Concurrent Optimization of Coverage, Capacity, and Load Balance in HetNets Through Soft and Hard Cell Association Parameters
Concurrent Optimization of Coverage, Capacity, and Load Balance in HetNets Through Soft and Hard Cell Association Parameters
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DOI：
10.1109/tvt.2018.2846655
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发表时间：
2018-06
影响因子：
6.8
通讯作者：
Ahmad Asghar;H. Farooq;A. Imran
中科院分区：
文献类型：
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作者：
Ahmad Asghar;H. Farooq;A. Imran
超密集异构网络（HetNet）正在成为解决蜂窝网络容量危机的必然途径。然而，在小型和宏小区之间的负载不平衡以及HetNet中的资源利用率差仍然是一个长期存在的问题。本文通过提出一种解决方案来解决这个问题，该解决方案用于最大化覆盖范围和容量，同时最小化宏小区和小小区之间的负载不平衡。最近关于该主题的研究主要集中在覆盖范围，容量或负载的优化，或者这三个相互交织的目标中的两个的组合。我们制定的优化问题作为两个硬参数，即天线倾斜和发射功率的函数，和一个软参数，小区的个人偏移，直接影响覆盖范围，容量和负载。由此产生的解决方案是相互冲突的覆盖和容量优化（CCO）以及负载平衡（LB）自组织网络（SON）功能的组合。在所提出的联合CCO-LB解决方案中，CCO和LB的无冲突操作，确保通过设计一种新的负载感知的用户关联方法和解决阴影的覆盖概率使用随机逼近的影响。该问题被证明是非凸的，并使用遗传算法，序列二次规划和模式搜索算法来解决。提出的CCO-LB解决方案进行了比较，最近提出的两个CCO和CCO-LB的解决方案在文献中。结果表明，所提出的解决方案可以产生显着的增益在吞吐量，频谱效率和负载分布。
Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution.