Analysis of SIR and Rate Meta Distributions for 3D Heterogenous Ultra-Dense Networks with Joint Offloading and Resource Partitioning

IEEE Access(2020)

引用 1|浏览20
暂无评分
摘要
In heterogeneous ultra-dense networks (HUDNs), load balancing is always considered by offloading users from the macro base station (MBS) tier to the small base station (SBS) tier, to alleviate the data traffic of the MBS tier, while it causes a serious problem that the offloaded users will suffer strong interference from the MBS tier, and hence the signal-to-interference ratio (SIR) is in low region for the offloaded users. In order to improve the performance of these offloaded users, an interference mitigation scheme based on resource partitioning is employed in the literature. Specifically, a fraction of resource blocks (RBs) is reserved for the offloaded users, and then the SIRs of these users will be enhanced because there is only interference from the SBS tier. In summary, the combination of offloading and resource partitioning is called joint offloading and resource partitioning scheme, which has been investigated in the HUDNs built on the 2D space, while it has rarely been deliberated in 3D HUDNs that are used to model the scenario of high-rise buildings. Moreover, a two-tier 3D HUDN is proposed in this paper, and the MBS and SBS tiers are modeled as two independent Poisson point processes. Then, the SIR and rate meta distributions, which are used to characterize the fraction of users that achieve a desired link reliability given SIR or rate thresholds, are formulated for the proposed 3D HUDN. Lastly, numerical results show that with the usage of the joint offloading and resource partitioning scheme, the performance of the proposed 3D HUND can be significantly improved.
更多
查看译文
关键词
Three-dimensional displays, Solid modeling, Interference, Shadow mapping, Base stations, Two dimensional displays, 3D, heterogenous ultra-dense network, offloading, Poisson point process, resource partitioning, SIR and rate meta distributions
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要