Load Aware Joint CoMP Clustering and Inter-Cell Resource Scheduling in Heterogeneous Ultra Dense Cellular Networks

For coordinated multipoint transmission in heterogeneous ultra dense networks (HUDN), clustering and multicell resource scheduling should be carried out jointly. Based on the <inline-formula><tex-math notation="LaTeX"> $\boldsymbol{\alpha }$</tex-math></inline-formula>-fairness function, the joint optimization problem is formulated that is NP-hard. A two-step joint clustering and scheduling (TS-JCS) scheme is proposed. Considering the extremely imbalanced cell loads in HUDN, the impact of cell loads on resource availability must be concerned, and a novel load aware clustering (LAC) is designed at the first step and solved by game theory. Based on the clustering results, a fair graph-coloring based inter-cell resource scheduling can be employed at the second step to maximize the resource utilization. Simulations are carried out to evaluate the performance of TS-JCS in a three-layer HUDN. It is shown that the utility of TS-JCS is close to the optimal value, demonstrating the high effectiveness of TS-JCS to solve the joint optimization problem. Compared with non-joint schemes, when fairness is considered in TS-JCS with <inline-formula> <tex-math notation="LaTeX">$\boldsymbol{\alpha }$</tex-math></inline-formula> = 1 and <inline-formula> <tex-math notation="LaTeX">$\boldsymbol{\alpha }$</tex-math></inline-formula> = 2, the average throughput can be improved by about 30% while keeping similar performance in cell-edge throughput. Finally, it is also shown that in HUDN with imbalanced load, the performance gain of TS-JCS with <inline-formula><tex-math notation="LaTeX"> $\boldsymbol{\alpha }$</tex-math></inline-formula> = 1 and <inline-formula><tex-math notation="LaTeX"> $\boldsymbol{\alpha }$</tex-math></inline-formula> = 2 over the baseline scheme is obvious compared to that in regular hexagonal networks with uniformly distributed loads, demonstrating the importance of the load aware scheme LAC for HUDN.

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