统性能,例如吞吐量和小区边缘用户数据速率。由于基站(BS)的回程通信和信号处理能力的限制,CoMP的内在问题是,哪些BS应该协作,因为只有几个BS可以分组在一个集群中。然而,由于其固有的组合复杂性,目前很少讨论在CoMP中此聚类问题的理论性能界限。本文针对蜂窝网络提出了一种基于开花树算法的新型高效多小区聚类方案,将CoMP与每个簇中的两个小区相结合。利用开花树算法,提出的方案可以找到最佳的聚类策略,并帮助CoMP传输在实时计算中达到其理论上的数据速率性能极限(在MATLAB仿真中以毫秒为单位进行一个聚类)。仿真结果表明,本文提出的方法在小区边缘用户平均可获得的数据速率方面优于现有的动态贪婪方法。此外,当扩展到较大的群集时,它还可以保持高性能,因为采用4单元群集,与动态贪婪方法相比,该方法可以达到23.8%的数据速率。

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